Functional and structural neuroimaging of facial emotion ... · 80 Seiten, 139 Literaturangaben 1,...

Functional and structural neuroimaging of facial

emotion recognition in alexithymia

Dissertation

zur Erlangung des akademischen Grades

Dr. rer. med.

an der Medizinischen Fakultät

der Universität Leipzig

eingereicht von: Klas Ihme, M.Sc.

geboren am 14. Oktober 1983 in Braunschweig

angefertigt in der: Klinik für Psychosomatische Medizin und Psychotherapie

Universität Leipzig

Leitung: Prof. Dr. med. Anette Kersting

Betreuer: Prof. Dr. med. Anette Kersting

Beschluss über die Verleihung des Doktorgrades vom: 21.04.2015

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Bibliographische Beschreibung

Ihme, Klas

Functional and structural neuroimaging of facial emotion recognition in alexithymia

Universität Leipzig, Dissertation

80 Seiten, 139 Literaturangaben1, 2 Abbildungen2, 0 Tabellen3, 7 Anlagen.

Referat:

Research in the last decades has shown that individuals with high degrees in the personality trait of

alexithymia not only have difficulties in identifying and recognizing own feelings, but also show deficits in

reading emotions from facial expressions of other people. Therefore, the current dissertation investigates

the neural correlates of recognizing emotional facial expressions as a function of alexithymia. Initially, a

theoretical introduction is given and existing findings from behavioral as well as structural and functional

neuroimaging research are presented. Open questions are identified and addressed in one structural and

two functional magnetic resonance imaging studies that were compiled into three original research articles.

Study 1 examined the gray matter profile of high and low alexithymic individuals in selected brain regions

relevant for processing emotional faces. In Study 2, functional neuroimaging was used to investigate the

neural correlates of high alexithymic individuals' difficulties in labeling briefly presented (≤ 100 ms) facial

expressions of emotion. Study 3 investigated neural activations as a function of alexithymia during the

labeling of emotional facial expressions when these are presented with little temporal constraints (≥ 1 s).

The results of these studies are summarized and integrated with the existing literature. Finally, open issues

are discussed and ideas for further research are outlined.

1 Dazu kommen 68 zusätzliche Referenzen in den angefügten Originalartikeln

2 Dazu kommen 5 Abbildungen in den angefügten Originalartikeln

3 Dazu kommen 12 Tabellen in den angefügten Originalartikeln

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Table of content

List of Abbreviations ........................................................................................................................................... 3

List of Figures ...................................................................................................................................................... 5

List of Tables ....................................................................................................................................................... 6

1 Introduction .............................................................................................................................................. 7

1.1 Alexithymia .......................................................................................................................................... 7

1.2 Emotions and expressions of emotions .............................................................................................. 9

1.3 Recognizing facial expressions of emotion ......................................................................................... 9

1.4 Alexithymia and the recognition of emotional facial expressions .................................................... 12

1.4.1 Evidence from behavioral studies ............................................................................................. 12

1.4.2 Evidence from structural neuroimaging studies ....................................................................... 13

1.4.3 Evidence from functional neuroimaging studies ...................................................................... 14

1.5 Integration of empirical evidence and hypotheses ........................................................................... 15

2 Original research articles ........................................................................................................................ 17

2.1 Study 1: Alexithymia is related to differences in gray matter volume (Ihme et al., 2013) ................ 18

2.2 Study 2: Alexithymic features and the labeling of brief emotional facial expressions – an fMRI

study (Ihme et al., 2014a) ............................................................................................................................ 27

2.3 Study 3: Alexithymia and the labeling of facial emotions: response slowing and increased motor

and somatosensory processing (Ihme et al., 2014b) ................................................................................... 39

3 General discussion .................................................................................................................................. 50

3.1 Summary of the original research articles ........................................................................................ 50

3.2 Integration of findings ....................................................................................................................... 50

3.3 Open issues and ideas for further research ...................................................................................... 53

3.4 Conclusion ......................................................................................................................................... 55

4 Zusammenfassung der Arbeit ................................................................................................................ 56

5 References .............................................................................................................................................. 61

6 Appendix ................................................................................................................................................ 70

Lebenslauf ................................................................................................................................................... 71

Publikationsverzeichnis ............................................................................................................................... 72

Erklärung über die eigenständige Abfassung der Arbeit ............................................................................. 74

Acknowledgement ....................................................................................................................................... 75

Study 1: Specification of author contribution ............................................................................................. 76

Study 2: Supplementary Materials .............................................................................................................. 77

Study 3: Supplementary Materials .............................................................................................................. 79

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List of Abbreviations

General text:

ACC anterior cingulate cortex

AMG amygdala

BVAQ Bermond‐Vorst Alexithymia

Questionnaire

DDF difficulties describing feelings

DIF difficulties identifying feelings

EOT external oriented thinking

FFG fusiform gyrus

fMRI functional magnetic resonance

imaging

GM gray matter

HAI high alexithymic individual

HC healthy control

IFG inferior frontal gyrus

INS insula

LAI low alexithymic individual

MTG middle temporal gyrus

OFC orbito‐frontal cortex

PMC pre‐motor cortex

S1 primary somatosensory cortex

SC superior colliculi

SFG superior frontal gyrus

SMA supplementary motor area

sMRI structural magnetic resonance

imaging

STR striatum

STG superior temporal gyrus

TAS‐20 20‐item version of the Toronto

Alexithymia Scale

TAS‐26 26‐item version of the Toronto

Alexithymia Scale

Th thalamus

TSIA Toronto Structured Interview for

Alexithymia

VC visual cortex

vmPFC ventro‐medial pre‐frontal cortex

Additional abbreviations in Study 1:

AAL automated anatomic labeling

BDI Beck Depression Inventory

DARTEL Diffeomorphic Anatomical

Registration Through

Exponentiated Lie algebra

FDR false discovery rate

FHWM full width at half maximum

HA high alexithymic

IAPS international affective picture

system

k cluster extent

LA low alexithymic

LEAS Levels of Emotional Awareness

Scale

MNI Montreal Neurological Institute

MRI magnetic resonance imaging

ROI region of interest

SPM statististical parametric mapping

VBM voxel‐based morphometry

WM white matter

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AN angry

EMG Electromyography

EPI echo planar imaging

FE fearful

fwe family‐wise error

HA happy

NE neutral

PANAS Positive And Negative Affect

Schedule

RT reaction time

SD standard deviation

STAI State‐Trait Anxiety Inventory

SVC small volume corrected


ANOVA analysis of variance

BOLD blood oxygen level dependent

KDEF Karolinska Directed Emotional

Face database

PANAS‐N PANAS negative score

PANAS‐P PANAS positive score

SCID‐I Structured Clinical Interview for

DSM‐IV Axis I disorders

SEM standard error of mean

STAI‐T trait version of the STAI

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List of Figures

General text:

Figure 1. Neural correlates of facial emotion processing. ............................................................................... 10

Figure 2. Integration of functional and structural MRI studies on facial emotion processing in alexithymia.. 51

Study 1:

Study 1 ‐ Fig. 1. Sagittal(x), coronal(y) and axial(z) images of the significant clusters revealed in the contrast

LA>HA in neurological view. ............................................................................................................................ 21

Study 2:

Study 2 ‐ Fig. 1. Sequence of events within a trial. ......................................................................................... 30

Study 2 ‐ Fig. 2. Relationship between TAS‐20‐DDF and labeling performance. ............................................. 32

Study 2 ‐ Fig. 3. Selection of clusters of brain activation negatively correlating with alexithymic features in

the contrast angry > neutral presented in axial (A) and coronal (B) view. ...................................................... 34

Study 2 ‐ Fig. 4. Selection of clusters of brain activation negatively correlating with alexithymic features in

the contrast fearful > neutral presented in axial (A) and coronal (B) view. ..................................................... 34

Study 3:

Study 3 ‐ Figure 1. Activation in the right postcentral gyrus (A) and supplementary motor area (B) in

response to angry (vs. neutral) faces (AN > NE) positively correlating with TSIA‐DDF. ................................... 45

Study 3 ‐ Figure 2. Relationship (as calculated with Spearman’s rho) between measures of alexithymia and

brain activations in regions‐ofinterest that are relevant for facial emotion processing. ................................ 46

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List of Tables

Study 1:

Study 1 ‐ Table 1. Significant results for the comparison LA>HA with age and gender as covariate. ............. 22

Study 1 ‐ Table 2. Socio‐demographic description of the sample. .................................................................. 24

Study 2:

Study 2 ‐ Table 1. Means, standard deviations and ranges of the alexithymia measures (total scores, DDF

and DIF subscale scores) and affectivity measures. ........................................................................................ 31

Study 2 ‐ Table 2. Correlations between alexithymia scales and measures of affectivity. .............................. 31

Study 2 ‐ Table 3. Correlations between alexithymia measures and labeling performance (sensitivity indices)

for all facial expression conditions. ................................................................................................................. 32

Study 2 ‐ Table 4. Brain activation due to facial emotion: main effect analyses (clusters are significant with

fwe‐correction on cluster level): ...................................................................................................................... 32

Study 2 ‐ Table 5. Correlations between brain response to emotional faces and TAS‐20 total score (clusters

are significant with fwe‐correction on cluster level). ...................................................................................... 33

Study 2 ‐ Table 6. Correlations between brain response to emotional faces and TAS‐20‐DDF (clusters are

significant with fwe‐correction on cluster level). ............................................................................................ 33

Study 2 ‐ Table 7. Correlations between brain response to emotional faces and TSIA‐DDF (clusters are

significant with fwe‐correction on cluster level). ............................................................................................ 33

Study 3:

Study 3 ‐ Table 1. Correlations (Spearman’s Rho) between measures of alexithymia. ................................... 43

Study 3 ‐ Table 2. Correlations (Spearman’s rho) between difficulties describing feelings (as assessed by TAS‐

20‐DDF and TSIA‐DDF) and reaction times in the four facial expression conditions. ...................................... 44

Study 3 ‐ Table 3. Significant brain activations for all fMRI main contrasts. ................................................... 44

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1 INTRODUCTION

Be it at work, when talking to your partner or during a poker game – in our daily life, we constantly interact

with fellow human beings. The ability to read nonverbal cues of other people is a prerequisite for successful

social interaction. Facial expressions can provide important information about emotions, beliefs and

intentions and a reduced ability to interpret these manifold behavioral cues may cause interpersonal

problems (Erickson and Schulkin, 2003). Moreover, facial expressions are inherent parts of emotions and

are thought be integral to one’s emotional experience (Ekman and Friesen, 1974; Strack et al., 1988).

Interestingly, individuals with high degrees of alexithymia are vulnerable to interpersonal problems. High

alexithymic individuals (HAIs) are reported to have less social skills and little perceived social support

(Lumley et al., 1996; Vanheule et al., 2007), so that Spitzer et al. (2005) hypothesized that HAIs' problems in

social interaction stem from their inability to read emotional facial expressions. Although the construct of

alexithymia refers to the inability to discriminate one’s own emotions, research in the last two decades has

indeed shown that HAIs also have difficulties interpreting emotional facial expressions of others (for a

recent review, see Grynberg et al., 2012). Interestingly, overlapping neuronal circuits are involved in the

production and experience of one’s own emotions and the interpretation of emotions of other people

(shared substrates of emotion, see Heberlein and Atkinson, 2009). However, surprisingly, the neural

mechanisms underlying alexithymic individuals' problems in recognizing emotional facial expressions so far

are still rather unknown. Thus, the aim of this dissertation is to shed light on the structural and functional

neural correlates underlying these difficulties of HAIs using functional and structural magnetic resonance

imaging.

1.1 Alexithymia

In the 1970s, Sifneos described a phenomenon often present in psychosomatic patients that involves a

lacking ability to identify and describe one’s own emotions accompanied by an externally oriented style of

thinking and reduced imaginal life (Sifneos, 1973). Sifneos (1973) referred to this condition as alexithymia

which literally translates from Greek as “no words for emotions”. It is described that high alexithymic

individuals tend to react rather somatic (Krystal, 1988). Moreover, when asked to say how they feel, they

may report that they do not feel anything at all or describe their conditions in terms of actions or bodily

states (“I feel like kicking something”) instead of emotional words such as angry (Lane et al., 1990, 1997;

Primmer, 2013). Nowadays it is assumed that alexithymia is a dimensional construct relatively stable across

lifespan that can be seen as a personality trait normally distributed among the general population (Franz et

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al., 2008; Messina et al., 2014)4. It has been argued that alexithymic features are related to socialization

factors such as emotional neglect in early childhood (Aust et al., 2013), but there is also evidence for

genetic dispositions (e.g., Kano et al., 2012), so that contributions from more than one factor is likely. With

respect to the neurobiological mechanisms underlying alexithymia, several explanations exist. These include

a deficit in right hemispheric processing (e.g., Jessimer and Markham, 1997), a callosal transfer deficit (e.g.,

Hoppe and Bogen, 1977, but see Grabe et al., 2004) or a dysfunction of the anterior cingulate cortex (Lane,

2008; Lane et al., 1997; Wingbermühle et al., 2012). Recent research, however, proposes that alexithymia is

related to a rather global deficit in the transmission of emotional information (Primmer, 2013; van der Velde

et al., 2013). The clinical significance of alexithymia entails that it comes along with an increased

vulnerability for psychiatric diseases such as depression (Honkalampi et al., 2000, 2001), but also with

cardiovascular illnesses like hypertension (Grabe et al., 2010; Jula et al., 1999). Moreover, HAIs are

sometimes reported to have a worse prognosis for the outcome of psychotherapy (e.g., Ogrodniczuk et al.,

2011, but see Rufer et al., 2010).

Alexithymia can be directly assessed using self‐report or observer‐based methods (see Lichev et al., 2014;

Lumley et al., 2005). The most important self‐report measure is the 20‐item version of the Toronto

Alexithymia Scale (TAS‐20, Bagby et al., 1994), which consists of the three subscales difficulties identifying

feelings (DIF), difficulties describing feelings (DDF) and an externally‐oriented style of thinking (EOT). The

TAS‐20 has good psychometric properties (Parker et al., 2003), has been translated into many languages

(e.g. Bach et al., 1996; Joukamaa et al., 2001), and is useful in research and medical settings alike due to its

easy and fast application. However, self‐report measures may be biased with respect to the fact that

individuals who are unable to differentiate between their emotions should evaluate this very ability (Bagby

et al., 2006; Gündel et al., 2000; Suslow et al., 2001). Therefore, the developers of the TAS‐20 have

advanced the assessment of alexithymia to a structured observer‐based method, the Toronto Structured

Interview for Alexithymia (TSIA, Bagby et al., 2006). The TSIA covers the same subscales as the TAS‐20 (DIF,

DDF, and EOT), but additionally integrates the facet imaginal processes. It is administrated by a trained

interviewer and thus provides a more objective view on the participant's alexithymic tendencies. In

addition, it is thought that self‐report and observer‐rated methods tap slightly different aspects of the

alexithymia construct. Thus, many authors propose to employ a multi‐method approach for assessing

alexithymia (Lichev et al., 2014; Lumley et al., 2005).

4 In addition, the classifications secondary alexithymia and organic alexithymia exist. While the former is thought of

as an emotion regulation mechanism following a psychological trauma, the latter refers to alexithymic tendencies

following lesions or strokes to emotion‐relevant brain regions (Messina et al., 2014). Nonetheless these are not

further discussed here as this dissertation only investigates effects of the personality trait alexithymia.

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1.2 Emotions and expressions of emotions

Emotions are adaptive biological programs that guide human decisions and behavior in a wide range of

situations (Dolan, 2002). Emotions are elicited by certain internal or external events or stimuli and can be

divided into emotional state and feeling. The term emotional state includes behavioral and physiological

tendencies arising through the triggering event as well as the cognitive appraisal of the stimulus. In

contrast, the term feeling refers to the conscious experience of the emotional state comprising the

awareness of changes in bodily state, as well as the stimuli generating the emotion (Tsuchiya and Adolphs,

2007). The physiological changes elicited by an emotion include increases or decreases in cardiovascular

activity (e.g. faster heart beats), but also modifications in the configuration of the facial muscles, body

posture and prosody of the voice (e.g. Scherer and Ellgring, 2007). It has been argued that certain basic

emotions (e.g., anger, fear, happiness, disgust, sadness, surprise) exist which have particular eliciting events,

likely behavioral dispositions, patterns of physiological reactions and appraisal strategies (Ekman, 1992;

Izard, 1992). These emotions and especially the accompanying facial expressions are thought to be

universal, i.e., produced and recognized independent of cultural background (e.g. Ekman et al., 1969).

However, some authors counter that this universality of facial expressions is rather weak and bring forward

the argument that facial expressions are not always tied to specific emotions but also serve a

communicative role (Russell et al., 2003). Still, it is argued that recognition of facial expressions is very

valuable to infer emotions, beliefs and intentions of other people and thus one of the prerequisites for

successful social interaction (Erickson and Schulkin, 2003).

1.3 Recognizing facial expressions of emotion

Important insights into the neural processes underlying the recognition of emotions from faces come from

the seminal work of Ralph Adolphs who investigated facial emotion processing in patients with brain lesions

(especially amygdala) and healthy participants. He assumed that recognizing emotional facial expressions is

achieved through a mix of parallel and sequential cognitive processes recruiting a multitude of visual,

limbic, somatosensory, motor and frontal brain areas (see Figure 1). According to his model (Adolphs,

2002a), initially after being confronted with an emotional face, the visual information is transferred from

the superior colliculi and/or the thalamus to the amygdala, in which an initial evaluation of the significance

of the stimulus takes place (~ 120 ms after presentation) (see also de Gelder et al., 2011; Haxby et al., 2000;

Pessoa and Adolphs, 2010; Phillips et al., 2003). Meanwhile, the thalamus transmits the information to the

visual cortex, where a detailed visual analysis is started (see Figure 1).

Later (~170 ms post stimulus), information travels to areas specified in facial and motion processing in the

temporal cortex (i.e., fusiform gyrus [FFG] and superior temporal gyrus [STG], respectively, see Haxby et al.,

2000; Kanwisher and Yovel, 2006; Kanwisher et al., 1997). In parallel, the amygdala sends the results of the

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significance evaluation to the basal ganglia (especially striatum) and hypothalamus as well as to ventro‐

medial pre‐frontal cortex (vmPFC) and orbito‐frontal cortex (OFC), thus triggering deeper emotional

processing and attention shifts towards the perceived stimulus. This elicits actual or covert physiological

reactions via connections to the brain stem. Through reciprocal connections, subcortical structures

(amygdala and basal ganglia) and OFC transmit affective information back to visual and temporal areas

(Adolphs, 2002a, 2002b) for more profound analysis. This process explains that emotional compared to

neutral faces receive more detailed processing in visual cortices and fusiform gyrus (e.g. Dannlowski et al.,

2012; Pizzagalli et al., 2002; Vuilleumier and Pourtois, 2007).

Figure 1. Neural correlates of facial emotion processing. Simplified depiction of the brain areas involved in the processing of facial expressions of emotion. Initially rough and automatic processing takes place in amygdala and visual cortex. Thereafter, detailed visual processing of the facial features takes place in visual and temporal areas. Increased processing of affective information is accomplished in subcortical structures and frontal cortex triggering also physiological reactions. Finally, a detailed holistic representation of the facial expression is gained through concerted activation in limbic, frontal, sensorimotor and somatosensory areas as well as the mirror neuron system. Model is adopted from Adolphs (2002a, 2002b), but integrates, among others, also work by Haxby et al. (2000), Palermo and Rhodes (2007), van der Gaag et al. (2007) as well as Vuilleumier and Pourtois (2007). Picture of the facial expression has been taken from the Radboud face database (Langner et al., 2010). Abbreviations not mentioned in the text so far: AMG = amygdala; Ins = Insula; SC = superior colliculi; STR = striatum; Th = Thalamus; VC = visual cortex.

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At a later stage (~300 ms post stimulus presentation and later) all aspects of the facial expression are

processed and related to each other. In particular, frontal regions link the perceived face to existing

representations of emotions. In addition, processing of the physiological responses takes place in

somatosensory cortex and the insula (e.g., Adolphs et al., 2000; Sel et al., 2014). Enhanced processing and

cross‐talk between OFC, vmPFC, parts of the anterior cingulate cortex (ACC) and the anterior insula may

ultimately create conceptual knowledge about the seen face and induce the same feeling as seen in the

facial expression (re‐feeling) (see Heberlein and Adolphs, 2007). This supports a precise recognition and

labeling of the emotional expression (Adolphs, 2002b; Palermo and Rhodes, 2007; Phillips et al., 2003;

Tsuchiya and Adolphs, 2007; see Figure 1). Hence, an integral of part of the recognition of the emotional

face is overtly or covertly simulating the seen emotional expression to create an internal representation of

the seen facial expression. This is also supported by the fact that partly overlapping brain areas are involved

in the expression of one’s own emotions and recognizing these in other people (shared substrates of

emotion, cf. Heberlein and Atkinson, 2009). Interestingly, showing facial expressions to others also elicits

similar muscle reactions in the observer (e.g., Dimberg et al., 2000), which may in turn facilitate internal

simulation of the seen emotion. This idea fits nicely to research on the mirror neuron system. Mirror

neurons are defined as having the properties to fire both, when accomplishing a goal‐directed movement

and when one sees other people conducting the very same movement. This potentially allows imitation and

learning actions from others (see Rizzolatti and Craighero, 2004; Rizzolatti, 2005, for reviews). Over and

above their function in motor actions, research in the last decade has proposed that structures with mirror‐

like properties also play an important role in creating internal representations of facial expressions thus

alleviating their recognition (Carr et al., 2003; Iacoboni, 2009). Regions with mirror‐like properties in facial

emotion recognition include pre‐motor cortex (PMC) inferior frontal gyrus (IFG), STG, insula, middle

temporal gyrus (MTG), inferior parietal lobule as well as superior frontal gyrus (SFG) and supplementary

motor area (SMA) (Iriki, 2006; Likowski et al., 2012; van der Gaag et al., 2007). Most importantly, IFG is

thought to be a key structure in creating fast simulations of others' facial expressions in interplay with other

areas such as the anterior insula (van der Gaag et al., 2007) (see Figure 1). From a functional point of view,

this proposes recognition from analogy: When I feel a certain emotion, this leads to a particular

configuration of facial muscles. If I see somebody else displaying this configuration of muscles, then he must

feel the same emotion (Iacoboni, 2009). The crucial point is that creating an internal representation from

simulation speeds up this process allowing recognition with little effort (Iacoboni, 2009). A simulation of the

seen facial expressions is necessary to create a holistic internal representation, which ultimately allows

recognizing the expressed emotion. In this representation affective, somatosensory, motor and visual

aspects are integrated through the concerted activation of limbic, frontal, somatosensory, temporal and

occipital structures.

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Patient and lesion studies suggest that a loss of or less elaborated processing in one of the implicated

regions gives rise to a fragmented internal representation and thus to deficient recognition performance.

For example, it has been shown that lesions to or diseases affecting amygdala, ventral striatum in the basal

ganglia and pre‐frontal cortex lead to a significant decline in recognizing emotional facial expression

(Adolphs et al., 2005; Calder et al., 2004, 2010; Heberlein et al., 2008; Schäfer et al., 2007; Trinkler et al.,

2013; Tsuchida and Fellows, 2012). As the system has many interconnections and dependencies, it hence

seems that normal functioning in all regions is necessary for adequate performance (see also Delle‐Vigne et

al., 2014). Significant changes in brain structure and function in the respective regions thus eventually lead

to reductions in recognition performance of facial expressions of emotion.

1.4 Alexithymia and the recognition of emotional facial expressions

As mentioned above, it has been argued that the problems of HAIs in interpersonal problems may stem

from their reduced ability to express and recognize facial expressions (Spitzer et al., 2005). Indeed

alexithymic tendencies are related to a decreased non‐verbal expressiveness and less automatic mimicry of

emotional facial expressions (Sonnby‐Borgström, 2009; Troisi et al., 1996). With respect to the recognition

of emotional facial expressions in alexithymia, many studies have been accomplished since the early 1990s.

In the following subsections, I will review behavioral as well as functional and structural neuroimaging

studies that are related to facial emotion labeling in alexithymia. Due to presumably complex interactions

between (psychiatric) diseases and alexithymia only research studying healthy populations or reporting

sufficient information about healthy controls (HCs) are reviewed. All studies presented employed either the

TAS‐20, the 26‐item version of the Toronto Alexithymia Scale (TAS‐26, the precursor of the TAS‐20, Taylor et

al., 1985) or the Bermond‐Vorst Alexithymia Questionnaire (BVAQ, Vorst and Bermond, 2001) as measure of

alexithymia. To the best of my knowledge no study exists so far that employs the TSIA and satisfies to the

other abovementioned criteria.

1.4.1 Evidence from behavioral studies

In the last two decades, many behavioral studies on the relationship between alexithymic features and the

ability to recognize facial expressions have been accomplished (for a recent review, see Grynberg et al.,

2012). In particular, there is strong evidence that HAIs have difficulties in recognizing facial expressions,

especially when these are presented with temporal (Parker et al., 2005; Prkachin et al., 2009; Swart et al.,

2009) and/or other perceptual constraints (Cook et al., 2013; Domes et al., 2011; Kätsyri et al., 2008). When

the faces are presented for a longer duration, generally no relationship between alexithymic features and

facial emotion recognition is found (Berenbaum and Prince, 1994; Mann et al., 1995; Mériau et al., 2006;

Montebarocci et al., 2011; Pandey and Mandal, 1997; Parker et al., 2005). Still, it has to be mentioned that

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studying the association with large sample sizes (Lane et al., 1996, 2000) and/or including participants with

extreme alexithymia scores (Jessimer and Markham, 1997; Mann et al., 1994; Parker et al., 1993) may even

uncover a relationship between alexithymia and facial emotion recognition at long stimulus presentation

times. Based on these findings, Grynberg et al. (2012) concluded that HAIs have problems labeling facial

expressions as they cannot process the perceptual information in a very short time. The border at which

difficulties in labeling occur for HAIs has been assumed to be roughly between 300 ms (Grynberg et al.,

2012) and 1 s (Parker et al., 2005).

1.4.2 Evidence from structural neuroimaging studies

One reason for the difficulties of HAIs in reading emotional facial expressions may be due to morphological

differences in brain areas of the network implicated in facial emotion recognition (see section 1.3 ). These

alterations may reduce elaborated processing in or information transfer between regions of this network,

which in turn can lead to severe problems when recognizing emotions. For example, a recent study has

shown that decreased gray matter (GM) in amygdala, temporal and frontal cortex predicted worse

performance in a facial emotion labeling task (Dean et al., 2013). This suggests that the GM profile of brain

areas associated with the facial emotion recognition network relates to recognition performance. Increases

in GM volume reflect an increase in the number of synapses, and not necessarily cell bodies, in the

respective brain regions (Anderson, 2011; Lövdén et al., 2013). Through this, the generation of action

potentials in the post‐synaptic cell may be facilitated. This interpretation is in keeping with studies showing

that more GM volume comes along with better capabilities (e.g., Draganski et al., 2004; Garrido et al., 2009;

Wenzel et al., 2014). Thus less GM volume in certain brain regions may be related to a decreased neural

information transfer within the respective structure and in interplay with other areas. If this occurs in one or

more of the brain regions implicated in facial emotion recognition, performance probably declines.

Up to now, five studies have investigated the morphological profile as a function of alexithymia5 in healthy

populations (Borsci et al., 2009; Gündel et al., 2004; Heinzel et al., 2012) or reporting data of HCs (Kubota et

al., 2011; Zhang et al., 2011). Borsci et al. (2009) found a negative association between alexithymia and GM

5 There are two further studies on GM volume as a function of alexithymia, however neither of them allows an

unbiased interpretation because they were not focused on alexithymia, but on the participants' age (Paradiso et al.,

2008) or the interaction between borderline personality and alexithymia (Bøen et al., 2014). Moreover, it has to be

noted that, by the time of submission of this dissertation, more studies have been published investigating the

relationship between alexithymia and brain morphology (Goerlich‐Dobre et al., 2014; Laricchiuta et al., 2014; van

der Velde et al., 2014). However, as they have been published after the structural neuroimaging study for this

dissertation (Study 1, see section 2.1 , Ihme et al., 2013), they are not presented in the introduction, but their

results are integrated in the discussion (see section 3 ).

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volume in the ACC, MTG, STG, anterior insula and OFC. In a study comparing a schizophrenic sample with a

healthy one, Kubota et al. (2011) revealed that the degree of alexithymia (TAS‐20) in the HCs correlated

negatively with the GM volume in the bilateral ventral striatum. These findings suggest an association

between alexithymia and reductions in GM volume in brain areas attributed to the classical network for

recognizing facial expressions (ventral striatum, OFC, anterior insula and ACC) (Adolphs, 2002a; Phillips et

al., 2003) and temporal regions (MTG and STG). Decreases in synaptic connectivity in these regions may

imply that crosstalk to, from and within the network can only take place to a lesser degree or at a slower

pace. In contrast, two other studies did not find any association between alexithymia and GM volume

(Heinzel et al., 2012; Zhang et al., 2011). However, Heinzel et al. (2012) employed a correction for multiple

testing (false discovery rate), which is very strict on smoothed data (see Nichols and Hayasaka, 2003) and

Zhang et al. (2011) studied a sample with TAS‐20 scores below the cut‐off for medium or high alexithymia

(cf. Taylor et al., 1997). Therefore, these studies may have some peculiarities in the methods, which could

explain that no effects with respect to alexithymia were uncovered. On the contrary, the study by Gündel et

al. (2004) investigated ACC size using a manual tracing procedure and revealed an increase in ACC size with

increasing alexithymia. Nonetheless, manual procedures have been suspected to be susceptible to human

errors introduced by misinterpretations of tissue boundaries (e.g., Dickstein et al., 2005; Eggert et al., 2012,

but see Lövdén et al., 2013). Thus, the role of ACC in alexithymia and facial emotion recognition is still

unclear. Over and above this, another issue is striking: none of the studies revealed (or explicitly tested) any

morphological differences in the amygdala and the FFG – two of the core structures of the model on facial

emotion processing (Adolphs, 2002a, see also Figure 1). To sum up, structural magnetic resonance imaging

(sMRI) studies so far point towards reduced GM volume in brain structures implicated in facial emotion

processing (ventral striatum, insula, MTG, STG, OFC). Still, up to now, the role of GM volume changes in

several regions, especially amygdala and FFG (not studied so far) and ACC (inconsistent results), as a

function of alexithymia needs to be investigated in more detail.

1.4.3 Evidence from functional neuroimaging studies

Neuronal activity can be indirectly measured using magnetic resonance imaging due to its link with the

blood oxygen level and the different magnetic properties of oxygenated and de‐oxygenated blood. This

method has been termed functional magnetic resonance imaging (fMRI) and allows measuring neuronal

activity with a relatively high resolution (~5 mm) (e.g, Logothetis et al., 2001; Ogawa et al., 1992). Thus,

fMRI provides researchers with the ability to measure brain activity in cortical and subcortical structures

during tasks, so that it is an ideal tool to study the neural basis of facial emotion recognition.

Although the basic brain networks supporting facial emotion recognition are relatively well known, so far

little research has been accomplished examining this process in relation to the degree of alexithymic

15

features. Some studies made use of the affective priming paradigm, which evaluates early automatic

processing of facial expressions (Murphy and Zajonc, 1993; Suslow et al., 2013). Interestingly, employing

this paradigm, it has been shown that alexithymic individuals display less activity in amygdala, FFG, insula

and STG (Duan et al., 2010; Eichmann et al., 2008; Kugel et al., 2008; Reker et al., 2010). This suggests that

there is a deficit in information processing already at the early steps of the facial emotion recognition model

(see Figure 1), which is reflected in less amygdala activation. Thus, at later stages less information about the

stimulus' significance may be available giving rise to less elaborated processing. However, to the best of my

knowledge, only two studies exist that explicitly investigated the recognition or labeling of facial emotions

as a function of alexithymia. In the study by Mériau et al. (2006) participants had to decide whether

presented pictures of facial affect were fearful or angry. The study could neither reveal differences in

accuracy nor in brain activation with respect to the degree of alexithymia. Yet, the faces were presented for

a rather long time (3.75 s), so that the task may have been too easy (cf. Grynberg et al., 2012). Moreover,

Mériau et al. (2006) employed a correlational approach with only 23 participants (e.g., Yarkoni and Braver,

2010, recommend at least 40) who had very low levels of alexithymia. These factors may limit the

generalizability of the results. Another study by Lee et al. (2011) investigated the recognition of facial

expressions as a function of alexithymia in the fMRI scanner. The authors revealed differences in brain

activation in the sense that high alexithymic individuals show a decreased activation in the right striatum

when confronted with angry facial expressions. Lee et al. (2011) interpreted this reduction in striatal activity

as reduced processing already at the level of production of affective states. This in turn may impede the

interpretation of others' facial expressions as the striatum is involved in both producing and recognizing

facial emotions (Trinkler et al., 2013). However, as no behavioral responses were collected in the study, it is

not entirely clear what the participants actually did during the experiment. Thus, the two studies on facial

emotion recognition and alexithymia delivered interesting results, but, due to methodological peculiarities,

these should be interpreted with caution. In particular, it is still unknown which brain regions are implicated

when HAIs show decreases in performance, i.e., when expressions are presented with perceptual

constraints. Moreover, it is still unclear how HAIs can reach a similar accuracy as low alexithymic individuals

(LAIs) when the faces are presented for a longer time, so that sufficient information is present.

1.5 Integration of empirical evidence and hypotheses

In summary, behavioral evidence suggests that high alexithymic individuals have problems labeling

emotional faces, especially when these are presented very briefly or with other perceptual constraints.

Nonetheless, with little temporal constraints and enough processing time recognition is possible even for

HAIs. Up to now, the neural underpinnings of these deficits are relatively unexplored. On the one hand,

research has shown that HAIs display less GM volume in brain areas important for facial emotion

16

recognition and that activity in the amygdala is reduced. On the other hand, the morphological profile of

HAIs in core structures such as amygdala, ACC and FFG as well as the functional activation of the emotional

face network especially in later stages of processing is largely unknown. This latter point may be specifically

relevant for explaining why HAIs have difficulties in labeling under temporal constraints, but are able to

recognize the facial emotion when having enough time for detailed processing. Thus, one structural and

two functional MRI studies have been conducted in order to further examine the underlying neural

correlates of HAIs' difficulties in recognizing facial expressions of emotion. In particular, Study 1 investigated

the morphological profiles of HAIs versus LAIs in several brain areas relevant to facial emotion processing in

a group design. In this first study, the following hypothesis was tested:

H1. High compared to low alexithymic individuals show reduced gray matter volume in brain areas

crucially involved in the processing and labeling of emotional facial expressions, especially

amygdala, FFG, ACC, and anterior insula.

Moreover, Study 1 included an exploratory whole brain analysis in order to reveal additional regions that

show differential gray matter volume between high and low alexithymic individuals.

Study 2 investigated the link between alexithymic features and behavior as well as neural activation during

the labeling of briefly presented emotional faces in a correlational design with 50 individuals tested for their

degree of alexithymia using TAS‐20 and TSIA. Here, the following hypotheses were tested:

H2.1 Individuals with high levels of alexithymic features show decreased performance when labeling

briefly presented facial expressions.

H2.2 When labeling briefly presented emotional facial expressions, the participants' degree of alexithymic

features negatively correlates with activation in brain regions previously linked to facial emotion

recognition such as amygdala, striatum, anterior insula, ACC as well as temporal (STG, MTG) and

frontal structures (OFC, IFG).

Finally, Study 3 analyzed brain activation and behavior in 48 volunteers during labeling of emotional facial

expressions presented with little temporal constraints. The following hypothesis was tested:

H3. When labeling emotional facial expressions presented with little temporal constraints, difficulties of

HAIs are related to increased response times instead of decreased accuracy.

Still, it was also aimed to examine whether activation in certain neural structures indicates how alexithymic

individuals are capable of labeling emotional facial expressions when temporal demands are low. Thus,

Study 3 additionally includes an exploratory fMRI analysis.

17

2 ORIGINAL RESEARCH ARTICLES

The dissertation is based on three original research articles, which are included as published in the

remainder of this chapter. The references for the articles are as follows:

Study 1:

Ihme K*, Dannlowski U*, Lichev V, Stuhrmann A, Grotegerd D, Rosenberg N, Kugel H, Heindel W, Arolt V, Kersting A, and Suslow T. Alexithymia is related to differences in gray matter volume: a voxel‐based morphometry study. Brain Research, 1491: 60–7, 2013. (*equal contribution)

Study 2:

Ihme K, Sacher J, Lichev V, Rosenberg N, Kugel H, Rufer M, Grabe HJ, Pampel A, Lepsien J, Kersting A, Villringer A, Lane R, and Suslow T. Alexithymic features and the labeling of brief emotional facial expressions – an fMRI study. Neuropsychologia, 64: 289‐299, 2014.

Study 3:

Ihme K, Sacher J, Lichev V, Rosenberg N, Kugel H, Rufer M, Grabe HJ, Pampel A, Lepsien J, Kersting A, Villringer A, and Suslow T. Alexithymia and the labeling of facial emotions: response slowing and increased motor and somatosensory processing. BMC Neuroscience, 15 (1): 40, 2014.

Please note that Study 3 has been published before Study 2, so that Study 2 already cites and discusses

Study 3.

18

2.1 Study 1: Alexithymia is related to differences in gray matter volume (Ihme et al.,

2013)

Study 1 was designed and conducted in cooperation with Udo Dannlowski and his working groups at the

University of Marburg and the University of Münster. As Udo Dannlowski and I equally contributed to the

published article, a specification of the contribution of all authors can be found in the Appendix on page 76.

Available online at www.sciencedirect.com

www.elsevier.com/locate/brainres

b r a i n r e s e a r c h 1 4 9 1 ( 2 0 1 3 ) 6 0 – 6 7

0006-8993/$ - see frohttp://dx.doi.org/10

nCorrespondence toLeipzig, Germany. F

E-mail address:1 These authors

Research Report

Alexithymia is related to differences in gray mattervolume: A voxel-based morphometry study

Klas Ihmea,n,1, Udo Dannlowskib,c,1, Vladimir Licheva, Anja Stuhrmannb,Dominik Grotegerdb, Nicole Rosenberga, Harald Kugeld, Walter Heindeld,Volker Aroltb, Anette Kerstinga, Thomas Suslowa

aDepartment of Psychosomatic Medicine and Psychotherapy, University of Leipzig, GermanybDepartment of Psychiatry and Psychotherapy, University of Munster, GermanycDepartment of Psychiatry, University of Marburg, GermanydDepartment of Clinical Radiology, University of Munster, Germany

a r t i c l e i n f o

Article history:

Accepted 24 October 2012

Objective: Alexithymia has been characterized as the inability to identify and describe feelings.

Functional imaging studies have revealed that alexithymia is linked to reactivity changes in

Available online 1 November 2012

Keywords:

Alexithymia

Amygdala

Anterior cingulate cortex

Anterior insula

Fusiform gyrus

Middle temporal gyrus

Voxel-based morphometry

nt matter & 2012 Elsevie.1016/j.brainres.2012.10.0

: Department of Psychosax: þ49 341 [email protected] equally to th

a b s t r a c t

emotion- and face-processing-relevant brain areas. In this respect, anterior cingulate cortex

(ACC), amygdala, anterior insula and fusiform gyrus (FFG) have been consistently reported.

However, it remains to be clarified whether alexithymia is also associated with structural

differences. Methods: Voxel-based morphometry on T1-weighted magnetic resonance images

was used to investigate gray matter volume in 17 high alexithymics (HA) and 17 gender-

matched low alexithymics (LA), which were selected from a sample of 161 healthy volunteers

on basis of the 20-item Toronto Alexithymia Scale. Data were analyzed as statistic parametric

maps for the comparisons LA4HA and HA4LA in a priori determined regions of interests

(ROIs), i.e., ACC, amygdala, anterior insula and FFG. Moreover, an exploratory whole brain

analysis was accomplished. Results: For the contrast LA4HA, significant clusters were detected

in the ACC, left amygdala and left anterior insula. Additionally, the whole brain analysis

revealed volume differences in the left middle temporal gyrus. No significant differences were

found for the comparison HA4LA. Conclusion: Our findings suggest that high compared to low

alexithymics show less gray matter volume in several emotion-relevant brain areas. These

structural differences might contribute to the functional alterations found in previous imaging

studies in alexithymia.

& 2012 Elsevier B.V. All rights reserved.

r B.V. All rights reserved.44

omatic Medicine and Psychotherapy, University of Leipzig, Semmelweisstrasse 10 04103

ig.de (K. Ihme).e work and should be both considered first author.

1. Introduction

The construct of alexithymia, which literally means ‘‘no

words for emotion’’, has been characterized as the inability

to identify and describe feelings. Moreover, alexithymics

show an externally oriented cognitive style and mundane

fantasies (Sifneos, 1973). In order to assess alexithymia,

most studies use a convenient self-report instrument, the

dx.doi.org/10.1016/j.brainres.2012.10.044



mailto:[email protected]


b r a i n r e s e a r c h 1 4 9 1 ( 2 0 1 3 ) 6 0 – 6 7 61

20-item Toronto Alexithymia Scale (TAS-20, Bagby et al.,

1994).

Despite extensive research on the neural basis of emotion

processing deficits in alexithymia, no distinct neural circuit

causing these deficits could be identified. Some researchers

argue in favor of a reduced automatic processing of emo-

tional information in alexithymia. This is reflected in dimin-

ished activity in structures involved in these automatic

processes such as amygdala, insula or sensory areas (Duan

et al., 2010; Eichmann et al., 2008; Kugel et al., 2008; Pollatos

and Gramann, 2011; Reker et al., 2010). For example, the study

of Kugel et al. (2008) reports a negative correlation between

individual TAS-20 score and amygdala reactivity to masked

emotional faces. This finding was confirmed by Reker et al.

(2010) using the same paradigm. Moreover, the work of Reker

et al. (2010) revealed reduced automatic insula activation and

reactivity of the fusiform gyrus (FFG) as a function of

alexithymia. FFG is seen as a sensory area most relevant in

face processing (Kanwisher et al., 1997; McCarthy, et al., 1997)

whose early activity can be modulated by emotions

(Pizzagalli et al., 2002; see also Adolphs, 2002). Smaller FFG

reactivity in high alexithymia has been frequently reported

(Duan et al., 2010; Eichmann et al., 2008) and might play a role

in alexithymics’ deficits in reading emotions from faces

(Parker et al., 2005). Therefore, this diminished early activity

in amygdala, insula and fusiform gyrus may lead to reduced

development of emotions in alexithymics.

However, other theories suspect that alexithymics automati-

cally generate emotional reactions, but rather direct too little

attention to these. This causes a diminished conscious experi-

ence of emotions (Lane et al., 1997; Lane, 2008). Therefore, Lane

(2008) has argued that altered functioning of the dorsal ACC

would be crucial for the development of alexithymic features.

Still, the role of the ACC is not clear, yet: whereas a recent study

(Heinzel et al., 2010) reports an increase in ACC activation to

different emotional stimuli in a group of high alexithymics

compared to low ones, other studies (Kano et al., 2003; Lane

et al., 1998; McRae et al., 2008) report diminished activity in the

ACC with increasing alexithymia.

As the insula is thought to play an eminent role in the

development of conscious feelings and empathy (Singer et al.,

2009), alexithymia might also be related to its altered function-

ing. Recently, insula activity was found to be reduced in an

empathy-for-pain experiment in high alexithymics compared

to low ones (Bird et al., 2010). Similarly, Silani et al. (2008)

reported that reduced anterior insula activity is associated with

less emotional awareness in interoception. From these findings,

it can be concluded that alexithymic traits might be linked to

difficulties to engage (anterior) insula when focusing on emo-

tions and a failure to simulate forward representations of bodily

states within the insula (Silani et al., 2008; Singer et al., 2009).

In summary, it seems that altered processing of emotional

stimuli as found in alexithymia is primarily accompanied by

reduced brain reactivity in ACC, amygdala, anterior insula as

well as in the fusiform gyrus. From this, the question arises if

there are differences in gray matter volume that might

promote the functional and behavioral differences.

This question was first examined by Gundel et al. (2004) using

a region-of-interest-(ROI)-based approach with manual tracing

of sagittal magnetic resonance images (MRI). Their study

revealed that gray matter volume in the ACC is larger in high

alexithymics than in low ones. However, the administered

manual procedure is susceptible to variance induced by human

perception of tissue boundaries. With the emergence of voxel-

based morphometry (VBM), an automated approach to investi-

gate brain morphology based on T1-weighted images was

developed. In this process, gray and white matter are auto-

matically segmented, so that their volume can be compared

voxel-wise in different brains (Ashburner and Friston, 2000). In

contradiction to Gundel et al. (2004), Borsci et al. (2009) used

VBM and found that high female alexithymics compared to low

ones show smaller ACC gray matter volume. Yet, these results

could not be confirmed by a recent VBM study examining gray

and white matter volume in a whole brain approach and in the

ACC depending on alexithymia in healthy young men (Heinzel

et al., 2011): the authors could not reject the null hypothesis of

no differences between high and low alexithymics.

Taken together, studies on structural differences in alexithy-

mia so far yielded inconsistent findings. This could result from

the different volumetric procedures (manual vs. automatic) but

also from different thresholds for statistical significance. While

Borsci et al. (2009) used a very liberal uncorrected threshold

revealing a difference, Heinzel et al. (2011) administered a

conservative false-discovery-rate-(FDR)-correction, which tends

to reduce the significance threshold in smoothed data (Nichols

and Hayasaka, 2003). The current study aims at examining size

of brain structures of high compared to low alexithymics using

the method of clustering (Forman et al., 1995) to correct for

multiple testing. This approach uses Monte Carlo simulation to

generate a null distribution of voxel activations based on the

noise for a particular model in the current search volume. From

this distribution, the probability of occurrence of a certain

cluster size (i.e., the number of contiguous voxels k) in a data

set solely consisting of noise can be determined at a particular

preset t-threshold (e.g. t¼3.39 or p¼0.001). From that, one can

empirically identify which cluster extent k can be seen as

significantly different from noise, e.g. its occurrence by chance

is less probable than 0.05. With this procedure, the assumption

is taken into account that, in comparison to noise, structural

properties like gray matter volume in specific brain areas or

interesting areas of neural activity tend to extend more than

individual voxels (Forman et al., 1995).

As functional neuroimaging studies have revealed differences

in reactivity of several brain regions in high alexithymics

compared to low, we conducted a ROI-based analysis examin-

ing gray matter volume in the ACC, amygdala, anterior insula

and fusiform gyrus. Moreover, an exploratory whole brain

analysis was calculated. In our model, we included age and

gender as covariate of nuisance to control for age- and gender-

related influences on gray matter volume.

2. Results

2.1. ROI-based analysis

For the contrast low alexithymia versus high alexithymia

(LA4HA) a significant cluster in the ACC (bilateral, cluster

size: 3565 voxels, [x¼2, y¼44, z¼�3]) was revealed. The peak

of this cluster was in the area of the subgenual ACC

Fig. 1 – Sagittal (x), coronal (y) and axial (z) images of the significant clusters revealed in the contrast LA4HA in neurological

view. Supra-threshold t-values (color-coded) in the respective masks are overlaid on a single T1 image template of SPM8. The

voxel-wise activation threshold is set to p¼0.05 for A–C and to p¼0.001 for D. Slice coordinates are given in the upper left

corner of each individual view. (A). Significant cluster revealed in the mask for the anterior cingulate cortex at a cluster

threshold of k¼1191. (B). Significant cluster revealed in the mask for the amygdala at k¼48. (C). Significant cluster revealed in

the mask for the anterior insula at k¼182 and (D). Significant cluster revealed in the exploratory whole brain approach at

k¼568. The cluster is located in the middle temporal gyrus.

b r a i n r e s e a r c h 1 4 9 1 ( 2 0 1 3 ) 6 0 – 6 762

extending to pregenual and dorsal regions (see Fig. 1A).

Additionally, a cluster in the left amygdala (57 voxels,

[x¼�30, y¼6, z¼�18]) and a cluster in the left anterior insula

(360 voxels, [x¼�35, y¼18, z¼6]) were detected (see Fig. 1B

and C). No significant clusters were revealed in the fusiform

gyrus. The contrast HA4LA did not expose significant differ-

ences in gray matter volume. For an overview of the results,

see Table 1.

2.2. Exploratory whole brain analysis

One cluster in the left middle temporal gyrus extending to the

occipital gyrus (1200 voxels, [x¼�48, y¼�75, z¼12]) exceeded

the significance threshold (see Table 1 and Fig. 1D) in the

contrast LA4HA. The analysis for the contrast HA4LA revealed

no significant cluster.

3. Discussion

The aim of the present study was to test whether there are

differences in gray matter volume between high and low

alexithymics in the brain. Several regions of interest deter-

mined a priori from recent neuroimaging studies, i.e., the

ACC, amygdala, anterior insula and fusiform gyrus were

investigated. In addition, an exploratory whole brain analysis

was conducted.

3.1. LA4HA: ROI-based analysis

The contrast LA4HA revealed significantly lesser gray matter

volume in high alexithymics compared to low in the ACC, the

amygdala and anterior insula. Considering the ACC, this is an

interesting finding as earlier studies examining ACC gray

matter volume in alexithymics so far yielded inconsistent

results. With our study, we support the result of Borsci et al.

(2009), who also reported a reduced GM volume in female

high alexithymics, and extend them to a mixed gender

sample. The discrepancy to the result of Heinzel et al.

(2011) could be explained by their alpha correction method:

an FDR-corrected significance level of 0.05 appears very

conservative when operating on smoothed data (Nichols

and Hayasaka, 2003). This threshold might thus have con-

cealed actual differences. The difference to Gundel et al.

Table 1 – Significant results for the comparisons LA4HA with age and gender as covariate.

Analysis Region Cluster level Peak level

k p Coordinates Z-score p

ROI-based ACC 3565 o.001 (2,44,�3) 3.57 o.001

Left amygdala 57 .044 (�30,6,�18) 2.8 .003

Left anterior Insula 360 .015 (�35,18,6) 2.54 .006

Whole brain left MTG 1200 .003 (�48,–75,12) 4.75 o.001

Note: ROI¼Region of interest; ACC¼anterior cingulate cortex; MTG¼middle temporal gyrus; at cluster level the cluster extent k and the

probability p of obtaining a cluster size with a voxel-wise threshold of p¼0.05 (ROI-based) or p¼0.001 (whole brain) respectively are given; at

peak level, coordinates (x,y,z) in MNI space, z-score and uncorrected p value are given for the maximum of the cluster. Only regions containing

significant clusters after correction with AlphaSim are reported. No significant clusters were revealed for the contrast HA4LA.

b r a i n r e s e a r c h 1 4 9 1 ( 2 0 1 3 ) 6 0 – 6 7 63

(2004) seems explainable by the procedures checking for

volume differences: in contrast to our automated VBM

approach, a manual procedure for tracing the ACC might

have been susceptible to variance induced by human percep-

tion of tissue boundaries (Dickstein et al., 2005). Less GM

volume in high alexithymics is consistent with many func-

tional studies investigating emotional processing in alexithy-

mia that report less ACC activity with increasing alexithymia

(e.g. Kano et al., 2003; Lane et al., 1998; McRae et al., 2008) and

support the theory of Lane et al. (1997) about a specific role of

the ACC in alexithymia. In a seminal paper on implicit and

explicit emotional processes, Lane (2008) states that the

dorsal regions of the ACC are most relevant for the manifes-

tation of alexithymic features. He supposes that alexithymia

is linked to problems in directing attention to emotions that

leads to a lack of conscious experience of emotions. Differ-

ences in GM in the dorsal region of the ACC could then be

related to attentional processes given that this dorsal part is

normally considered the cognitive or attention division of the

ACC (Bush et al., 2000). The current study underscores this

idea as the revealed cluster extends to dorsal regions. None-

theless, according to our results, also the subgenual, ventral

part of ACC shows reduced gray matter volume. It is not

surprising that this region is of relevance in alexithymia,

because it is traditionally seen to be the affective division of

the ACC (e.g Bush et al., 2000; Whalen et al., 1998). Moreover,

a lesion study by Schafer and colleagues (Schafer et al., 2007)

reports alexithymia-like syndromes in a female patient that

suffered from an infarct in the perigenual part of the ACC.

Her affected region spans a comparable area as the cluster

found in the current study. Thus, it seems that alexithymia is

related to reductions in GM across several parts of the ACC.

Studies combining functional and structural measures are

needed in order to clarify the exact involvement of the ACC

and its subdivisions.

High alexithymics compared to low show a lesser gray matter

volume in the left amygdala. The amygdala plays a significant

role during the early processing of facial emotions, especially

fear (Adolphs, 2002; Adolphs et al., 2005). Recent imaging

studies on automatic processing of facial emotions in alexithy-

mia have found a functional amygdala hyporesponsivity of high

alexithymic individuals (Kugel et al., 2008; Reker et al., 2010). In

this vein, the revealed reduced GM volume in high alexithymics

might promote the functional hyporesponsivity. However, the

amygdala is not a homogenous structure but consists of several

interconnected nuclei that also receive input from different

regions of the brain (e.g. Amunts et al., 2005; LeDoux, 2003).

Thus, it may be worthwhile to investigate the involvement of

the different nuclei in alexithymia in future studies. Addition-

ally, it could be the case that the connectivity of the amygdala

with other regions is altered as a function of alexithymia. This

could be explored using diffusion tensor imaging and (resting

state) functional connectivity as has been done in a recent work

by Baur et al. (2012) for anxiety.

The anterior insula is thought to play an eminent role in

feelings and empathy (Singer et al., 2009) and could therefore

be linked to alexithymia. Reduced (anterior) insula activity in

high alexithymics was revealed during empathy processing

(Bird et al., 2010), interoception (Silani et al., 2008) and during

automatic processing of emotional faces (Reker et al., 2010).

Moreover, the VBM study of Borsci and colleagues (2009)

reports GM volume reductions with high alexithymia, albeit

a small cluster at a fairly lenient threshold. Thus, diminished

activity of the anterior insula in high alexithymia in different

emotion processing paradigms might be due to the revealed

reduced gray matter volume in the anterior insula in high

alexithymics compared to low. Our results support this

notion by revealing decreased GM volume in high alexithy-

mics in the left anterior insula. Still, studies have to be

accomplished that compare insula activation during auto-

matic and controlled processing of emotions as a function of

alexithymia and gray matter volume to clarify the exact

involvement of the anterior insula.

Despite functional studies reporting less activation of the

fusiform gyrus in high alexithymics (Duan et al., 2010;

Eichmann et al., 2008; Reker et al., 2010), the current study did

not expose differences in gray matter between high and low

alexithymics. So it seems that this reduced activation is not

related to gray matter differences in the FFG. The FFG is a

sensory region which can be modulated by emotions (Pizzagalli

et al., 2002), but it is traditionally not seen as a part of the limbic

system. Therefore, it may be so, that reduced activation of the

FFG in high alexithymia during emotion processing is not related

to a reduced gray matter volume. It can be so, that the FFG gets

less input from limbic areas such as the amygdala, which leads

to that reduced activation. However, it may also be so, that the

mask for the FFG taken from automated anatomical labeling

(AAL) toolbox (Tzourio-Mazoyer et al., 2002) is defined too broad

to localize the regions of the FFG that play the most important

role in face processing in alexithymia. Future studies combining

functional and structural methods should investigate the invol-

vement of the FFG in alexithymia.

b r a i n r e s e a r c h 1 4 9 1 ( 2 0 1 3 ) 6 0 – 6 764

3.2. LA4HA: exploratory whole brain analysis

The whole brain approach provided one significant cluster in

the left middle temporal gyrus (MTG) indicating less gray

matter (GM) volume in high alexithymics compared to low.

One earlier work reports less GM volume in the MTG with high

alexithymia (Borsci et al., 2009). Moreover, in the functional

neuroimaging study by Kano et al. (2003), activity in the left

MTG negatively correlated with the individual TAS-20 score

when viewing facial expression. In contrast, Berthoz et al.

(2002) reported increased activation in the middle temporal

gyrus in high alexithymics in a passive viewing task with

positive and negative picture of the International Affective

Picture System (IAPS; Lang et al., 1997). Although the results of

the functional studies are equivocal, they point to an involve-

ment of the middle temporal gyrus in alexithymia. This

involvement may be linked to the reduced GM volume revealed

in our study. However, the exact influence of the MTG on

emotion processing in alexithymia needs to be clarified.

3.3. HA4LA: null findings

According to our analyses (ROI-based approach and whole

brain) high alexithymics compared to low do not show larger

gray matter volume in any part of the brain. Alexithymia is

considered a deficit in emotion processing with high alex-

ithymics showing difficulties in correctly identifying and

labeling facial expression (e.g. Parker et al., 2005; Prkachin

et al., 2009). A recent VBM study has shown that the speed of

facial affect ratings is linked to GM variations in visual and

limbic areas, such as the amygdala: healthy people with less

GM volume needed more time to label the facial expression

(Dean et al., 2012). In this vein, it seems likely that higher

alexithymia is rather related to less gray matter volume in

emotion-relevant areas.

3.4. Possible biological significance of gray matterreductions

Important questions that remain to be answered about gray

matter volume reductions include their cellular basis and func-

tional implications. It has been argued that increases in gray

matter volume could reflect growth and branching of dendritic

trees and an increment in the number of synapses rather than

an increment in the number of cell bodies (Anderson, 2011). The

increased number of synapses may ease the generation of action

potentials in the post-synaptic cell. Accordingly, recent studies

on motor learning show increased gray matter volume after

acquisition of new skills (e.g., Bezzola et al., 2011). Against this

background, reduced gray matter volume in the amygdala in

high alexithymic individuals could be related to less automatic

transfer of emotional information from the amygdalae to other

brain areas as suggested by functional imaging studies (Kugel

et al., 2008; Reker et al., 2010). Less volume in ACC and anterior

insula in alexithymia may contribute by lowered cell connectiv-

ity to less emotional experience as suggested by Lane et al.

(1998), Lane (2008) and others (Silani et al., 2008; Singer et al.,

2009). However, since our understanding of the basic biology

behind gray matter volume reductions is still very limited the

abovementioned assumptions are clearly speculative. Future

research using, for example, animal models has to be accom-

plished to further clarify the relationship between gray matter

volume as revealed by MRI and cellular properties in the brain

(Anderson, 2011).

3.5. Gender differences and alexithymia

Since there seem to be gender differences in emotional

processing (see, e.g., Donges et al., 2012), it may be so, that

differences in morphology of emotion-relevant brain regions

are due to gender. We controlled for this by (1) examining

gender-matched groups and (2) entering gender as a covariate

in our analysis. Interestingly, findings from a recent study

(Campanella et al., 2012) indicate that sex differences in

emotional processing can be predicted by the level of alex-

ithymia (and depression). Therefore, it seems likely that the

revealed reductions in gray matter in our study are really

related to alexithymia, not to gender. Our sample size did not

allow us to stratify between gender and alexithymia (the

group sizes would drop to eight and nine subjects which is

substantially below the recommended group size of 16–32

individuals in neuroimaging studies; see Friston (2012)).

3.6. Limitations

As a limitation, it shall be mentioned that there have been

concerns whether a self-descriptive questionnaire, such as

the TAS-20, is able to tap people’s difficulties in describing

and identifying feelings (Gundel et al., 2000; Suslow et al.,

2001). Performance- and observer-based procedures such as

the Levels of Emotional Awareness Scale (LEAS, Lane et al.,

1990) and the Toronto Structured Interview for Alexithymia

(TSIA, Bagby et al., 2006) have to be administered in future

studies to reach more definite conclusions about the relation-

ship between alexithymia and brain morphology.

As alexithymia is a personality trait normally distributed

among the population (Franz et al., 2008), it may be interesting

to examine brain morphology as a function of alexithymia

based on a correlational design which requires large samples.

In our study, we chose an extreme group comparison in order

to be more sensitive to the effects of alexithymia because our

participant pool included mostly individuals with TAS-20

scores in the range of low alexithymia. In the future, studying

larger samples with a significant number of individuals

characterized by high alexithymia in correlational designs

could add valuable insights about the effects of alexithymia

on brain morphology. As a side effect, large study samples

would also resolve problems with nuisance variables in SPM

correlation analyses which emerge in small samples.

4. Conclusion

To conclude, alexithymia is associated with smaller gray

matter volume in ACC, amygdala and anterior insula as well

as in the left MTG. Since all these regions have previously

been shown to be engaged in different stages of emotional

processing, alexithymics’ alterations in emotion processing

may—to some extent—be promoted by these structural

reductions.

Table 2 – Socio-demographic description of the sample.

HA LA T-statistics

t (d.f.) p

N (females) 17 (8 f.) 17 (8 f.)

Age 36 (11) 40 (12) t(32)¼1.1 .26

TAS 54.4 (5.9) 26.6 (2.0) t(19.6)¼�18.5 o.001n

BDI 1.4 (1.9) 1.5 (1.6) t(32) ¼ .1 .9

Note: N¼Sample size; HA¼High alexithymics; LA¼Low alexithymics; Age¼Age in years; TAS¼Score on 20-item Toronto Alexithymia Scale;

BDI¼Score on Beck Depression Inventory. For Age, TAS and BDI mean (and standard deviation) is given. Significant differences are marked

with an asterisk.

All participants were right-handed and had a BDI o11.

b r a i n r e s e a r c h 1 4 9 1 ( 2 0 1 3 ) 6 0 – 6 7 65

5. Experimental procedure

5.1. Participants

161 healthy volunteers completed a battery of psychological

instruments including the TAS-20 (Bach et al., 1996) and the

Beck Depression Inventory (BDI, Hautzinger et al., 1995).

Seven individuals had to be excluded due to missing data.

Of the remaining 154, 17 (eight female) participants had a

TAS-20 score greater or equal to 50 and were classified as high

alexithymic (HA). A gender-matched group (n¼17) of low

alexithymics (LA) was selected from the individuals showing

lowest TAS-20 values in the sample. Both groups differed

significantly in terms of their TAS-20 score but not in terms of

their age or BDI score. None of the selected participants had a

BDI 411 and any history of psychiatric, neurological and

severe medical illness or abuse of psychoactive substances.

See Table 2 for an overview of the socio-demographic char-

acteristics of study participants.

The study was conducted in accordance with the Declara-

tion of Helsinki. It was approved by the Ethics Committee of

the University of Muenster and written informed consent was

obtained from all participants prior to the commencement of

the study.

5.2. Data acquisition

Participants were scanned at a 3 T scanner (Gyroscan Intera

3T, Philips Medical Systems, Best, The Netherlands). T1-

weighted high resolution anatomical images were acquired

with a 3D fast gradient echo sequence (‘Turbo Field Echo’,

TFE), TR¼7.4 ms, TE 3.4 ms, FA¼91, 2 signal averages, contrast

augmented by an inversion prepulse every 814.5 ms, acquired

over a field of view of 256 mm (FH)�204 mm (AP)�160 mm

(RL), phase encoding in AP and RL direction, acquired voxel

size 1.0 m�1.0 mm�1.0 mm, reconstructed by zero padding

(spectral interpolation) to cubic voxels of 0.5 mm edge length.

5.3. Preprocessing

The VBM8-toolbox (version 419; http://dbm.neuro.uni-jena.

de/vbm) was used for preprocessing the structural images

with default parameters. Images were bias-corrected, tissue

classified (Ashburner and Friston, 2005) including high-

dimensional Diffeomorphic Anatomical Registration Through

Exponentiated Lie algebra (DARTEL) normalization. As the

DARTEL template is in MNI space, data was transformed into

MNI space in this step. Gray and white matter (WM) segments

were modulated only by the non-linear components in order

to preserve actual GM and WM values locally (modulated GM

and WM volumes). The modulated GM images are corrected

for individual brain size when using the default parameters

of VBM8. The gray matter images were smoothed with a

Gaussian kernel of 8 mm FWHM. Homogeneity of gray matter

images was checked using the covariance structure of each

image with all other images, as implemented in the check

data quality function. None of the participants showed any

anatomical abnormalities or artifacts in the images.

5.4. Data analysis

All statistical models and tests were analyzed using Statis-

tical Parametric Mapping 8 (SPM8; http://www.fil.ion.ucl.ac.

uk/spm/software/spm8/). The masks for the ACC, the amyg

dala and the fusiform gyrus were defined according to the

automated anatomical labeling (AAL) toolbox (Tzourio-

Mazoyer et al., 2002) as implemented in the WFU Pick Atlas

(Maldjian et al., 2003) using SPM8. As the ROI for the insula in

the AAL is not subdivided into an anterior and a posterior

part and the anterior insula is thought to be most important

for alexithymia, we decided to define a ROI based on the

results of the study of Silani et al. (2008). A sphere with a

radius of 10 mm was drawn around the MNI coordinates of

their revealed maximum of activation in the left anterior

insula (center: [x¼�32, y¼30, z¼2]) and analog for the right

anterior insula (center: [x¼32, y¼20, z¼2]). Utilizing t-con

trasts, gray matter was compared between HA and LA. In

order to control for gray matter volume changes related to

age and gender, these factors were included as nuisance

covariate in the model. Given our a-priory hypotheses, a

ROI-based analysis was accomplished on the ACC, the amyg

dala, the fusiform gyrus and the anterior insula. Monte Carlo

simulations (AlphaSim, Ward, 2000; as implemented in the

Resting-State fMRI Data Analysis Toolkit REST V1.6, http://

restfmri.net/) were used to determine a cluster size threshold

(k) in order to maintain a cluster corrected significance level

of po0.05, given a voxel-wise threshold of po0.05 (uncor

b r a i n r e s e a r c h 1 4 9 1 ( 2 0 1 3 ) 6 0 – 6 766

rected) for each bilateral ROI. Two supra-threshold voxels are

declared to belong to one cluster if they are no more than 2.

2 mm away from each other. The inherent smoothness of the

individual ROIs was calculated on the model residuals using the

Matlab function y_Smoothest, which is provided by the REST

developers on their website (http://pub.restfmri.net/index.

php?dir=&file=Programs_YAN.zip). Based on these parameters,

significant cluster sizes were as follows: k¼1191 for the ACC,

k¼48 for the amygdala, k¼182 for the anterior insula, and

k¼671 for fusiform gyrus. Additionally an exploratory whole

brain analysis was accomplished. For this po0.001 was chosen

in order to preserve local specificity in the clusters. Using

AlphaSim, the empirically determined cluster threshold was

k¼568 voxels.

As sub-clinical depression is strongly associated with the

TAS-20 score (Honkalampi et al., 2000) and thus may influence

the results, we also calculated a model with BDI as additional

nuisance covariate. This did not change the findings substan-

tially, so that results are not presented and discussed.

Acknowledgment

The authors thank Dr. Bertram Walter from the Bender Institute

for Neuroimaging, Gießen, Germany, for some helpful advice on

the statistical methods employed. We thank Mrs. Nina Nagel-

mann for her skillful technical support during the MRI data

acquisition. This work was supported by a grant from the

German Research Foundation [SU 222/6-1] to Thomas Suslow

and Harald Kugel and a grant from fund Innovative Medical

Research of the University of Munster Medical School [IMF

DA211012; DA120903; DA111107] to Udo Dannlowski.

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27

2.2 Study 2: Alexithymic features and the labeling of brief emotional facial expressions

– an fMRI study (Ihme et al., 2014a)

Study 2 was designed and conducted within a research project supported by a grant of the German

research foundation (Deutsche Forschungsgemeinschaft) to Thomas Suslow and Harald Kugel (grant

number SU 222/6‐1). The supplementary materials of Study 2 can be found in the Appendix on page 77.

Alexithymic features and the labeling of brief emotional facialexpressions – An fMRI study

Klas Ihme a, Julia Sacher b,c, Vladimir Lichev a, Nicole Rosenberg a, Harald Kugel d,Michael Rufer e, Hans-Jörgen Grabe f,g, André Pampel h, Jöran Lepsien h, Anette Kersting a,Arno Villringer b,c, Richard D. Lane i, Thomas Suslow a,j,n

a Department of Psychosomatic Medicine and Psychotherapy, University of Leipzig, Semmelweisstr. 10, 04103 Leipzig, Germanyb Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103 Leipzig, Germanyc Clinic of Cognitive Neurology, University of Leipzig, Liebigstraße 16, 04103 Leipzig, Germanyd Department of Clinical Radiology, University of Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149 Münster, Germanye Department of Psychiatry and Psychotherapy, University Hospital Zurich, Culmannstr. 8, 8091 Zurich, Switzerlandf Department of Psychiatry, University of Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germanyg Department of Psychiatry, HELIOS Hospital Stralsund, Rostocker Chaussee 70, 18437 Stralsund, Germanyh Nuclear Magnetic Resonance Unit, Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103 Leipzig, Germanyi Department of Psychiatry, University of Arizona, 1501 N. Campbell Ave., Tucson, AZ 85724, USAj Department of Psychiatry, University of Münster, Albert-Schweitzer-Str. 11, 48149 Münster, Germany

a r t i c l e i n f o

Article history:Received 26 May 2014Received in revised form18 August 2014Accepted 24 September 2014Available online 2 October 2014

Keywords:AlexithymiaEmotional facial expressionsStriatumToronto Alexithymia ScaleToronto Structured Interview forAlexithymia

a b s t r a c t

The ability to recognize subtle facial expressions can be valuable in social interaction to infer emotionsand intentions of others. Research has shown that the personality trait of alexithymia is linked todifficulties labeling facial expressions especially when these are presented with temporal constraints.The present study investigates the neural mechanisms underlying this deficit. 50 young healthyvolunteers had to label briefly presented (r100 ms) emotional (happy, angry, fearful) facial expressionsmasked by a neutral expression while undergoing functional magnetic resonance imaging (fMRI). Amulti-method approach (20-Item Toronto Alexithymia Scale and Toronto Structured Interview forAlexithymia) was administered to assess alexithymic tendencies. Behavioral results point to a globaldeficit of alexithymic individuals in labeling brief facial expressions. Alexithymia was related todecreased response of the ventral striatum to negative facial expressions. Moreover, alexithymia wasassociated with lowered activation in frontal, temporal and occipital cortices. Our data suggest thatalexithymic individuals have difficulties in creating appropriate representations of the emotional state ofother persons under temporal constraints. These deficiencies could lead to problems in labeling otherpeople's facial emotions.

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1. Introduction

Being able to recognize non-verbal cues and especially emo-tional facial expressions of others is very important to understandintentions, emotions and beliefs of fellow human beings andtherefore a prerequisite for the ability to interact socially(Erickson and Schulkin, 2003). Research in the last decades hasshown that processing facial expressions requires the recruitmentof several cortical and subcortical structures. In addition to areasimplicated in vision and processing of visual information from theface (e.g., occipital areas, superior temporal, and fusiform gyrus),

brain regions involved in experiencing and feeling emotions are alsothought to be crucial when processing facial expressions of others(Adolphs, 2002a; Heberlein and Adolphs, 2007; Heberlein andAtkinson, 2009). Specifically, these include motor areas which arerelated to the simulation of changes in the configuration of facialmuscles and parts of the somatosensory network (e.g., primarysomatosensory cortex, posterior insula) for simulation of proprio-ceptive feedback related to the seen emotion (Adolphs et al., 2000,2002a; Sel et al., 2014). Frontal and limbic areas play an important role in reenacting and feeling the according emotion(e.g., amygdala, striatum, frontal areas, anterior insula) (Adolphs,2002a; Heberlein and Adolphs, 2007; Trinkler et al., 2013). Inaddition, structures like the inferior frontal gyrus, insula, middleand superior temporal gyrus as well superior frontal and pre-motorareas are thought to be involved in the production and feeling ofemotions, but also in recognizing these in others (Iriki, 2006;

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Neuropsychologia

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n Corresponding author at: Department of Psychosomatic Medicine andPsychotherapy, University of Leipzig, Semmelweisstr. 10, 04103 Leipzig, Germany.Tel.: þ49 341 9718891; fax: þ49 341 9718849.

E-mail address: [email protected] (T. Suslow).

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Van der Gaag et al., 2007). In sum, the concerted activation of theaforementioned brain areas creates a holistic representation of theemotional expression of the other person based on affective, motorand sensory processing and simulation (Van der Gaag et al., 2007).Given that the same regions are involved in recognizing one’s ownemotions and those of others (Heberlein and Adolphs, 2007;Heberlein and Atkinson, 2009), it is well possible that individualswho have difficulties in recognizing and describing own feelingsalso have problem to label facials expressions of others.

Alexithymia, which literally translates to “no words for emo-tions”, is a personality trait that has been characterized as theinability to identify and describe feelings. Moreover, alexithymicindividuals show an externally oriented cognitive style accompa-nied by a reduced imaginary life (Sifneos, 1973; Taylor, 2000). Ithas been found that alexithymic features are linked to interperso-nal problems (Spitzer et al., 2005) as well as poorer social skillsand little perceived social support (Lumley et al., 1996). Given theimportance of non-verbal cues, such as facial expressions, in socialinteraction, Spitzer et al. (2005) proposed that difficulties ininterpersonal relationships of alexithymic individuals may resultfrom problems to express own emotions and reliably label facialexpressions of others. Indeed, recent studies have shown thatalexithymia is related to poor non-verbal expressiveness (Troisiet al., 1996) and less automatic mimicry of facial expressions(Sonnby-Borgström, 2009). Concerning the labeling of facial emo-tions, it has been reported that alexithymic tendencies are asso-ciated with decreased performance in terms of labeling accuracy,especially when the faces are presented under temporal (Cooket al., 2013; Parker et al., 2005; Swart et al., 2009) or otherperceptual (Kätsyri et al., 2008) constraints. Under conditionswith little or no constraints some studies have reported a relation-ship between recognition accuracy and alexithymia (e.g., Laneet al., 1996, 2000; Mann et al., 1994; Parker et al., 1993), whileothers did not reveal such correlations (e.g., Mann et al., 1995;Pandey and Mandal, 1997). Indeed, the duration of face presenta-tion at which labeling performance of alexithymic individualsdeclines has been determined to be between 300 ms (Grynberget al., 2012) and 1 s (Parker et al., 2005). Taken together, alex-ithymic individuals show decreased recognition accuracy underconditions of perceptual or temporal constraints and little or noaccuracy abnormalities when emotional facial expressions areclearly visible and presented for long durations.

Two previous studies have investigated the neural underpin-nings of the difficulties of alexithymic individuals in recognizingfacial expressions, however only with relatively long stimuluspresentation times of 3.73 s (Mériau et al., 2006) and 1 or 3 s(Ihme et al., 2014). Importantly, neither of the studies found arelationship between alexithymia and labeling accuracy. In addi-tion, Mériau et al. (2006) did not reveal neural activity related tothe degree of alexithymia which could be due to a relatively lownumber of 23 participants in a correlational design (Yarkoni andBraver, 2010, recommend at least 40 participants). Furthermore,alexithymic tendencies were only assessed using self-report,although measuring alexithymia with a multi-method approachis desirable (Lichev et al., 2014; Lumley et al., 2005; Taylor andBagby, 2004). Interestingly, using a multi-method approach toassess alexithymia, we recently revealed that response latencieswhen labeling facial expressions are increased in alexithymia(Ihme et al., 2014). Moreover, alexithymic features correlated withincreased activation in brain areas related to somatosensory andmotor processing during the labeling of facial emotions. Thisindicates that alexithymic individuals rely on information relatedto the bodily configuration of the facial expression rather thanaffective cues when inferring others' emotions (Ihme et al., 2014,see also Laricchiuta et al., in press; Moriguchi and Komaki, 2013).This mechanism presumably enables alexithymic individuals to

correctly label facial expressions presented with little temporalconstraints. However, when temporal constraints increase andfaces are presented for less than 1 s (Grynberg et al., 2012;Parker et al., 2005), performance decreases suggests that theincreased processing of bodily features does not work anymore,as alexithymic individuals may need more perceptual informationto process emotional facial expressions (Grynberg et al., 2012).Interestingly, to the best of our knowledge, no studies exist thatinvestigate the neural mechanisms of labeling brief facial expres-sions as a function of alexithymic features. In some studies facialexpressions were presented only for 33 ms and masked by neutralexpressions and brain response to these very brief facial expres-sions was examined as a function of self-reported alexithymia(Duan et al., 2010; Eichmann et al., 2008; Kugel et al., 2008; Rekeret al., 2010). It was observed that alexithymia is related todecreased activation in the amygdala, insula, fusiform gyrus andsuperior temporal gyrus. These findings suggest that alexithymicindividuals encode affective information to a lesser degree at avery early stage of processing. Still, these latter studies investi-gated automatic or non-conscious processing of affective informa-tion using affective priming paradigms (see Murphy and Zajonc,1993; Suslow et al., 2013) and were not interested in processesrelated to labeling facial expressions.

The aim of the present functional magnetic resonance imaging(fMRI) study was to elucidate the neural processes involved in thedifficulties of alexithymic individuals to explicitly label facialemotions (as opposed to the neural processes related to automaticaffective reactions as assessed in the studies of Duan et al., 2010,Eichmann et al., 2008, Kugel et al., 2008, and Reker et al., 2010).Different from prior neuroimaging studies (Ihme et al., 2014;Mériau et al., 2006), we presented facial expressions only brieflyfor 66 and 100 ms which are presentation durations for which analexithymia-related accuracy deterioration in labeling perfor-mance is highly likely (cf. Grynberg et al., 2012). We hypothesizedthat alexithymic features are negatively related to labeling accu-racy and activation in brain areas that are implicated in creating arepresentation of the emotional state of other persons.

2. Methods

2.1. Participants

Fifty-two healthy young German native speakers (26 women, aged between 18and 29 years) participated in the study. All of them were right-handed and hadnormal or corrected-to-normal visual acuity. None of the participants had anyhistory of neurological (according to self-report) or psychiatric illnesses (StructuredClinical Interview for DSM-IV Axis I disorders [SCID-I]; Wittchen et al., 1997) orcontraindications for magnetic resonance imaging. The procedure of the study wasexplained to participants before the experiment. All participants gave writtenconsent to participate and were financially compensated upon completion of thestudy. The study's procedure was approved by the ethics committee of theUniversity of Leipzig, Medical School, and in accordance with the Declaration ofHelsinki.

2.2. Assessment of alexithymia

A multi-method approach was administered in order to assess alexithymictendencies. Participants completed the 20-item-version of the Toronto AlexithymiaScale (TAS-20, Bagby et al., 1994, German version: Bach et al., 1996) and weresubsequently observer-rated using the Toronto Structured Interview for Alexithy-mia (TSIA, Bagby et al., 2006, German version: Grabe et al., 2009, 2014). Thecomplete TSIA was administered by one trained interviewer and rated during theinterview according to the manual. Before the study, the interviewer was trained toconduct and score the TSIA by the translators of the German version of the TSIA(coauthors MR and HJG). This included becoming familiar with the alexithymiaconstruct, the manual outlining administration and the scoring procedures for theTSIA, as well as discussion of the guidelines, the scoring of the items and the correctuse of the prompts and probes. Moreover, test interviews were supervised until theinterviewer was secure in the administration and scoring of the interview. The TAS-20 includes three subscales, namely difficulties describing feelings (DDF),

K. Ihme et al. / Neuropsychologia 64 (2014) 289–299290

difficulties identifying feelings (DIF) and externally-oriented thinking. Besidesthese three subscales, the TSIA also includes the measurement of imaginalprocesses. We focused our analysis on the TAS-20 and TSIA total scores and thesubscales DDF and DIF as these two facets of the construct have been linked tolabeling and processing of facial expressions of emotion (Grynberg et al., 2012).

2.3. Assessment of depressive symptoms and trait affectivity

Participants completed the Beck Depression Inventory (BDI, German version:Hautzinger et al., 2006), the trait version of the State-Trait-Anxiety-Inventory(STAI-T, German version: Laux et al., 1981) and the trait version of the Positive andNegative Affect Schedule (PANAS, German version: Krohne et al., 1996). Thesequestionnaires were administered to control for participants' (negative) affectivitywhich could be linked to alexithymia (e.g., Honkalampi et al., 2000).

2.4. Experimental task

Participants' task was to label the facial emotion of a briefly presented target facemasked by a neutral face of the same identity. The facial stimuli were colorphotographs taken from the Karolinska Directed Emotional Face database (KDEF,Lundqvist et al., 1998) depicting four different emotions (happy – HA, angry – AN,fearful – FE, and neutral). As the neutral face was used only as a mask, a verticallymirrored version of the neutral mask face was utilized as neutral target (NE). Picturesof ten different individuals (five females) were shown and each person was showntwice in each of the four emotion conditions, so that the experiment consisted of 80trials (2�10 persons�4 emotions). Each trial lasted 9 s and started with thepresentation of a fixation cross in the center of the screen for 800 ms. Then a briefemotional facial expression was shown (66 ms or 100 ms) which was masked by aneutral facial expression (434 ms or 400 ms respectively). The presentation times forthe target andmask were determined in a pilot study with nine participants, for whichidentification performance was on average about .75 (as measured with the sensitivityindex by Aaronson and Watts (1987)). After presentation of the mask, participants hadto label the facial emotions by button presses in a forced choice manner within max.7700 ms (for a detailed overview, see Fig. 1). Participants were provided with oneresponse pad per hand with two buttons each and responded with the index andmiddle fingers. Each emotionwas assigned to one button during the entire experimentin a counterbalanced design. Participants were instructed to respond as correct asaccurate as possible within the given time frame. Trials were shown in two fixedrandom sequences with the constraints that no two subsequent trials depict the sameperson, not more than two subsequent trials show the same emotion and that nopicture (i.e., same target emotion and same person) was shown twice per half.

For the later analysis, we pooled across both target expression presentationtimes (66 ms and 100 ms) in order to increase the number of trials per condition.Originally, we aimed at differentiating between both presentation times. However,there were no significant differences between both presentation times regardingthe correlations of labeling performance with alexithymic features (TAS-20 total,TAS-20-DDF, TAS-20-DIF, TSIA total, TSIA-DDF and TSIA-DIF) as assessed withSteiger's Z (Steiger, 1980) (all ps4 .05) (calculated using formulas implemented inLee and Preacher (2013)). These findings are in line with a recent review (whichwas published after data collection for this study has started) concluding that highalexithymic individuals have problems labeling facial expressions when these arepresented below 300 ms (Grynberg et al., 2012). Both presentation times adminis-tered in the current study were clearly below this time value, so that the poolingseems also justified based on the literature. Please refer to Supplementarymaterials for details of the Steiger's Z statistics.

2.5. fMRI scanning protocol

Structural and functional MR imaging was performed on a 3T scanner(Magnetom Verio, Siemens, Erlangen, Germany) using a standard 12-channel headcoil. For each participant, structural images were acquired with a T1-weighted 3DMP-RAGE (Mugler and Brookeman, 1990). Magnetization preparation consisted of a

non-selective inversion pulse. The imaging parameters were TI¼650 ms, TR¼1300 ms, TE¼3.5 ms, flip angle 101, isotropic spatial resolution of 1 mm3, twoaverages. Blood oxygen level dependent (BOLD) contrast sensitive images werecollected using a T2n-weighted echo-planar imaging (EPI) sequence (matrix 642;resolution 3 mm�3 mm�4 mm; gap .8 mm; TR¼2 s; TE¼30 ms; flip angle 901;interleaved slice acquisition; 385 images). Slices were oriented parallel to a linethrough the posterior and anterior commissures.

2.6. Data analysis

2.6.1. Measures of alexithymia and affectivityThe TSIA was rated by a trained interviewer according to the manual. To test

our hypotheses, TAS-20 and TSIA total scores as well as the respective DDF and DIFsubscales scores were taken into account for statistical analysis. To control forrelationships of alexithymia with (negative) affectivity correlations were calculatedbetween measures of alexithymia and BDI, STAI-T, and PANAS.

2.6.2. Behavioral dataFor each trial of the experiment, responses were extracted and classified as true

positives (e.g., angry face correctly labeled as angry) or false positives (e.g., non-angry face incorrectly labeled as angry). From these values a sensitivity score wascalculated for each of the emotions using the procedure described by Aaronson andWatts (1987). This index varies between 0 and 1, with 1 denoting perfectperformance and .5 referring to performance at chance level as it controls formore than one choice alternative. As proposed by Yarkoni and Braver (2010), wecalculated the reliability of our performance measure using Cronbach’s alpha.Performance data were analyzed for significant differences between the conditionswith an analysis of variance (ANOVA). Product–moment correlations were calcu-lated between the alexithymia measures and performance for each facial expres-sion condition using SPSS 20.0. In case of deviation from normal distribution,correlation coefficients were estimated using rank correlations (Spearman's rho).

2.6.3. fMRI preprocessing and data analysisMRI data were preprocessed and analyzed using SPM8 (http://www.fil.ion.ucl.

ac.uk/spm/). The initial five functional volumes were discarded in order to allowlongitudinal magnetization to reach equilibrium. Functional volumes were slicetime-corrected (temporal middle slice as reference), realigned to the temporallyfirst image and corrected for movement-induced image distortions (6-parameterrigid body affine realignment). Functional and anatomical images were co-registered. Anatomical images were then segmented, including normalization toa standard stereotaxic space using the T1 template by the Montreal NeurologicalInstitute (MNI) delivered with SPM. The normalization parameters were thenapplied to the functional EPI series. On the functional data, spatial smoothing wasperformed using a three-dimensional Gaussian filter of 6 mm full-width at half-maximum (FWHM). We chose this rather small smoothing kernel such that thepotential activation in subcortical areas involved in facial emotion processing wasstill detectable and not washed out.

Data were analyzed using event-related models which means that the onset ofthe targets was modeled using a stick function and convolved with the hemody-namic response function (default in SPM) for the different expression conditions.We sought to use the neutral faces as baseline for the emotional face condition toisolate the effects of emotional content of target stimuli from other aspects of thestimuli. Given that there was a relationship between some of the alexithymiameasures and performance for neutral faces, we wanted to ensure that neuralprocessing to neutral faces was relatively independent of alexithymic features.Thus, we calculated first-level t-contrasts for the neutral condition alone and usedthese as input for six second-level regression models with one alexithymiameasure (TAS-20 total, TAS-20-DDF, TAS-20-DIF, TSIA total, TSIA-DDF, or TSIA-DIF) as input variable. None of these models revealed a significant relationshipbetween alexithymic features and activation for neutral faces administering afamily-wise-error-(fwe)-corrected significance threshold on cluster level of p¼ .05at an individual voxel threshold of t¼3.27 (p¼ .001). Against this background, theuse of the neutral face baseline appeared justified.

First level t-contrasts were calculated by contrasting each emotional condition tothe neutral one (i.e., HA4NE, AN4NE, FE4NE). The contrast images of the first levelcontrasts were then transferred to the second level into regression models for each ofthe main contrasts. These three models were analyzed as a manipulation check. Inorder to estimate the influence of alexithymic features, we calculated second levelmodels using the TAS-20 and TSIA total scores as well as the DIF and DDF subscalescores of TAS-20 and TSIA as regressors of interest. In this way, 18 different secondlevel models were calculated with respect to alexithymic features (6 measures ofalexithymic features�3 conditions). Significance was tested at cluster level against anfwe-corrected significance threshold of p¼ .05 at an individual voxel threshold oft¼3.27 (p¼ .001). Because there were modest negative correlations between somemeasures of alexithymia (TAS-20 total, TAS-20-DDF, TSIA total and TSIA-DDF) andpositive affectivity (see Section 3.1), we calculated additional regression analyses forthese variables taking into consideration PANAS-P as nuisance covariate. Similarly, asthe TAS-20-DIF score was significantly related to STAI and PANAS-N (see Section 3.1),these were entered as nuisance covariates into the additional regression analyses.

Fig. 1. Sequence of events within a trial. In the example a fearful face is masked byneutral facial expression.

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3. Results

3.1. Sample and questionnaires

One participant had to be excluded due to an elevated BDIscore (414) at the time of the MRI session. Furthermore, onesubject was excluded because of excessive head motion (43 mmtranslation) during scanning, which could not be corrected for bythe applied motion correction algorithm. Thus, 50 participants (24women) with a mean age of 23 years (standard deviation 3 years)were included in the analysis. All remaining participants had aBDIr14. Mean values, standard deviations and ranges of thealexithymia measures (TAS-20, TSIA) and affectivity measures(BDI, STAI-T, and PANAS) are given in Table 1. TAS-20 and TSIAscales had satisfactory internal consistencies as determined byCronbach's alpha (TAS-20 total scale: α¼ .86, TAS-20-DDF: α¼ .86,TAS-20-DIF: α¼ .81, TSIA total scale: α¼ .93, TSIA-DDF ¼ .89, TSIA-DIF: α¼ .83). Kolmogorov–Smirnov tests indicated that the dis-tribution of TAS-20-DIF (Kolmogorov–Smirnov Z¼1.5, po .05),TSIA-DDF (Kolmogorov–Smirnov Z¼1.9, po .01) and TSIA-DIF(Kolmogorov–Smirnov Z¼1.9, po .01) scores significantly deviatedfrom normal distribution. No significant deviations from normaldistribution were revealed for the remaining three measures ofalexithymia (all ps4 .05) and the measures of affectivity (BDI,STAI-T, PANAS-N, and PANAS-P; all ps4 .05).

As could be expected, the measures of alexithymia werecorrelated with each other. From the alexithymia measures, onlyTAS-20-DIF correlated with negative affectivity (i.e., STAI andPANAS-N). None of the alexithymia scores was related to the BDIscore. However, PANAS-P was negatively correlated with TSIA totaland TSIA-DDF and tended to correlate with TAS-20 total and TAS-20-DDF (see Table 2).

3.2. Labeling performance and correlations with alexithymia

The performance measures for each individual condition hadan adequate reliability (all Cronbach's αs4 .85). Only the distribu-tion of the sensitivity scores in the happy condition containedoutliers and differed significantly from normal distribution (Kol-mogorov–Smirnov Z¼1.6, po .01; NE, AN, FE: all ps4 .05). Themean sensitivity indices for the four facial expression conditionswere as follows: HA: .86 (standard error of the mean, SEM¼ .02),NE:.83 (.02), AN:.83 (.02), and FE:.79 (.02). The ANOVA revealed asignificant effect of facial expression (F(3,147)¼6.7, po .001), withperformance for fearful faces being significantly lower comparedto the other expression conditions (HA vs. FE: po .01; NE vs. FE:po .05; AN vs. FE: po .05) and better performance for happy thanangry faces (po .05; all other ps4 .05).

Results from the correlation analysis indicate that the TAS-20total score was negatively correlated with performance for angryfaces and marginally correlated with performance for happy andfearful faces. Negative correlations were also revealed betweenTAS-20-DDF and performance for all facial expression conditions.Controlling for PANAS-P affected these results only in one case: thecorrelation between TAS-20 total score and performance forneutral faces became significant (po .05). TAS-20-DIF showed anegative correlation with performance for angry faces. TSIA total,TSIA-DDF, and TSIA-DIF did not correlate with labeling perfor-mance (see Table 3 for details; Fig. 2 shows the correlationsbetween TAS-20-DDF and labeling performance for angry andfearful faces)

3.3. Brain activation: main effects of facial emotions

For the contrast HA4NE, a significant cluster was revealedincluding parts of the ventro-medial frontal cortex and perigenualanterior cingulate cortex. Angry versus neutral faces (AN4NE)elicited significant activation in the left inferior frontal gyrus.Fearful versus neutral faces (FE4NE) activated the left inferiorfrontal gyrus extending to the insular gyrus, the left superiormedial gyrus including the supplementary motor area, the

Table 1Means, standard deviations and ranges of the alexithymia measures (total scores,DDF and DIF subscale scores) and affectivity measures.

Psychometric measures Mean SD Range

TAS-20 total 43.5 10.8 22–71TAS-20-DDF 12.5 4.6 5–24TAS-20-DIF 12.7 4.5 7–25

TSIA total 16.8 9.8 2–37TSIA-DDF 2.8 3.4 0–11TSIA-DIF 1.4 2.1 0–8

BDI 2.9 3.2 1–14STAI-T 33.9 8.3 20–50PANAS positive 36.1 5.3 22–49PANAS negative 15.0 3.5 10–24

Note: TAS-20¼20-Item Toronto Alexithymia Scale, DDF¼Difficulties DescribingFeelings, DIF¼Difficulties Identifying Feelings, TSIA¼Toronto Structured Interviewfor Alexithymia, BDI¼Beck Depression Inventory, STAI-T¼State-Trait AnxietyInventory trait version, PANAS¼Positive and Negative Affect Schedule, SD¼stan-dard deviation.

Table 2Correlations between alexithymia scales and measures of affectivity.

TAS-20-DDF TAS-20-DIF TSIA TSIA-DDF TSIA-DIF BDI STAI PANAS-P PANAS-N

TAS-20 total .84nn .72nn .45nn .55nn .32n .10 .16 � .25þ � .00TAS-20-DDF .60nn .39nn .54nn .24þ .15 .18 � .22þ � .02TAS-20-DIF .10 .29n .23þ .02 .31n � .17 .25þ

TSIA total .80nn .71nn � .02 � .04 � .32n � .20TSIA-DDF .72nn .12 � .11 � .32n � .14TSIA-DIF .10 � .02 � .17 � .23BDI .44nn � .24þ .32n

STAI � .48nn .40nn

PANAS-P � .08

Note: Significant correlations are marked (nn: po .01, n: po .05, þ: po .1, two-tailed).The coefficients were calculated using Pearson’s product-moment correlation if both measures were normally distributed; for the TAS-20-DIF, TSIA-DDF and TSIA-DIF scoresSpearman’s rho rank correlation was employed. TAS-20¼20-Item Toronto Alexithymia Scale, TSIA¼Toronto Structured Interview for Alexithymia, DDF¼DifficultiesDescribing Feelings, DIF¼Difficulties Identifying Feelings, BDI¼Beck Depression Inventory, STAI-T¼State-Trait Anxiety Inventory trait version, PANAS¼Positive AndNegative Affect Schedule.


striatum (caudate nucleus) bilaterally and right cerebellar regions.An overview of the above-mentioned effects is presented inTable 4.

3.4. Brain activation: relationships with alexithymic features

In the following, the results of the regression models withalexithymic features as regressor are presented. For the modelsincluding alexithymia measures which show correlations withpositive affectivity (TAS-20 total, TAS-20-DDF, TSIA total, TSIA-DDF), information is presented on whether the significance ofresults changes when entering PANAS-P as nuisance covariate intothe regression models (see Tables 5–7). Information on significantclusters related to TAS-20-DIF and TSIA-DIF are mentioned in thetext. No significant correlations were found between brainresponse to emotional faces and TSIA total score.

HA4NE. No suprathreshold clusters correlating with alexithy-mic features were revealed for the contrast HA4NE.

AN4NE. There was a negative correlation between TAS-20-total score and activations in the right fusiform gyrus, right middleoccipital gyrus extending to middle temporal gyrus, right striatum(putamen, caudate) and left superior temporal gyrus extending tosupramarginal gyrus (see Table 5). The TAS-20-DDF score corre-lated negatively with clusters in the right fusiform gyrus and rightstriatum extending to rectal gyrus (see Table 6). With respect toTSIA-DDF, significant clusters were revealed in right supplemen-tary motor area, left superior frontal gyrus, right middle frontalgyrus extending to inferior frontal gyrus, left middle orbito-frontal

Fig. 2. Relationship between TAS-20-DDF and labeling performance. Scatterplots between TAS-20-DDF (x-axis) and sensitivity indices (y-axis) for angry (A) and fearful(B) faces are displayed.

Table 4Brain activation due to facial emotions: main effect analyses (clusters are significant with fwe-correction on cluster level).

Contrasts cluster peak localization

k pfwe x y z Z hem. region

HA4NE 128 o .01 �9 41 1 4.48 left ventro-medial prefrontal cortex, perigenual ACCAN4NE 115 o .01 �51 29 10 4.62 left inferior frontal gyrusFE4NE 1157 o .001 �45 35 2 8.52 left inferior frontal gyrus, insular gyrus

201 o .001 �15 2 �8 5.23 left striatum (caudate) extending to thalamus

138 o .01 12 �79 �38 5.23 right cerebellum, lobus crus

161 o .001 �9 20 43 4.90 left superior medial gyrus, supplementary motor area

71 o .05 9 5 4 5.03 right striatum (caudate)

Note: HA4NE¼contrast happy4neutral; AN4NE¼contrast angry4neutral; FE4NE¼contrast fearful4neutral; k¼cluster extent; pfwe¼ family-wise-error-corrected p-value; xyz¼peak coordinates in MNI space; hem.¼hemisphere; ACC¼anterior cingulate cortex.

Table 3Correlations between alexithymia measures and labeling performance (sensitivityindices) for all facial expression conditions.

Happy Neutral Angry Fearful

TAS-20 total � .20þ � .21þ � .33n � .22þ

TAS-20-DDF � .26n � .31n � .37nn � .32n

TAS-20-DIF � .18 � .17 � .25n � .05TSIA total � .05 � .10 .01 .06TSIA-DDF � .11 .08 � .05 � .10TSIA-DIF � .08 .16 .06 � .02

Note: Significant correlations are marked (nn: po .01, n: po .05, þ: po .1, two-tailed).The coefficients were calculated using Pearson's product-moment correlation if bothmeasures were normally distributed; for the TAS-20-DIF, TSIA-DDF, and TSIA-DIFscores as well as labeling performance for happy faces Spearman's rho rank correlationwas employed. Labeling performance (index of sensitivity) was calculated from truepositives and false positives as suggested by Aaronson and Watts (1987). TAS-20¼20-Item Toronto Alexithymia Scale, TSIA¼Toronto Structured Interview for Alexithymia,DDF¼Difficulties Describing Feelings, DIF¼Difficulties Identifying Feelings.


gyrus, and left striatum (all correlations were negative, seeTable 7). In addition, activation in a right occipital cluster (cuneus,middle occipital gyrus) correlated negatively with TAS-20-DIF(cluster extent k¼98, pfwe-clustero .01, x, y, z¼[15, �94, 16],Zpeak¼5.05, cluster drops below threshold when controlling forSTAI and PANAS-N) (see Fig. 3 for an overview).

FE4NE. For this contrast, a negative correlation with the TAS-20 total score was revealed in the right striatum (putamen,caudate) (see Table 5). Moreover, TSIA-DDF showed a negativecorrelation with a cluster in the left middle temporal gyrusextending to inferior temporal gyrus (Table 7). Finally, there wasa negative correlation between TSIA-DIF and brain activation to

fearful faces in the right supplementary motor area (cluster extentk¼81, pfwe-clustero .05, coordinates in MNI space x, y, z¼[12, 8, 61],Zpeak¼4.65). An overview of these results is presented in Fig. 4.

4. Discussion

The present study is the first one that investigates the neuralcorrelates of labeling briefly presented emotional facial expres-sions as a function of alexithymic features. We revealed thatalexithymic features are negatively related to labeling perfor-mance for happy, angry, and fearful expressions. With respect to

Table 5Correlations between brain response to emotional faces and TAS-20 total score (clusters are significant with fwe-correction on cluster level). All clusters show negativecorrelations with the TAS-20 total score.

Contrasts cluster peak localization PANAS

k pfwe x y z Z pfwe hem. region

HA4NE no suprathreshold clusters.AN4NE 139 o .01 33 �67 1 4.30 .18 right fusiform gyrus fwe

104 o .01 30 �85 13 3.88 .59 right middle occipital gyrus extending to middle temporal gyrus lenient101 o .01 24 14 �5 4.66 o .05 right ventral striatum (Putamen, caudate, Pallidum) fwe72 o .05 �51 �40 13 4.20 .24 left superior temporal gyrus extending to supermarginal gyrus lenient

FE4NE 91 o .05 12 8 �2 4.29 .52 right ventral striatum (Putamen caudate) extending to Insula lenient

Note: The last column (PANAS) indicates which threshold this cluster exceeds when correcting for PANAS-P (fwe¼significant on cluster level, marginal¼marginallysignificant on cluster level, lenient¼survives p¼ .001, kZ10). HA4NE¼contrast happy4neutral; AN4NE¼contrast angry4neutral; FE4NE¼contrast fearful4neutral;k¼cluster extent; pfwe¼ family-wise-error-corrected p-value; xyz¼peak coordinates in MNI space; hem.¼hemisphere.

Table 6Correlations between brain response to emotional faces and TAS-20-DDF (clusters are significant with fwe-correction on cluster level). All clusters show negative correlationswith the TAS-20-DDF.



HA4NE No suprathreshold clustersAN4NE 129 o .01 24 14 �5 4.36 right ventral striatum (putamen, caudatum) extending to rectal gyrus fwe

63 o .05 33 �70 1 4.69 right fusiform gyrus lenientFE4NE No suprathreshold clusters

Note: The last column (PANAS) indicates which threshold this cluster exceeds when correcting for PANAS-P (fwe¼significant on cluster level, marginal¼marginallysignificant on cluster level, lenient¼survives p¼ .001, kZ10). HA4NE¼contrast happy4neutral; AN4NE¼contrast angry4neutral; FE4NE¼contrast fearful4neutral;k¼cluster extent; pfwe¼ family-wise-error-corrected p-value; xyz¼peak coordinates in MNI space; hem.¼hemisphere.

Table 7Correlations between brain response to emotional faces and TSIA-DDF (clusters are significant with fwe-correction on cluster level). All clusters show negative correlationswith TSIA-DDF.



HA4NE no suprathreshold clusterAN4NE 104 o .01 �15 11 49 4.20 left superior frontal gyrus fwe

65 o .05 �9 8 �5 3.68 left ventral striatum (putamen, caudate) marginal64 o .05 30 47 �5 4.01 right middle orbitofrontal gyrus lenient63 o .05 12 8 67 4.48 right SMA fwe62 o .05 33 35 19 3.80 right middle frontal gyrus extending to inferior frontal gyrus fwe

FE4NE 86 o .05 �48 �46 �8 4.15 left middle temporal gyrus extending to inferior temporal gyrus fwe

Note: The last column (PANAS) indicates which threshold this cluster exceeds when correcting for PANAS-P (fwe¼significant on cluster level, marginal¼marginallysignificant on cluster level, lenient¼survives p¼ .001, kZ10). HA4NE¼contrast happy4neutral; AN4NE¼contrast angry4neutral; FE4NE¼contrast fearful4neutral;k¼cluster extent; pfwe¼ family-wise-error-corrected p-value; xyz¼peak coordinates in MNI space; hem.¼hemisphere; SMA¼supplementary motor area.


the neural underpinnings, activation in the striatum was found tobe negatively correlated with alexithymic features – primarilywhen participants were confronted with negative or threateningemotional faces. Moreover, alexithymic individuals showed lessactivation in several other brain areas crucially involved in theprocessing of facial emotional expressions, such as the inferior andsuperior frontal gyrus, the superior and middle temporal gyrus,supplementary motor area as well as the orbito-frontal gyrus,fusiform gyrus and middle occipital gyrus. Our measures ofalexithymia showed correlations with positive affectivity(PANAS-P), but the performance and fMRI results remained largelystable when controlling for positive affectivity. The subscale DIF ofthe TAS-20 correlated with negative affectivity. When controllingnegative affectivity in the regression model, a significant cluster in

the middle occipital cortex dropped below threshold. However,this seems to be the only cluster revealed in this study, which isstrongly related to affectivity instead of alexithymia. In thefollowing our results are discussed against the background of theexisting literature.

4.1. Main effects of emotional faces: labeling performance and brainactivation

Concerning the performance in our facial emotion labeling task(as assessed by the sensitivity index), it can be concluded thatparticipants were on average near .8, which is well above chancelevel but still below perfect recognition. Thus, it appears that ourtask was difficult enough to avoid ceiling effects. On the otherhand, the average performance well above chance level suggeststhat the task was not too difficult. In the fMRI main contrasts, wefound increased activations in the ventro-medial frontal cortex (inthe happy face condition), the inferior frontal cortex (in the angryand fearful face conditions), the inferior frontal cortex, insula andstriatum, superior medial gyrus and cerebellar regions (in thefearful face condition). All of these brain regions are known to beimplicated in the processing and labeling of facial emotions(Adolphs et al., 2000; Adolphs, 2002a, 2002b; Fusar-Poli et al.,2009; Ihme et al., 2014; Iriki, 2006; Tsuchida and Fellows, 2012). Insum, it appears that our paradigm was suitable for studying facialemotion labeling as a function of alexithymic features.

4.2. Relationships between alexithymic features and labelingperformance

Our results suggest that alexithymic features as assessed byself-report (TAS-20) show a negative relationship with perfor-mance when labeling emotional facial expressions presented for100 ms or below. This is in line with previous literature reportingthat alexithymic individuals display difficulties in labeling facialexpressions when these are presented with temporal or otherperceptual constraints (see Grynberg et al., 2012). When the facesare presented with little temporal constraints, in several studiesno associations between alexithymia and labeling accuracy havebeen observed (e.g., Ihme et al., 2014; Parker et al., 2005). Thus,the current study supports the assumption of Grynberg et al.(2012) that alexithymic individuals can not sufficiently process theperceptual information of emotional faces in such a short timewindow. Our study also revealed that the subscale DDF of the TAS-20 showed a (descriptively) stronger correlation with labelingperformance than the TAS-20 total score and the TAS-20-DIF score.Indeed, research has shown that DDF is most predictive forlabeling performance (Ihme et al., 2014; Parker et al., 2005), whileDIF rather seems to be related to automatic processing of affectiveinformation (Kugel et al., 2008; Reker et al., 2010). This isconsistent with the conclusions of Grynberg et al. (2012) whoargued that describing feelings unlike identifying feelings or anexternally oriented thinking style implies semantic processing ofemotional information. This semantic processing also occurs whenattributing a verbal emotional label to a facial expression in alabeling task.

4.3. Relationships between alexithymic features and brain activation

It has been assumed that a holistic internal representation ofthe facial expression is created by concerted processing in motor,affective and somatosensory brain regions, which then facilitatesthe interpretation of other persons' emotional state (Jabbi andKeysers, 2008). A less complete representation could lead toproblems in labeling facial expressions (e.g., Delle-Vigne et al.,2014). The results of our study suggest that high alexithymic

Fig. 3. Selection of clusters of brain activation negatively correlating with alex-ithymic features in the contrast angry4neutral presented in axial (A) and coronal(B) view. Red¼negative correlation with TAS-20 total score, blue¼negativecorrelation with TAS-20-DDF score, green¼negative correlation with TSIA-DDFscore; fwe-corrected significance level of p¼ .05 on cluster level; the pictures on theright display the locations of the slices; coordinates are in MNI space. (Forinterpretation of the references to color in this figure legend, the reader is referredto the web version of this article.)

Fig. 4. Selection of clusters of brain activation negatively correlating with alex-ithymic features in the contrast fearful4neutral presented in axial (A) and coronal(B) view. Red¼negative correlation with TAS-20 total score, green¼negativecorrelation with TSIA-DDF score, yellow¼negative correlation with TSIA-DIF score;fwe-corrected significance level of p¼ .05 on cluster level; the pictures on the rightdisplay the locations of the slices; coordinates are in MNI space. (For interpretationof the references to color in this figure legend, the reader is referred to the webversion of this article.)


individuals show decreased processing in several brain areasinvolved in creating the holistic representation of a seen face.

First of all, analysis of brain activation revealed several regionsnegatively correlating with alexithymic features. Most strikingly, wefound decreased activations in the ventral striatum (putamen,pallidum, and caudate). These activations survived fwe-correctionon cluster level related to TAS-20-DDF, TAS-20 total score and TSIA-DDF during labeling of negative or threatening (i.e. angry and/orfearful) expressions. This suggests that striatal activation plays animportant role in the difficulties of alexithymic individuals regard-ing the labeling of negative facial emotions. The ventral part of thestriatum shows strong connections to areas related to emotionalprocessing such as the amygdala, orbito-frontal cortex, inferiorfrontal areas, anterior cingulate cortex, and insula (Cohen et al.,2009; Di Martino et al., 2008). Decreased activity in the ventralstriatum may be linked to decreased transfer of emotional informa-tion within the limbic system. It has been proposed that the basalganglia are specifically involved in the processing of disgust (Gorno-Tempini et al., 2001; Sprengelmeyer et al., 1998). However, recentlesion studies (Calder et al., 2004) and research on patients withHuntington’s disease (Calder et al., 2010; Trinkler et al., 2013) – aneurodegenerative disease that primarily affects striatal areas atfirst – have shown deficits in the labeling of facial emotions otherthan disgust, especially anger and fear. According to Heberlein andAdolphs (2007), the basal ganglia are, in interplay with otherstructures, involved in linking the perceived stimulus to represen-tations of emotional reactions (including expressions). Interestingly,there is evidence from other neuroimaging studies that alexithymicfeatures could be related to an altered function or structure of thestriatum. Lee et al. (2011) found that TAS-20-DDF predicteddecreased striatum response when viewing pictures of facial emo-tions. In a structural MRI study, Kubota et al. (2011) reported thatalexithymic individuals showed decreased gray matter volume inthe ventral striatum, which is assumed to be related to reductionsin the number of synapses, thus promoting a slowed or reducedinformation transfer (Anderson, 2011). In our view, low activation inthe ventral striatum could be associated with a decreased ability toform internal representations of others' facial expressions inalexithymia.

In addition to reduced activity in striatal areas, less neuronalactivation to negative faces with increasing alexithymic featureswas found in frontal regions such as orbito-frontal as well asinferior and superior frontal gyrus. Inferior frontal gyrus (inconjunction with insula and operculum) appears to be involvedin fast and covert simulations of others' facial expressions (Iriki,2006; Van der Gaag et al., 2007). Moreover, it has been argued thatactivity in orbito-frontal areas creates the same emotional experi-ence as seen in the face of the other through afferent and efferentconnections with the amygdala (Adolphs, 2002b). This “recreatingof feelings” within oneself facilitates the interpretation of theemotional expression of the other. Thus, a decreased activation inthese frontal areas may indicate less affective simulation and lessemotional experience with increasing alexithymia, so that readingout the emotion based on internal representations becomes harderfor high alexithymic individuals. Moreover, high alexithymicindividuals show decreased brain activation in the supplementarymotor area (SMA). SMA plays an important role in interpretingfacial expressions (Rochas et al., 2012) by being involved insimulating motor components (Van der Gaag et al., 2007). There-fore, processing in the SMA could contribute to the holisticrepresentation of the seen stimulus. Thus, reduced activation inthe SMA appears to be another contributing factor for a lessdetailed representation of the seen facial expressions (see alsoVan der Velde et al., 2013), which impedes attributing an emo-tional label to it for high alexithymic individuals. Interestingly,when faces are presented for a sufficiently long time, alexithymic

individuals rely rather on bodily features of the facial expressionsby showing increased neural processing in the SMA (and somato-sensory areas) when labeling negative facial expressions (Ihmeet al., 2014). However, this mechanism of overamplifying sensor-imotor and motor aspects of the facial expression (cf. Moriguchiand Komaki, 2013) apparently does not work when the faces arepresented for a very short time leading to a decline in performanceof high alexithymic individuals.

In addition, decreased brain activation with increasing alex-ithymia was revealed in middle and superior temporal gyrus aswell as middle occipital and fusiform gyrus when labeling negativefaces. According to Adolphs' model of facial emotion processing,these areas implicated in detailed visual analysis of the face(Haxby et al., 2000; Kanwisher et al., 1997; Kanwisher and Yovel,2006) receive information about the emotional significance of theseen faces through reciprocal connections with basic limbic areas(amygdalae and basal ganglia) (Adolphs, 2002a, 2002b). This earlyemotional processing seems to be disrupted in alexithymia asshown by reduced activation and gray matter volume in thestriatum (see above, Kubota et al., 2011; Lee et al., 2011) and theamygdala (Ihme et al., 2013; Kugel et al., 2008; Reker et al., 2010).In sum, these findings could indicate that alexithymic individualsexhibit less visual processing when confronted with briefly pre-sented negative faces and therefore need more time to fullyanalyze the seen face (cf. Grynberg et al., 2012).

The conclusion that we can draw from our findings is thatalexithymic individuals have difficulties to construct a holisticrepresentations of facial expressions when these are only brieflypresented. This is related to a decreased processing of affective,motor and sensory features of the facial expressions and leads toreduced performance. However, when the faces are displayed forseveral seconds (Ihme et al., 2014), alexithymic individuals seem tobe able to create representations of facial expressions mainlybased on motor and somatosensory (instead of affective) informa-tion. Under these circumstances they are able to correctly decodeand name emotional facial expressions.

4.4. Emotion specificity

The present behavioral results point towards a global deficit infacial emotion recognition in alexithymia. This is line with thefindings of a recent review (Grynberg et al., 2012) that suggest ageneral deficit of alexithymic individuals in identifying facialemotions. With respect to the neural correlates, evidence fordecreased brain activity in several regions was revealed fornegative (i.e., angry and fearful) facial expressions. Interestingly,we recently also found relations between alexithymic features andbrain activation only for angry and fearful, but not for happy faces(Ihme et al., 2014). There is evidence that the recognition of happyfaces is easier than that of angry or fearful faces (Calvo andLundqvist, 2008; see also the labeling performance in the presentstudy). An easier task might require less mental processing andrecruit less neural resources, so that it could be more difficult todetect associations between brain activation and alexithymicfeatures. However, in our study alexithymia was found to benegatively correlated with labeling performance of happy faces.Thus, future work is needed to investigate whether alexithymicindividuals have global deficits in labeling emotions or whetherthey have specific problems with the perception and interpreta-tion of negative or threatening emotions.

4.5. Assessment of alexithymia

To our knowledge this is one of the first neuroimaging studiesassessing alexithymia not only with a self-report measure, butalso with the observer-rated Toronto Structured Interview


for Alexithymia. The correlation of r¼ .45 between self-report(TAS-20) and observer-rated (TSIA) measure of alexithymia inthe current study is comparable with correlations found in otherstudies in which TAS-20 and TSIA have been administered. Bagbyet al. (2006), for example, reported a correlation of r¼ .36 innormal participants, whereas correlation coefficients of r¼ .34(Inslegers et al., 2013), r¼ .49 (Grabe et al., 2009), or r¼47(Meganck et al., 2011) have been observed in patient samples.Caretti et al. (2011) reported a correlation of r¼ .44 in a mixedsample of patients and healthy controls. Against this background,the correlation of r¼ .45 revealed in our study is not exceptionallylow. According to Bagby et al. (2006) it is not unusual for differentmethods of assessment to attenuate validity coefficients betweenmeasures of the same construct. Another reason for the relativelylow convergence of TSIA and TAS-20 may be that the TSIA containsa subscale for the facet imaginal processes, which is not includedin the TAS-20.

In our study, no relationship between alexithymic features asmeasured with the observer-based method (TSIA) and perfor-mance was revealed. This is somewhat surprising as a recentstudy from our laboratory (Ihme et al., 2014) could reveal acorrelation between alexithymia and behavioral effects (i.e.,response latency) during the labeling of facial stimuli presentedfor a long time only for the DDF scale of the TSIA but not for theDDF scale of the TAS-20. According to the present results itappears that young and well educated individuals are able toreliably judge their own difficulties in recognizing and verbalizingemotions in a self-report questionnaire (see also Parker et al.,2005). It can be speculated that completing a questionnaire (theTAS-20) in a rather short period of 5 min relates more to fastlabeling judgments while deliberate answers in an interviewwhich takes up to 90 min are more linked to processing of picturespresented for a longer time. Another reason for the higherpredictive power of the TAS-20 compared to the TSIA in thepresent study may be the fact that most of our participants hadrelatively low scores of alexithymia. The TSIA could be worse indifferentiating between subjects with low levels of alexithymiadue to a less fine-grained scale (3-point-Likert-scale compared tothe 5-point scale of the TAS-20). The TSIA values are relatively low(especially for the facets DDF and DIF) and standard deviations aresomewhat lower for TSIA-DDF and TSIA-DIF compared to TAS-20DDF and TAS-20 DIF. In our opinion, the TSIA and its subscalescould be a better predictor in samples including highly alexithymicindividuals compared to the TAS-20 as these persons are possiblyless able to correctly assess their own difficulties in describing anddifferentiating feelings.

As a limitation, it has to be mentioned that the TAS-20 and theTSIA are thought to measure solely cognitive alexithymia, whileneglecting affective aspects of the alexithymia construct (Bermondet al., 2007, but see Bagby et al., 2009). In future studies theBermond–Vorst–Alexithymia Questionnaire could be administeredto assess the affective components of alexithymia (Vorst andBermond, 2001). However, the results of a recent study suggestthat the cognitive dimension of the alexithymia construct is abetter predictor for brain activation related to the perception ofemotional signals (Van der Velde et al., 2014).

5. Conclusions

To sum up, alexithymic tendencies are related to deficits inlabeling briefly presented facial expressions. These performancedeficits are accompanied by less neural activation in the ventralstriatum, frontal cortex, and supplementary motor areas as well asoccipital and temporal regions. All of these brain areas seem to beimplicated in forming a holistic representation of other persons'

emotional state by combining affective, sensory and motor proces-sing. Our results suggest that alexithymic individuals may be lessable to create an adequate representation of the emotional state ofother persons under temporal constraints, which hampers a fastinterpretation and labeling of the facial expressions.

Funding

This work was supported by grants from the German ResearchFoundation DFG to Thomas Suslow and Harald Kugel (SU 222/6-1).

Acknowledgments

We thank Sophie-Luise Lenk and Marc Rupietta for their help indata collection. The authors of the present manuscript declare thatthey have no competing (financial or non-financial) interests.

Appendix A. Supporting information

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.neuropsychologia.2014.09.044.

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39

2.3 Study 3: Alexithymia and the labeling of facial emotions: response slowing and

increased motor and somatosensory processing (Ihme et al., 2014b)

Similar to Study 2, Study 3 was accomplished in the project supported by a grant of the German research

foundation to Thomas Suslow and Harald Kugel (grant number SU 222/6‐1). Please note that the

supplementary data of Study 3 can be found in the Appendix on page 79.

Ihme et al. BMC Neuroscience 2014, 15:40http://www.biomedcentral.com/1471-2202/15/40

RESEARCH ARTICLE Open Access

Alexithymia and the labeling of facial emotions:response slowing and increased motor andsomatosensory processingKlas Ihme1, Julia Sacher2,3, Vladimir Lichev1, Nicole Rosenberg1, Harald Kugel4, Michael Rufer5,Hans-Jörgen Grabe6,7, André Pampel8, Jöran Lepsien8, Anette Kersting1, Arno Villringer2,3 and Thomas Suslow1,9*

Abstract

Background: Alexithymia is a personality trait that is characterized by difficulties in identifying and describingfeelings. Previous studies have shown that alexithymia is related to problems in recognizing others’ emotional facialexpressions when these are presented with temporal constraints. These problems can be less severe when theexpressions are visible for a relatively long time. Because the neural correlates of these recognition deficits are stillrelatively unexplored, we investigated the labeling of facial emotions and brain responses to facial emotions as afunction of alexithymia.

Results: Forty-eight healthy participants had to label the emotional expression (angry, fearful, happy, or neutral) offaces presented for 1 or 3 seconds in a forced-choice format while undergoing functional magnetic resonanceimaging. The participants’ level of alexithymia was assessed using self-report and interview. In light of the previousfindings, we focused our analysis on the alexithymia component of difficulties in describing feelings. Difficultiesdescribing feelings, as assessed by the interview, were associated with increased reaction times for negative(i.e., angry and fearful) faces, but not with labeling accuracy. Moreover, individuals with higher alexithymia showedincreased brain activation in the somatosensory cortex and supplementary motor area (SMA) in response to angry andfearful faces. These cortical areas are known to be involved in the simulation of the bodily (motor and somatosensory)components of facial emotions.

Conclusion: The present data indicate that alexithymic individuals may use information related to bodily actions ratherthan affective states to understand the facial expressions of other persons.

Keywords: Alexithymia, Supplementary motor area, Somatosensory cortex, Facial emotion, Labeling, Torontostructured interview for Alexithymia

BackgroundUnderstanding the emotional expression of another personis thought to require mimicry or simulation of others’ facialexpressions [1,2]. Thus, it is likely that neural assembliesexist that are active both when a person is experiencing andexpressing an emotion and when the same person is seeingand interpreting the facial emotions of somebody else [3,4].Recent evidence indicates that interpreting facial expres-sions is a multi-faceted endeavor that requires recruiting a

* Correspondence: [email protected] of Psychosomatic Medicine and Psychotherapy, University ofLeipzig, Semmelweisstrasse 10, 04103 Leipzig, Germany9Department of Psychiatry, University of Münster, Münster, GermanyFull list of author information is available at the end of the article

© 2014 Ihme et al.; licensee BioMed Central LCommons Attribution License (http://creativecreproduction in any medium, provided the orDedication waiver (http://creativecommons.orunless otherwise stated.

multitude of cortical and subcortical circuits, such as thevisual system (e.g., occipital gyrus, fusiform gyrus [FFG]), toprocess the visual information of the face, the motor systemfor the (covert) physical simulation of the facial movement(supplementary motor area [SMA] or premotor cortex),somatosensory areas for proprioceptive feedback (primarysomatosensory cortex, insula) and limbic or frontal re-gions for reenacting and feeling the according emotion(striatum, ventromedial pre-frontal cortex [vmPFC],amygdala [AMG]) [3-8].A personality trait that is related to difficulties in the

recognition of emotional facial expression is alexithymia(literally translated as “no words for emotion”). Alexithymia

td. This is an Open Access article distributed under the terms of the Creativeommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andiginal work is properly credited. The Creative Commons Public Domaing/publicdomain/zero/1.0/) applies to the data made available in this article,


http://creativecommons.org/licenses/by/2.0

http://creativecommons.org/publicdomain/zero/1.0/

Ihme et al. BMC Neuroscience 2014, 15:40 Page 2 of 10http://www.biomedcentral.com/1471-2202/15/40

is characterized by deficits in identifying and describingone’s feelings [9]. Alexithymic features can be assessedusing the 20-item self-reported Toronto Alexithymia Scale(TAS-20, [10]) or the Toronto Structured Interview forAlexithymia (TSIA, [11]). Both measures of alexithymiainclude the subscales Difficulties Describing Feelings(DDF), Difficulties Identifying Feelings and ExternallyOriented Thinking (the TSIA additionally includesimaginal processing).It has been repeatedly shown that alexithymia is asso-

ciated with a decreased ability to identify the facialexpressions of others, especially when these expressionsare presented under temporal constraints [12-14]. Inter-estingly, a recent electromyographic (EMG) study demon-strated that highly alexithymic individuals exhibit less facialmimicry when confronted with emotional faces [15]. Thiscould mean that individuals who are high in alexithymiahave difficulties in interpreting the emotions of others be-cause they automatically simulate others’ facial expressionsto a lesser degree and therefore lack the capability to fullycapture the other person’s feelings.On the contrary, when the presentation time is increased,

most studies did not reveal a relationship between the de-gree of alexithymia and recognition accuracy for emotionalfacial expressions (e.g., [12,16,17]). So far, only one study[16] has investigated brain activation related to facial emo-tion labeling, as assessed with longer presentation times(3.75 s) and as a function of alexithymia. No differences asa function of alexithymia could be found. However, theauthors studied only 23 participants in a correlational ap-proach. Yarkoni and Braver instead proposed the use of atleast 40 participants for a correlational analysis in neuroim-aging research [18]. In addition, alexithymic tendencies wereonly assessed through self-report, although a multi-methodapproach is recommended [19-21]. Moreover, behavioralevidence [12] suggests that DDF, as opposed to the TAS-20total score, is most predictive for facial emotion recognition.Thus, the current study investigated the labeling of facialemotions and brain responses to facial emotions as a func-tion of DDF (as measured with TAS-20 and TSIA) usingfunctional magnetic resonance imaging (fMRI). Because ourdesign includes a relatively long response window after thepresentation of the facial stimuli, we hypothesized that DDFwould have an adverse effect on response latencies but notrecognition accuracy.

MethodsParticipantsFifty-two healthy young German native speakers (age range:18 to 29 yrs) participated in the study. All of them wereright-handed and had normal or corrected-to-normal visualacuity. None of the participants had any history of neuro-logical or psychiatric illnesses or contraindications formagnetic resonance imaging. All participants gave written

consent to participate and received financial compensa-tion for their participation. The study procedure was ap-proved by the ethics committee of the Medical Schoolof the University of Leipzig and was in accordance withthe Declaration of Helsinki. Four participants had tobe excluded from data analysis (one participant had adepression score of BDI > 14 at time of scanning, onesubject displayed excessive head motions in the magneticresonance imaging (MRI) scanner (>3 mm translation)and two participants demonstrated erroneous reactionsand responded before the intended time window). Thus,48 participants (23 female, age 24 ± 3 yrs, mean ± SD)entered final analysis.

Assessment of alexithymia and control variablesAlexithymic tendencies were measured using a ques-tionnaire, the TAS-20 (German version: [22]), and anobserver-rated measure, the TSIA (German version: [23]).The complete TSIA was administered by one trainedinterviewer and rated during the interview according tothe manual. Before the study, the interviewer was trainedto conduct and score the TSIA by the translators of theGerman version of the TSIA (coauthors MR and HG). Thisincluded becoming familiar with the alexithymia construct,the manual outlining administration and the scoring proce-dures for the TSIA, as well as discussion of the guidelines,the scoring of the items and the correct use of the promptsand probes. Moreover, test interviews were supervised untilthe interviewer was secure in the solo administration andscoring of the interview. Our analysis was focused on onesubscale, DDF, of the TAS-20 and TSIA. This subscale con-sists of five items in the TAS-20 and six items in the TSIA,respectively. To control for depressive symptoms, anxietyand affectivity, participants also completed the BeckDepression Inventory (German version: [24]), the State-Trait-Anxiety Inventory (German Version: [25]) and thePositive and Negative Affect Schedule (German Version:[26]) trait version.

Task and designThe participants’ task was to label the facial emotion ofa target face. Facial stimuli were color photographs takenfrom the Karolinska Directed Emotional Face database[27] depicting four different emotions (happy – HA,angry – AN, fearful – FE, and neutral – NE). Pictures oftwenty different individuals (ten females) were shown ineach of the four emotional conditions, consisting of 80trials in total. Each trial lasted for 9 s, initiated by thepresentation of a fixation cross in the center of the screenfor 800 ms. In the first 40 trials of the experiment, thetarget was shown for 1 s; in the second half of theexperiment, the target presentation time was set to 3 s.After presentation of the target, participants had 7.2 (5.2)s to label the emotions by pressing a button. Participants


had one response pad per hand with two buttons each andprovided their responses with their index and middle fin-gers. Each emotion was attributed to one button during theentire experiment counterbalanced across participants.During the response window, participants saw the fouroptions in the order of button attribution, e.g., the labelon the left side on the screen matched the most left but-ton (i.e., left middle finger). After pressing a button, thelabels vanished and only a gray screen was visible until thenext trial started with the presentation of the fixationcross. Participants were instructed to answer as correctlyas possible within the given time frame and were aware ofthe fact that the response window was shorter in the sec-ond half of the experiment. Trials were shown in two fixedrandom sequences with the constraints that no two subse-quent trials depict the same person and that no more thantwo subsequent trials show the same emotion.

MRI acquisition and preprocessingStructural and functional MR images were obtained on a 3 Tscanner (Magnetom Verio, Siemens, Erlangen, Germany).For each participant, structural images were acquired with aT1-weighted 3D MP-RAGE [28]. Magnetization preparationconsisted of a non-selective inversion pulse. The imagingparameters were as follows: TI 650 ms, TR 1300 ms,TE 3.5 ms, flip angle 10°, isotropic spatial resolution of1 mm3, two averages. Blood oxygen level dependent con-trast sensitive images were collected using T2*-weightedecho-planar imaging (EPI) sequence [matrix 642; resolution3 mm× 3 mm× 4 mm; gap 0.8 mm; TR 2 s; TE 30 ms; flipangle 90°; interleaved slice acquisition; 385 images]. Theslices were oriented parallel to a line through the posteriorand anterior commissures.MRI data were preprocessed and analyzed using SPM8

(http://www.fil.ion.ucl.ac.uk/spm/). The initial five functionalvolumes were discarded to allow longitudinal magnetizationto reach equilibrium. Functional volumes were slice-timecorrected (temporal middle slice as reference), realigned tothe first image and corrected for movement-induced imagedistortions (6-parameter rigid body affine realignment).The structural T1 images were coregistered to the meanfunctional EPI image (default in SPM). Anatomical imageswere segmented, including normalization to a standardstereotaxic space using the T1 MNI within SPM8. Thenormalization parameters were then applied to thefunctional EPI series. The resulting voxel size for thefunctional images was 3x3x3 mm3. A temporal high-passfilter (128 s) was applied to remove slow signal drifts. Forthe functional data, spatial smoothing was performed usinga three-dimensional Gaussian filter of 6 mm full-width athalf-maximum. We chose this rather small smoothingkernel such that the potential activation in subcorticalareas involved in facial emotion processing was stilldetectable and not washed out.

Data analysisLabeling accuracy was evaluated by the Grier sensitivityindex [29], which considers true and false positives. Theresulting values for this sensitivity index range from 0 to1, with a value of 1 meaning perfect performance and avalue of 0.5 referring to chance level. Due to the highaccuracy and thus lack of sufficient trials to reliably esti-mate error responses, incorrect trials were discardedprior to analysis of reaction time and fMRI data. Thedata were pooled across both presentation time condi-tions. Originally, we aimed to differentiate between thetwo temporal conditions (1 and 3 s), similar to the studyof Parker et al. [12]. However, the accuracy was at itsceiling (> .9) with little variance, such that we decidedto collapse across temporal conditions for analysis ofreaction time and fMRI data. The high recognition ratesin the current study compared to those of Parker et al.seem to be related to our long response window. Theparticipants in Parker et al.’s study had to respond whilethe picture was presented (1 or 3 s). Participants hadmore time to respond in the current study, most likelyresulting in higher accuracy. This is in line with the con-clusions of a recent review (Grynberg et al. [14]), whichwas published when the data collection for this studywas almost finished. Grynberg and colleagues concludedthat alexithymic individuals' difficulties in recognizing facialemotions are most prominent when the pictures are pre-sented for less than 300 ms. To investigate associationsbetween measures of alexithymia and labeling accuracy, aswell as RTs, correlational analyses were accomplished usingSpearman’s rho. Spearman’s rho was also used to checkfor associations between the measures of alexithymiaand affectivity questionnaires (BDI, STAI, and PANAS).We employed Spearman’s rho for correlational analysesbecause the RT and TSIA-DDF scores were not normallydistributed. All associations were tested against a signifi-cance threshold of p = .05 (two-tailed).The fMRI data were analyzed by modeling the onset

and duration of the presentation times of each facialexpression and by convolving these regressors with thehemodynamic response function for the different emo-tions. Incorrect trials were included in the first-level de-sign matrix as nuisance regressor. First level t-contrastswere calculated by contrasting each emotional conditionwith the neutral one (HA >NE, AN >NE, FE > NE). Thecontrast images for the first level contrasts were thentransferred to the second level models for the main effects(HA >NE, AN >NE and FE >NE) and regression modelswith TAS-20-DDF and TSIA-DDF as regressors. One sec-ond level model was calculated per alexithymia measure(TAS-20-DDF, TSIA-DDF) and experimental condition.For all models, significance was tested at the clusterlevel against a family-wise-error-corrected significancethreshold of p = .05 at an individual voxel threshold of


Table 1 Correlations (Spearman’s rho) between measuresof alexithymia

TAS-20 TAS-20-DDF TSIA TSIA-DDF

TAS-20 .85** .47* .57**

TAS-20-DDF .41* .55**

TSIA .81**

TSIA-DDF

*significant at p < .01 (two-tailed).**significant at p < .001 (two-tailed).Note. TAS-20 = 20-Item Toronto Alexithymia Scale, TSIA = Toronto StructuredInterview for Alexithymia; DDF: subscale Difficulties Describing Feelings.


t = 3.5. As advised in the literature [30], we also reportthe activations that would survive a more lenientthreshold (p = .001, k = 10) in the additional material toafford using these data in future meta-analyses.In a recent paper, Yarkoni and colleagues [31] argued that

the reaction times per second increase brain activation be-cause the time required for preparatory processes for motoractivation is increased. Thus, for contrasts yielding signifi-cant clusters, we checked whether adding the differencein RT between the two the conditions in that contrast(e.g., AN >NE) or the RT for the emotion only (e.g., AN)as nuisance covariates changed the results substantially.Although an association between behavior and TSIA-

DDF was revealed for angry and fearful faces, it was onlyreflected in significant brain activation related to TSIA-DDFin the contrast AN>NE, but not in FE >NE. For FE >NE,the effects on brain activation may be smaller and could thusnot be detected using a whole brain approach. Thus, weadditionally tested whether there was an association betweenTSIA-DDF and brain activation in these clusters in an ROI-based approach using small volume correction for FE >NE.For this, the significant clusters from the model testing for apositive correlation between TSIA-DDF and brain activationfor the contrast AN>NE were saved as a mask. These, inturn, were employed as an ROI to check for activations posi-tively correlating with TSIA-DDF in these brain areas.Finally, an exploratory analysis was conducted to check

whether our measures of alexithymia (TAS-20, TAS-20-DDF, TSIA, TSIA-DDF) displayed a relationship with brainactivations in ROIs, which, based on the previous literature,are associated with facial emotion processing. To estimatethe activation in these ROIs, the eigenvariates of the ac-tivation in these ROIs were extracted for the main con-trasts (i.e., HA > NE, AN > NE, FE > NE) using SPM8.The activations in these ROIs were then related to themeasures of alexithymia by employing Spearman’s rho.We decided to employ the following ROIs: amygdala(AMG), ventro-medial pre-frontal gyrus (vmPFC), fusiformgyrus (FFG) and striatum. The masks for AMG, FFGand striatum were defined using the automated anatom-ical labeling toolbox [32] as implemented in the WFUPick Atlas [33] using SPM8. However, this tool did notinclude a reasonable mask for the vmPFC, so we definedthis region as a sphere of 20 mm around the MNI coor-dinates xyz = [0 50–2]. These coordinates were based onthe results of a study by Pessoa et al. on facial emotionprocessing [34]. We also decided to include the clusters(SMA, right S1) positively correlating with TSIA-DDFin the contrast AN > NE as further ROIs.

ResultsAlexithymia measures and control variablesThe mean scores for the alexithymia subscales were 12.4 ±4.6 (mean ± standard deviation) for the TAS-20-DDF and

2.9 ± 3.4 for the TSIA-DDF. The TAS-20 total score was43.0 ± 10.7, and the TSIA total score was 16.9 ± 9.9. Internalconsistencies for TAS-20-DDF (Cronbach’s α = .87) andTSIA-DDF (α = .90) were sufficiently high. All measuresof alexithymia were significantly correlated with eachother (see Table 1). There was no correlation betweenTAS-20-DDF and depression as assessed by the BDI[35], trait-anxiety as measured by the STAI [36], or posi-tive and negative affect as assessed by the PANAS [37](all ps > .05). TSIA-DDF was not related to BDI or toSTAI and PANAS negative (all ps > .05), but there was anegative correlation between TSIA-DDF and PANASpositive (rho = −.33, p < .05). There was a correlationbetween STAI and BDI (rho = .49, p < .005).

Behavioral dataLabeling accuracy was above 0.9 for all facial emotionconditions (happy [HA]: .99, neutral [NE]; .97; angry[AN]: .96; fearful [FE]: .96), and there was no relationshipbetween TAS-20-DDF or TSIA-DDF and performance asmeasured using the Grier sensitivity index [29] in anyof the emotional conditions (all ps > .1). The fastestreaction times were revealed for happy faces, and theslowest reaction times for fearful faces (HA: .73 s, NE: .96 s,AN: 1.06 s, FE: 1.17 s; F(3,141) = 36.4, p < .01; post-hocs:HA <NE =AN< FE). TAS-20-DDF did not correlate withreaction time (RT) in any condition (all ps > .1). However,there was a positive correlation between TSIA-DDF andRT for angry (rho = .30, p < .05) and fearful faces (rho = .31,p < .05), but no correlation was observed between TSIA-DDF and RT for happy (rho =−.01, p = .47) and neutralfaces (rho = .07, p = .32) (see Table 2).

fMRI dataMain effectsHappy versus neutral faces elicited significant brain acti-vation in clusters in the left middle occipital gyrus ex-tending to the middle temporal gyrus, in the left middleorbital gyrus extending to both the superior frontalgyrus and the bilateral anterior cingulate gyrus, and acluster in the middle frontal gyrus extending to the su-perior frontal gyrus. In the contrast AN >NE, significant

Table 2 Correlations (Spearman’s rho) between difficultiesdescribing feelings (as assessed by TAS-20-DDF andTSIA-DDF) and reaction times in the four facialexpression conditions

Reaction times

Happy Neutral Angry Fearful

TAS-20-DDF rho .06 -.04 .15 .11

p 34 .41 .15 .22

TSIA-DDF rho -.01 .07 .30* .31*

p .47 .32 .02 .02

*significant at p < .05 (two-tailed).Note. TAS-20 = 20-Item Toronto Alexithymia Scale, TSIA = Toronto StructuredInterview for Alexithymia; DDF: subscale Difficulties Describing Feelings.


clusters were revealed in the right fusiform gyrus, theright inferior occipital gyrus extending to middle occipitaland lingual gyrus, the left fusiform gyrus extending to infer-ior temporal gyrus and the left middle occipital gyrus. Thecontrast FE >NE activated the left inferior frontal gyrus,left fusiform gyrus extending to inferior occipital gyrus, leftmiddle temporal gyrus, right inferior occipital gyrusand right cerebellar structures (lobule VIIb and VIIa).An overview of the results is presented in Table 3. Theactivations for the main contrasts are presented at a morelenient threshold (p = .001, k = 10) in the Additional file 1:Table S1.

Relationships between brain activation and measuresof alexithymiaA significant cluster positively correlating with TSIA-DDFin the contrast AN >NE was revealed in the supplemen-tary motor area (SMA) (Montreal Neurological Institute[MNI] coordinates xyz = [−6 -1 61], cluster extent k = 64,

Table 3 Significant brain activations for all fMRI main contras

Cluster Peak

k pfwe x y z Z pfwe

HA > NE 1 361 <.001 −42 −76 31 5.10 <.01

2 461 <.001 −6 53 2 4.99 .01

3 54 <.05 −30 26 49 4.43 .16

AN > NE 1 98 <.01 42 −49 −14 4.63 .05

2 81 <.01 36 −91 4 4.98 .11

3 121 <.001 −39 −73 −8 4.40 .13

4 48 <.05 −30 −94 7 3.92 .52

FE > NE 1 615 <.001 −45 14 22 5.93 <.001

2 167 <.001 36 −91 2 5.58 <.001

3 292 <.001 −36 −73 −8 5.03 <.01

4 106 <.01 −57 −55 7 4.62 .05

5 102 <.01 15 −79 −35 4.58 .06

Note. Only clusters that are significant on cluster level (pfwe < 0.05) at an individualwhich the cluster is spanning. HA > NE = happy versus neutral faces, AN >NE = angry vand z are in MNI space.

pcluster = .013). The peak of this cluster lay in SMA proper,but the activity clearly extended more rostrally to pre-SMA.A second cluster was found in the post-central gyrus(xyz = [30–37 40], pcluster = .039, k = 47) (see Figure 1),which could be attributed to the right primary somatosen-sory (S1) cortex. No significant clusters were revealed re-lated to TAS-20-DDF or any other contrast for TSIA-DDF.Because TSIA-DDF was negatively correlated with thepositive affect, we checked whether entering the PANASpositive score as a nuisance covariate into the model af-fected the results. We found a small change for the clusterin the SMA (xyz = [−6 -1 61], k = 39, pcluster = .089) and adecrease in cluster size in the somatosensory cortex (xyz =[30–37 40], k = 14, pcluster = .45, ppeak-uncorrected < .0001).Entering the difference between the reaction times for

AN and NE as nuisance covariates into our second-levelmodel slightly changed the results (SMA: xyz = [3 2 61],k = 43, pcluster = .05; somatosensory cortex: xyz = [30–37 40],k = 47, pcluster = .038). Similarly, using only the reaction timein the angry condition as a covariate led to small changes inthe results (SMA: xyz = [3 2 61], k = 37, pcluster = .08; som-atosensory cortex: xyz = [30–37 40], k = 45, pcluster = .046).Thus, our findings are highly likely to mainly reflect differ-ences due to alexithymia (TSIA-DDF) and cannot be attrib-uted to (differences in) the reaction time. The activationsfor the models related to the measures of alexithymia arepresented at a more lenient threshold (p = .001, k = 10) inthe Additional file 2: Table S2.

Post-hoc analysis of activation in SMA and S1 positivelycorrelating with TSIA-DDF for contrast FE > NEA post-hoc region of interest (ROI) analysis revealed asignificant small-volume-corrected (SVC) peak voxel

ts

Localization

hem. Region

Left Middle occipital gyrus, middle temporal gyrus

Left Middle orbital gyrus, superior frontal gyrus, superiormedial gyrus, anterior cingulate gyrus

Left Middle frontal gyrus, superior frontal gyrus

Right Fusiform gyrus

Right Inferior occipital gyrus, middle occipital gyrus, lingual gyrus

Left Fusiform gyrus, inferior temporal gyrus

Left Middle occipital gyrus

Left Inferior frontal gyrus, pars triangularis

Right Inferior occipital gyrus

Left Fusiform gyrus, inferior occipital gyrus

Left Middle temporal gyrus

Right Cerebellum, lobule VIIb

voxel threshold of t = 3.5 are reported. The region refers to brain areas throughersus neutral faces, FE > NE = fearful versus neutral faces, hem. = hemisphere, x, y

Figure 1 Activation in the right postcentral gyrus (A) and supplementary motor area (B) in response to angry (vs. neutral) faces(AN > NE) positively correlating with TSIA-DDF.


activation in the SMA (xyz = [−9 -4 61], pSVC = .019)and a marginally significant peak voxel activation in S1(xyz = [30–37 40], pSVC = .069) positively correlatingwith TSIA-DDF in the contrast FE > NE. The activationin SMA remained marginally significant when control-ling for PANAS positive affect (pSVC = .061) and signifi-cant when entering the difference in RT between FEand NE (pSVC = .032), or RT in FE alone (pSVC = .034).Similarly, the significance of the activation in S1 changedonly slightly when controlling for PANAS (pSVC = .084),the difference in RT (pSVC = .052) or the reaction time forFE (pSVC = .059).

Exploratory analysis of correlations between brain regionsrelevant for emotion processing and measures of alexithymiaThe results of our exploratory analysis considering associa-tions between measures of alexithymia and brain activity inthe AMG, FFG, vmPFC, striatum, SMA and S1 are dis-played in Figure 2. Descriptively, our measures of alexithy-mia are rather positively related to activation in S1 andSMA and show no or negative correlative trends withAMG and vmPFC. These relationships between alexithymiaand FFG as well as striatum depend on the contrast, andno consistent pattern emerges. When thresholding the plotat p = .05 (two-tailed), SMA seems to be strongly associatedwith TSIA-DDF (all contrasts), while S1 is related to TSIA-DDF (AN >NE, FE >NE), TSIA and TAS-20 (AN>NE) inthe conditions with negative emotions. For the contrastHA > NE, the activity in vmPFC is negatively related toTAS-20-DDF and TSIA-DDF. Moreover, FFG activityseems to be positively related to TAS-20 in both negativeconditions. For AN >NE, activation in the left striatumseems to be positively related to TSIA-DDF. However, it

has to be noted that the correlations between TSIA-DDFand SMA and S1 for AN >NE are likely to be an overesti-mation of the real correlations in these areas because weextracted a cluster using a mask defined by voxels thatpositively correlated with TSIA-DDF in that very contrast(cf. [38,39]). Thus, these correlations are only presentedhere in an exploratory and descriptive fashion.

DiscussionThis study investigated the effects of self-report (TAS-20-DDF) and observer-rated (TSIA-DDF) facets of alexithymiaon the labeling and neural processing of facial emotionspresented for a rather long time (1 or 3 seconds). Our ana-lysis of the main contrasts revealed significant clusters ofbrain activation in the fusiform gyrus, inferior and middleoccipital gyrus (all conditions), in the middle temporalgyrus (fearful faces), inferior (fearful) and orbital and medial(happy) frontal gyrus as well as the cerebellum. All of theseregions have been reported to be implicated in facial emo-tion processing (e.g.: [7,8,40-42]). Thus, we can assume thatour experimental design is suitable for eliciting brain activa-tion related to facial emotion recognition. Considering thespecific effects of alexithymia, we found that high TSIA-DDF scores were related to increased reaction times whenlabeling angry and fearful faces and to increased brainactivation in SMA and right S1 during the recognitionof these negative faces. A post-hoc exploratory analysissuggests that activity in brain areas that are importantin the affective components of facial emotion processing(AMG, vmPFC, striatum) does not show a particularrelationship with alexithymia in the current task.Their increased reaction times indicate that alexithymic

individuals were slower in labeling negative emotions.

Figure 2 Relationship (as calculated with Spearman’s rho) between measures of alexithymia and brain activations in regions-of-interest that are relevant for facial emotion processing. The left column (A,C,E) depicts the magnitude of rho coded by color; in the rightcolumn (B,D,F), rho is only presented as different from zero if the according p < .05. Each row is related to one contrast: HA > NE (happy > neutral) inA and B. AN > NE (angry > neutral) in C and D. FE > NE (fearful > neutral) in E and F. l = left, r = right; AMG = amygdala, FFG = fusiform gyrus,vmPFC = ventro-medial pre-frontal cortex, STR = striatum, SMA = supplementary motor area, S1 = primary sensory cortex.


Highly alexithymic individuals appear to need moretime to reach a labeling accuracy level similar to subjectswith low alexithymia. In contrast to previous studiesdescribing a relationship between accuracy and degreeof alexithymia [12,13], we used relatively long stimuluspresentation times and response windows and couldnot reveal interrelationships between alexithymia andrecognition accuracy. Thus, it seems that alexithymicindividuals have difficulties in recognizing facial ex-pressions, which are reflected in decreased accuracywhen presentation times and response windows are short(see also [14]). Prolonging presentation times and responsewindows could improve recognition accuracy, however, atthe cost of increases in response time.SMA is part of a brain network that is involved in the

processing of motor-related information and motor prepar-ation and has been shown to be involved in the productionof facial emotions [43]. Moreover, it has been argued that(especially pre-) SMA is involved in the recognition of facialemotions [44] by playing an important role in the motorcomponents of simulation (see also [6]). Additionally,a cluster in S1 was revealed, which seems to reflectsomatosensory aspects of facial emotion processing[3,7,45]. According to Adolphs et al. [46], recognizingemotions from facial expressions requires right primarysomatosensory areas. The authors argue that recognition ofanother individual’s emotional state is mediated by intern-ally generated somatosensory representations that simu-late how the other individual would feel when displaying a

certain facial expression. Taken together, this mediationcould mean that highly alexithymic individuals have diffi-culties in automatically reenacting the negative facial emo-tion of others when these are presented briefly [15]. Whenthe presentation time is increased, highly alexithymic indi-viduals can reach a similar performance as less alexithymicindividuals, which seem to require an increased activationof motor and somatosensory areas. Interestingly, it has beenfound that highly (as compared to less) alexithymic individ-uals also show increased activation in motor-related brainareas when interpreting the directed actions of others ina classical mirror-neuron task and show no differencesin interpreting these actions [47]. Thus, highly alexithymicindividuals may be more inclined to imitate the actionsof others via (covert) motor simulation than are non-alexithymics. A recent meta-analysis by van der Veldeet al. [48] reported that high levels of alexithymia arerelated to decreased activity in the SMA when partici-pants are confronted with negative stimuli. However,this meta-analysis included all types of emotional para-digms and tasks (not only facial emotion recognition), sothe published results may not necessarily reflect processesrelated specifically to facial emotion recognition.There seems to be no particular relationship between

activity in the amygdala, vmPFC and ventral striatum andalexithymia in the task studied here. This finding is very in-teresting because earlier studies on brain function [49-52]and structure [53] reported alterations in highly alexithymicindividuals in these regions. In particular, functional studies


on automatic processing of emotional faces (affectivepriming) [49-51] have revealed decreased activations inthese brain areas. The lack of involvement in the currenttask may be the case because the emotional faces werepresented for a rather long time in the current study. Theamygdala and the ventral striatum, however, are thoughtto operate in a fast and automatic fashion and may be lessrelevant when the participants are fully aware of the emo-tional nature of the faces (e.g., [54,55]), as in the currentstudy. Thus, it seems that alexithymic individuals showless automatic activation in brain regions particularlyinvolved in the affective components of face processing(AMG, ventral striatum, vmPFC), which most likely leadsto alterations in the processing of and difficulties in thelabeling of briefly presented faces. However, alexithymicindividuals seem to be able to simulate the bodily aspectsof facial expressions when the presentation times and re-sponse windows are long enough, which makes the correctrecognition of faces possible in this case.Our study points to deficits limited to the recognition

of negative faces in alexithymia. Neither behavioral norneurobiological differences were revealed for happyfaces. This finding suggests that alexithymics have fewerproblems interpreting positive compared to negative facialexpressions. A recent review on alexithymia and the pro-cessing of emotional facial expressions concluded that thedifficulties of alexithymic individuals in processing facialemotions are not specific to certain emotions [14]. Thework of Sonnby-Borgström [15] shows that the imitationof facial expressions (measured with facial EMG) in highlyalexithymic individuals was only decreased for corru-gator activity related to negative emotions, but not forzygomaticus activity related to happy faces. Againstthis background, alexithymic individuals may displayfewer deficits in automatically simulating happy facescompared to neutral ones, which possibly renders therecognition of happy faces easier.It is important to note that in our study, the objective

measure of alexithymia (TSIA), but not the self-reportmeasure (TAS-20), was predictive for recognition per-formance. Because some alexithymic individuals maynot be aware of their own deficits, self-report tests couldbe less suitable for measuring difficulties in describingfeelings compared to objective tests such as the TSIA.It has been argued that the TAS-20 and the TSIA only

measure cognitive aspects of alexithymia and neglectaffective parts of the alexithymia construct [56]. A ques-tionnaire that possibly captures these affective compo-nents is the Bermond-Vorst-Alexithymia Questionnaire([57], but see also [58]). It is possible that additionallyapplying this measure of alexithymia may have the potentialto discover relationships between the brain areas involvedin the affective components of emotional face processing.Future studies need to be conducted to determine whether

the results of the current study are only related to cog-nitive alexithymia or whether they generalize to affectivealexithymia as well.

ConclusionIn summary, alexithymic individuals have difficulties inlabeling facial expressions of emotion, even when these arepresented with little temporal constraints. Such individualsare slowed in their labeling of angry and fearful facial emo-tions, and they manifest increased activation in the somato-sensory and supplementary motor cortex in response tothese negative faces. These cortical regions are involved inthe simulation of the bodily components of facial emotionalexpressions. Thus, the present data suggest that alexithymicindividuals may recruit cortical processing resources thatare involved in the simulation of the bodily componentsrather than of affective states (angry and fearful) to interpretthese facial expressions.

Additional files

Additional file 1: Table S1. Brain activation in the three main contrastsat a threshold of t = 3.27, k = 10.

Additional file 2: Table S2. Brain activation related to measures ofalexithymia in the three contrasts at a threshold of t = 3.27, k = 10.

AbbreviationsAMG: Amygdala; AN: Experimental condition with angry faces; BDI: Beckdepression inventory; DDF: Difficulties describing feelings (subscale ofTAS-20 and TSIA); EMG: Electromyography; EPI: Echo planar imaging;FE: Experimental condition with fearful faces; FFG: Fusiform gyrus; (f)MRI: (functional) magnetic resonance imaging; HA: Experimental conditionwith happy faces; MNI: Montreal neurological institute; NE: Experimentalcondition with neutral faces; PANAS: Positive and Negative affect schedule;ROI: Region of interest; RT: Reaction time; S1: Primary somatosensory cortex;SMA: Supplementary motor area; STAI: State-trait-anxiety inventory;SVC: Small volume corrected; TAS-20: 20-Item Toronto Alexithymia scale;TSIA: Toronto structured interview for Alexithymia; vmPFC: Ventro-medialprefrontal cortex.

Competing interestThe authors declare that they have no competing interests.

Authors’ contributionsKI, JS, VL, NR, HK, MR, HG, AP, JL, AK, AV, TS designed the study; MR, HGsupervised the alexithymia interviews; KI, VL, NR, TS conducted thepsychometric testing of the participants; JS, HK, AP, JL, AV prepared the fMRIsequences; KI, JS, VL, NR, TS, AP run the fMRI experiments; KI, VL, NR, MR, HG,TS analyzed the psychometric data; KI, JS, TS analyzed the fMRI data; KI, JS,VL, NR, HK, MR, HG, AP, JL, AK, AV, TS interpreted the data; KI, JS, AK, TSwrote the manuscript. All authors read and approved the final manuscript.

AcknowledgementsThis work was supported by a grant from the German Research FoundationDFG to Thomas Suslow and Harald Kugel (SU 222/6-1).We thank Sophie-Luise Lenk and Marc Rupietta for their help in data collection.

Author details1Department of Psychosomatic Medicine and Psychotherapy, University ofLeipzig, Semmelweisstrasse 10, 04103 Leipzig, Germany. 2Department ofNeurology, Max-Planck-Institute of Human Cognitive and Brain Sciences,Leipzig, Germany. 3Clinic of Cognitive Neurology, University of Leipzig,Leipzig, Germany. 4Department of Clinical Radiology, University of Münster,

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Münster, Germany. 5Department of Psychiatry and Psychotherapy, UniversityHospital Zurich, Zurich, Switzerland. 6Department of Psychiatry, University ofGreifswald, Greifswald, Germany. 7HELIOS Hospital, Stralsund, Germany.8Nuclear Magnetic Resonance Unit, Max-Planck-Institute of Human Cognitiveand Brain Sciences, Leipzig, Germany. 9Department of Psychiatry, Universityof Münster, Münster, Germany.

Received: 4 December 2013 Accepted: 7 March 2014Published: 14 March 2014

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51. Kugel H, Eichmann M, Dannlowski U, Ohrmann P, Bauer J, Arolt V, HeindelW, Suslow T: Alexithymic features and automatic amygdala reactivity tofacial emotion. Neurosci Lett 2008, 435:40–44.

52. Lee B-T, Lee H-Y, Park S-A, Lim J-Y, Tae WS, Lee M-S, Joe S-H, Jung I-K, Ham B-J:Neural substrates of affective face recognition in alexithymia: a functionalmagnetic resonance imaging study. Neuropsychobiology 2011, 63:119–124.

53. Ihme K, Dannlowski U, Lichev V, Stuhrmann A, Grotegerd D, Rosenberg N,Kugel H, Heindel W, Arolt V, Kersting A, Suslow T: Alexithymia is related todifferences in gray matter volume: a voxel-based morphometry study.Brain Res 2013, 1491:60–67.

54. Pessoa L: On the relationship between emotion and cognition. Nat RevNeurosci 2008, 9:148–158.

55. Pessoa L, Japee S, Sturman D, Ungerleider LG: Target visibility and visualawareness modulate amygdala responses to fearful faces. Cereb Cortex2006, 16:366–375.

56. Bermond B, Clayton K, Liberova A, Luminet O, Maruszewski T, Ricci Bitti PE,Rimé B, Vorst HH, Wagner H, Wicherts J: A cognitive and an affectivedimension of alexithymia in six languages and seven populations.Cogn Emot 2007, 21:1125–1136.

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doi:10.1186/1471-2202-15-40Cite this article as: Ihme et al.: Alexithymia and the labeling of facialemotions: response slowing and increased motor and somatosensoryprocessing. BMC Neuroscience 2014 15:40.

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50

3 GENERAL DISCUSSION

This chapter summarizes the results of the three original research articles and integrates them into a model

of facial emotion recognition in alexithymia. Furthermore, open issues of the model and the dissertation in

general are discussed. Finally, I will close the dissertation with a conclusion.

3.1 Summary of the original research articles

Study 1 revealed that HAIs display less GM in the left amygdala, left anterior insula and pregenual ACC

extending to dorsal part, which is in accordance with H1. Against the hypothesis, no differences in GM

volume could be found in the FFG. However, the exploratory whole brain analysis revealed that GM volume

in left MTG was shown to be reduced in HAIs. Neither the ROI analysis nor the whole brain analysis

uncovered brain regions in which HAIs show more GM volume than LAIs.

Study 2 showed that high alexithymic features are related to deficiencies in the accuracy when labeling

briefly presented (≤ 100 ms) masked facial expressions (H2.1). This deterioration is existent for all tested

emotions (happy, angry and fearful). The fMRI data point towards reduced activation with high alexithymic

features in the basal ganglia, temporal and frontal areas. Specifically, during the labeling of angry facial

expressions, HAIs display reduced neural processing in the ventral striatum, the OFC, IFG, STG and SFG. In

addition, high degrees of alexithymia were related to decreased activations in ventral striatum and MTG

when labeling fearful faces. Thus, in keeping with H2.2, reduced brain activity in a broad network of brain

areas postulated to be important for facial emotion recognition has been found.

Study 3 did not reveal an association between alexithymic features and accuracy in a facial emotion labeling

task, in which the emotional faces were presented for ≥ 1 s. However, the study revealed that high degrees

of alexithymic features are related to increased response latencies during labeling of negative (angry and

fearful) facial expressions, which is line with H3. In the exploratory part investigating the neural

underpinnings, we could show that individuals with high degrees of alexithymia displayed increased brain

activations in SMA and primary somatosensory cortex (S1) when labeling negative (angry and fearful) facial

expressions. No significant increases in brain activation were revealed during the labeling of happy faces. A

post‐hoc ROI analysis suggests none or only weak negative relationships between alexithymic features and

activation in areas related to processing of affective information (amygdala, ventral striatum, vmPFC).

3.2 Integration of findings

HAIs show accuracy decrements in labeling facial expressions of emotions when these are presented briefly

(i.e. ≤ 100 ms, see Study 2), but are able to correctly attribute an emotional label to a facial expression in

51

case these are displayed for longer times (1 s or more, see Study 3). Study 3 suggests that this equalization

of accuracy at longer presentation times comes at a cost of increased reaction times for HAIs. These results

are in line with the proposal of Grynberg et al. (2012) that HAIs need more perceptual information to

process facial expressions of emotion, and therefore have problems when facial expressions are presented

for a brief time.

Figure 2. Integration of functional and structural MRI studies on facial emotion processing in alexithymia. At the earliest stage, emotional information is already processed shallower in the amygdala (AMG). Less affective processing takes also place at later stages in the striatum (STR) and frontal areas (OFC, vmPFC). Moreover, activation in the frontal and temporal areas (IFG, SFG, MTG, STG) is reduced and the decreased activation in ACC and anterior insula point to diminished conscious feeling. Together, this leads to reduced simulation giving rise to a rather fragmented representation of the facial expression. If presentation of the facial expressions is interrupted at this stage (or other perceptual constraints are present), a recognition is impeded for HAIs. Facial expressions presented for a longer time can be recognized through increased processing of bodily and sensory properties of the facial expressions in somatosensory (S1, posterior insula) and sensorimotor (SMA) regions. Picture on bottom right presents contribution of this dissertation to the presented integration across presentation times. Meaning of color with respect to high levels of alexithymia: red filling: reduced activation in HAIs; red boundary: reduced GM volume in HAIs; dotted red boundary: GM found to be reduced in this dissertation, but seems more complicated considering the literature; green filling: increased activation in HAIs; green boundary: increased GM in HAIs. Picture of facial affect is taken from the Radboud faces database (Langner et al., 2010).

52

The findings related to the neural basis of facial emotion recognition in alexithymia are best described with

respect to the network for facial emotion recognition introduced in section 1.3. In the initial stage, the seen

face is subject to a relatively coarse significance evaluation in the amygdala (Adolphs, 2002a). HAIs already

show decreased activation in the amygdala at this stage (Kugel et al., 2008; Reker et al., 2010). This may be

promoted by a reduced GM volume in the amygdala (Study 1), possibly leading to a reduced transfer of

affective information to other brain regions (see Figure 2). In support of this, a newer study has confirmed

this decreased GM volume in the amygdala with increasing degrees of alexithymia (Laricchiuta et al., 2014).

At the next stage, signals from the amygdala are sent to visual and temporal areas to guide a more

elaborated visual analysis of emotional faces. In HAIs, this preferred processing of emotional stimuli seems

to be weaker as reflected in decreased activation in the FFG and STG (Eichmann et al., 2008; Reker et al.,

2010). Decreases in activation in these regions may be related to reductions in GM volume, which have

been revealed for the STG (Borsci et al., 2009), but not for the FFG (see Study 1 and also Laricchiuta et al.,

2014; see Figure 2). In the meantime, communication between amygdala, striatum and frontal areas

induces simulated or actual affective reactions leading to changes in the physiological state. Study 2

proposes that high degrees of alexithymia are related to decreases in activity in these regions (ventral

striatum, OFC), which again may be fostered by decreased GM volumes with increasing alexithymia in

striatum (Kubota et al., 2011) and OFC (Borsci et al., 2009, see also van der Velde et al., 2014; see Figure 2).

Through simulation and concerted activation in the aforementioned regions (amygdala, striatum, OFC), the

anterior insula, areas with mirror‐like properties (IFG, MTG, STG and SFG) as well as motor (SMA and PMC)

and somatosensory cortices, a multi‐modal internal representation of the facial expression is created. Based

on this internal representation, the emotion on the seen face can be ultimately interpreted and labeled (see

Adolphs, 2002a; van der Gaag et al., 2007). Yet, in persons with high alexithymic features, activity in some of

these regions, as IFG, MTG, STG, SFG and OFC, is reduced (Study 2), probably resulting in a less vivid internal

representation. Again, GM reductions in some of the implicated areas (MTG, STG, OFC, striatum, amygdala,

and anterior insula) might promote this (Study 1; Borsci et al., 2009; Kubota et al., 2011; Laricchiuta et al.,

2014; van der Velde et al., 2014). With this impoverished representation, recognition is impeded, especially

when the faces are only presented with temporal constraints (Study 2, see Figure 2). Nonetheless, when

enough time for extensive processing is available, HAIs can correctly label the facial expressions, though at

the costs of increased reaction times (Study 3). The accompanying increased activations in SMA and S1 with

increasing alexithymia revealed in Study 3 indicate that an increased analysis of sensorimotor and

somatosensory aspects of the facial expression takes place. Hence, HAIs initially acquire a less complete

affective representation as reflected in decreased limbic activation (Study 2; Kugel et al., 2008; Reker et al.,

2010), but can make up for this by relying on bodily cues of the face like the configuration of the muscles

(Study 3). This overamplification of bodily and physical signals (see also Krystal, 1988; Moriguchi and

53

Komaki, 2013) is in line with two recent sMRI studies revealing more GM volume with increasing

alexithymia in cerebellar regions (Laricchiuta et al., 2014) and posterior insula (Goerlich‐Dobre et al., 2014)

which – in contrast to the anterior insula – has been linked to the somatosensory network (Deen et al.,

2011) (Figure 2). To sum up, it seems as if the problems in labeling facial emotions in HAIs stem from

difficulties in early processing which is then passed to later stages leading to impoverished internal

representations of the seen emotional face. With enough time, HAIs can partly account for this by

increasingly relying on processing and interpretation of physical (sensorimotor and somatosensory) aspects

of the faces.

3.3 Open issues and ideas for further research

One open issue is the role of the ACC during facial emotion recognition in alexithymia. The ACC has been

suspected to be one of the core structures in the generation of alexithymia (Wingbermühle et al., 2012). In

facial emotion recognition, it is thought to produce an affective state based on the seen stimulus (Phillips et

al., 2003), which likely enriches the internal representation of the seen face. Moreover, in interaction with

other frontal areas and anterior insula, a conscious feeling is produced (Lane, 2008; Tsuchiya and Adolphs,

2007). Still, the fMRI studies did not reveal a relationship between alexithymia and ACC activation during

facial emotion labeling (Studies 2 and 3). Moreover, the literature on the association between alexithymia

and ACC morphology provided highly diverse findings (Borsci et al., 2009; Gündel et al., 2000; Heinzel et al.,

2012, see also Goerlich‐Dobre et al., 2014; van der Velde et al., 2014). Our results indicate that HAIs have

decreased GM volume in the pregenual ACC, extending dorsally (Study 1). A patient study has shown that

lesions to a similar part of the ACC have a negative impact on facial emotion recognition (Schäfer et al.,

2007), so that the reductions in GM volume revealed in Study 1 may link to this as well. This indicates a role

of the (pregenual) ACC in alexithymia (and facial emotion recognition), but this still needs to be further

investigated in order to explain the ambiguous findings reported so far.

The model presented here (cf. Figure 2) only integrates studies reporting either GM differences or task‐

related activations associated with alexithymia. Thus, the described interactions between the brain regions

are based on general theory on facial emotion recognition (e.g., Adolphs, 2002a; van der Gaag et al., 2007,

see Figure 2). To further refine the model, data on the connectivity between these regions and their

dependence on alexithymia and facial emotion recognition are needed. This would include structural

connectivity analysis as well as task‐related and resting‐state functional connectivity studies. To the best of

my knowledge, up to now only few studies exist that examine connectivity in alexithymia, but the results

are inconclusive. In support of the model presented here, Liemburg et al. (2012) found decreased resting‐

state connectivity in brain areas relating to emotional processing, but increases in rather sensory and

somatosensory regions indicating a more action‐oriented focus of HAIs. In contrast, Kubota et al. (2012)

54

report no associations between structural connectivity and alexithymia in a group of HCs. Other than that,

results on connectivity profiles attributed to increased alexithymic features are sparse, so that research is

desired to shed light on the exact interactions between the described nodes of the network.

Outside laboratory settings, emotions are expressed not only in the face, but also by other verbal and non‐

verbal cues, such as emotional words, prosody or body language (Scherer and Ellgring, 2007). Up to date,

research points towards reduced processing of emotional vocalizations with increasing alexithymia possibly

linked to decreased amygdala and STG processing (Goerlich et al., 2011, 2012; Goerlich‐Dobre et al., 2013).

Moreover, viewing bodily expressions of emotions was linked to decreased amygdala activation in HAIs

(Pouga et al., 2010). The findings support the model presented in Figure 2. Hence, systematically studying

the influence of alexithymic features on the neural processing and recognition of bodily and vocal

expressions of emotions could heavily enrich the model and highlight its ecological validity.

In their review, Grynberg et al. (2012) conclude that the difficulties of HAIs in processing facial expressions

are not limited to particular (categories of) emotions. This is basically in line with the behavioral results of

Study 2. However, alterations in neuronal processing seem to be basically present for negative emotions.

Similarly, in Study 3 only negative emotions were significantly related to an increased processing in

somatosensory and motor areas. Moreover, response latencies were found to be positively correlated with

alexithymic features only for fearful and angry, not for happy faces, suggesting that recognizing negative

emotions is harder. This may be due to the stimuli used here (Karolinska Directed Emotional Face Database,

Lundqvist et al., 1998) for which it has been reported that happy faces are easiest to recognize (Calvo and

Lundqvist, 2008). Thus, future research needs to examine possible emotion specific effects in relation to

alexithymic features.

With respect to the assessment of alexithymia, it is worth mentioning that a multi‐method approach for

assessing alexithymia, as desired in the literature (Lichev et al., 2014; Lumley et al., 2005), was employed in

Study 2 and 3. Thus the results presented in these studies are unbiased by the mere self‐report of the

participants. For Study 1, this was unfortunately not possible, so that it needs be tested whether

alexithymia as assessed with observer‐based methods is linked to similar morphological profiles as revealed

here. Due to space constraints, I referred to alexithymic features as whole and did not discuss specific

results of the measuring approach (TAS‐20 versus TSIA) or particular subscales. However, Study 2 and Study

3 replicate previous studies reporting that labeling facial emotions is especially linked to the subscale DDF,

possibly because this task involves a form of semantic processing when providing the label (see also

Grynberg et al., 2012; Parker et al., 2005).

In light of a global neurobiological model for explaining the difficulties in identifying and describing feelings

of high alexithymic individuals, the findings of this dissertation argue in favor of deficient processing of

emotional information. This deficiency seems to be most severe in bottom‐up processing and lead to a

55

decreased affective reactivity to external emotional stimuli. As HAIs have an altered brain structure and

show reduced activation in the shared substrates of emotion (Heberlein and Adolphs, 2007), it may be that

also processing of internally triggered affect is reduced in alexithymia. In addition, the current results point

to altered brain structure and function in several brain regions, so that it seems that no single fix structure

but rather a network as neural correlate of alexithymia.

This dissertation shed light on the neural mechanisms underlying HAIs’ difficulty in recognizing emotional

facial expressions and the conditions in which this is less severe. Based on the findings, it may be speculated

that interaction with HAIs in daily life could improve if the social partner expresses emotions more clearly

and for a longer time in order to provide guidance for HAIs in the recognition of emotions from non‐verbal

cues. During therapeutic interventions this may also help to enhance patient‐therapist interaction and

therefore the chance for a positive therapy outcome. Furthermore, it would be interesting to conduct

longitudinal studies examining whether training to recognize facial emotions leads to an improvement in

HAIs’ ability therein and a change in brain structure and function giving rise to an amelioration of their

social skills.

3.4 Conclusion

To sum up, this dissertation investigated the neural correlates underlying alterations in facial emotional

processing with increasing alexithymic features. Together with the existing literature, it indicates that high

alexithymic individuals show a reduced early processing of affective information when confronted with

emotional faces. At later processing stages this leads to an impoverished internal representation of the seen

face resulting in a decreased ability to recognize its emotional content. However, in case temporal demands

are low, high alexithymic individuals can account for this reduced affective representation by increased

processing of the sensorimotor and somatosensory aspects of the facial expressions. Under this condition,

it becomes more likely that high alexithymic individuals correctly recognize the facial expression.

56

4 ZUSAMMENFASSUNG DER ARBEIT

Dissertation zur Erlangung des akademischen Grades

Dr. rer. med.

Functional and structural neuroimaging of facial emotion recognition in alexithymia

eingereicht von:

Ihme, Klas

geboren am 14.10.1983 in Braunschweig

angefertigt in der:

Klinik für Psychosomatische Medizin und Psychotherapie, Universität Leipzig

betreut von Prof. Dr. med. Anette Kersting

Oktober 2014

Introduction

The ability to infer emotions, motivations and beliefs of other people from facial expressions is one of the

prerequisites for successful social interaction as these provide valuable information about inner affective

states (Erickson and Schulkin, 2003). Recognizing emotional facial expressions requires a holistic internal

representation of the seen expressions, which is achieved through covertly or overtly simulating the facial

expression and the underlying emotion of the other person. This is based on the integration of affective,

visual, somatosensory and motor information and requires parallel and sequential processing in a multitude

of cortical and subcortical brain structures (Adolphs, 2002; van der Gaag et al., 2007). In this process, brain

areas with mirror‐like properties are essential, because these are involved in the expression and feeling of

one’s own emotions and their recognition in other people (shared substrates of emotion, cf. Heberlein and

Atkinson, 2009). Accordingly, a fragmented, incomplete representation of the seen facial expression hinders

its correct recognition (Delle‐Vigne et al., 2014).

Interestingly, although alexithymia (“no words for emotion”) is generally characterized by difficulties

identifying and describing one’s own feelings, recent research has found that high degrees in this

57

personality trait also come along with difficulties in recognizing emotional facial expressions of other

persons (Grynberg et al., 2012). As this difficulty is most prominent when the facial expressions are

presented with temporal or other perceptual constraints, it has been suggested that high alexithymic

individuals need more time to process the given perceptual information (Grynberg et al., 2012). However,

little research has systematically investigated the structural and functional neural correlates of high

alexithymic individuals' difficulties in recognizing facial expressions. Especially, it is still unclear whether high

alexithymic individuals show a different gray matter profile than low alexithymic individuals. Moreover, it

needs to be investigated whether alterations in brain function relate to high alexithymic individuals’

difficulties in labeling briefly presented facial expressions of emotion. Finally, it has been shown that high

alexithymic individuals are capable of recognizing facial expressions of emotion when these are presented

with little temporal constraints. However, whether a change in neural activation accompanies this change in

performance remains to be elucidated.

Original Research Articles

In order to examine the neural correlates of facial emotion recognition as a function of alexithymia, one

structural and two functional neuroimaging studies have been conducted. These are compiled into the

following three original research articles:

Study 1:


Study 2:

Ihme K, Sacher J, Lichev V, Rosenberg N, Kugel H, Rufer M, Grabe HJ, Pampel A, Lepsien J, Kersting A, Villringer A, Lane R, and Suslow T. Alexithymic features and the labeling of brief emotional facial expressions – an fMRI study. Neuropsychologia, 64: 289‐299, 2014.

Study 3:

Ihme K, Sacher J, Lichev V, Rosenberg N, Kugel H, Rufer M, Grabe HJ, Pampel A, Lepsien J, Kersting A, Villringer A, and Suslow T. Alexithymia and the labeling of facial emotions: response slowing and increased motor and somatosensory processing. BMC Neuroscience, 15 (1): 40, 2014.

Study 1 investigated whether high degrees of alexithymia are related to decreases in gray matter volume in

brain regions associated with facial emotion processing. Using voxel‐based morphometry of structural

magnetic resonance images, a group of high alexithymic individuals was compared to a group of low

alexithymic individuals with respect to their gray matter volume in four a priori defined regions of interest

(amygala, anterior insula, anterior cingulate gyrus, and fusiform gyrus). Moreover, an exploratory whole

brain analysis was accomplished in order to assess whether the gray matter profile was different in other

58

brain regions. In total, the data of 34 individuals were analyzed. Participants were assigned to the two

alexithymia groups based on their score on the 20‐item version of the Toronto Alexithymia Scale (TAS‐20).

Both groups significantly differed in their degree of alexithymia, but not in terms of age, gender (eight

females per group) or depressivity. Results indicate that high compared to low alexithymic individuals show

decreased gray matter volume in amygdala, anterior insula and anterior cingulate gyrus. Moreover, the

whole brain approach revealed decreased gray matter volume in the middle temporal gyrus. Against

expectations, no difference in gray matter volume was revealed for the fusiform gyrus.

The aim of Study 2 was to investigate brain activation in alexithymia during the labeling of emotional facial

expressions presented with high temporal constraints. The presentation times were chosen according to a

range in which performance decrements with increasing alexithymia are highly likely (cf. Grynberg et al.,

2012). It was assumed that alexithymia negatively correlates with performance and the activation of brain

areas implicated in the recognition of facial expressions of emotion. Functional magnetic resonance imaging

(fMRI) data was recorded from 50 participants during the labeling of briefly presented (66 and 100 ms)

emotional (happy, neutral, angry, and fearful) facial expressions. The degree of alexithymia of the

participants was measured using the 20‐item version of the Toronto Alexithymia Scale and the Toronto

Structured Interview for Alexithymia (TSIA). Moreover, participants' degree of positive and negative

affectivity was assessed. In a correlational design, accuracy and neural activations were related to the

degree of alexithymic features (TAS‐20 and TSIA total score, TAS and TSIA subscales difficulties describing

feeling and difficulties identifying feelings). In case one of these measures of alexithymia correlated with

negative or positive affectivity, the respective affectivity score was added as nuisance covariate into the

calculated models for the respective alexithymia measure. The behavioral data show that the degree of

alexithymia was negatively related to labeling accuracy for all presented emotions. In terms of the neural

correlates, increased degrees of alexithymic features were associated with reduced activation in the basal

ganglia, temporal and frontal areas. Specifically, during the labeling of angry facial expressions, high

alexithymic individuals display a reduced neural processing in the ventral striatum, the orbito‐frontal cortex,

inferior and superior frontal gyrus as well as superior temporal gyrus. In addition, high degrees of

alexithymia were related to decreased activations in ventral striatum and middle temporal gyrus when

labeling fearful faces. These results were relatively independent of negative and positive affectivity. Thus,

reduced brain activity was revealed in a broad network of brain areas linked to facial emotion recognition.

Study 3 was designed to examine whether high alexithymic individuals show a particular neural activation

pattern when labeling emotional facial expressions presented with little temporal constraints (i.e., ≥ 1 s). In

this time range high alexithymic individuals are generally able to correctly assign an emotional label to the

seen expression. It was hypothesized that there is no relationship between alexithymia and accuracy, but

that the degree of alexithymic features positively correlates with response latency. The accompanying brain

59

activations were exploratorily investigated. Therefore, 48 participants were asked to label emotional facial

expressions (happy, neutral, angry, and fearful) presented for 1 or 3 seconds while undergoing functional

magnetic resonance imaging. Participants' level of alexithymia was assessed using self‐report (TAS‐20) and

interview (TSIA). In addition, their degree of negative and positive affectivity was recorded. Behavioral data

were analyzed in terms of accuracy (sensitivity) and response latency and were tested for their relation to

the degree of alexithymic features using correlation analyses. Moreover, neural activations during labeling

were related to the degree of alexithymic features (TAS‐20 and TSIA subscale difficulties describing feelings)

in a correlational design. Participants’ positive affectivity scores were entered as nuisance covariate into the

respective models, as these were inversely related to TAS‐20 subscale difficulties describing feelings. In this

experiment, no association between alexithymic features and accuracy in the facial emotion labeling task

could be revealed. Interestingly, it could be shown that high degrees of alexithymic features were related to

increased response latencies during labeling of negative (angry and fearful) facial expressions. With respect

to the neural underpinnings, individuals with high degrees of alexithymia displayed increased brain

activations in supplementary motor area and primary somatosensory cortex when labeling negative (angry

and fearful) facial expressions. No significant changes in brain activation were revealed during the labeling

of happy faces. A post‐hoc region‐of‐interest analysis suggests none or only weak negative relationships

between alexithymic features and activation in areas related to processing of affective information

(amygdala, ventral striatum, ventro‐medial pre‐frontal cortex).

General Discussion

In keeping with previous research, this dissertation shows that high alexithymic individuals have difficulties

in labeling facial expressions of emotion, when these are presented briefly, but have less problems when

temporal demands are low (cf. Grynberg et al., 2012). The increased response times found in Study 3

indicates that high alexithymic individuals need more perceptual information to recognize facial expressions

(cf. Grynberg et al., 2012). The findings related to the neural basis of facial emotion recognition in

alexithymia are best described with respect to the network for facial emotion recognition as described in

the literature (Adolphs, 2002; van der Gaag et al., 2007). In the initial stage, the seen face is subject to a

relatively coarse significance evaluation in the amygdala (Adolphs, 2002). High alexithymic individuals show

decreased activation already at this stage (e.g. Reker et al., 2010). This appears to be promoted by reduced

gray matter volume in the amygdala (Study 1), possibly leading to a reduced transfer of affective

information to other brain regions. At the next stage, signals from the amygdala are sent to visual and

temporal areas to guide a more elaborated visual analysis of emotional faces. In high alexithymic

individuals, this preferred processing of emotional stimuli seems to be weaker as reflected in decreased

activation in fusiform and superior temporal gyrus (e.g. Reker et al., 2010). Decreases in activation in these

60

regions seems to be related to reductions in gray matter volume, which have been revealed for the superior

temporal gyrus (Borsci et al., 2009), but not for the fusiform gyrus (see Study 1). Meanwhile,

communication between amygdala, striatum and frontal areas induces simulated or actual affective

reactions leading to changes in the physiological state. Study 2 proposes that high degrees of alexithymia

are related to decreases in activity in these regions (ventral striatum, orbito‐frontal gyrus), which seems to

be fostered by decreased gray matter volumes in these regions with increasing alexithymia (Kubota et al.,

2011; Borsci et al., 2009). Through simulation and concerted activation in the aforementioned regions

(amygdala, striatum, orbito‐frontal gyrus), the anterior insula as well as other frontal and temporal regions

plus motor (supplementary motor area and pre‐motor cortex) and somatosensory areas, a holistic internal

representation of the facial expression is created. Based on this internal representation, the emotion on the

seen face can be ultimately interpreted and labeled (see Adolphs, 2002a; van der Gaag et al., 2007). Yet, in

persons with high degrees of alexithymic features, activity in regions with mirror‐like properties (inferior

and superior frontal gyrus as well as middle and superior temporal gyrus) and orbito‐frontal cortex is

reduced (Study 2), likely resulting in a less vivid internal representation. Again, gray matter reductions in

some of the implicated areas (temporal areas, orbito‐frontal cortex, striatum, amygdala, and anterior insula)

appear to promote this (Study 1; Borsci et al., 2009; Kubota et al., 2011). With this impoverished

representation, recognition is impeded, especially when the faces are presented with temporal constraints

(Study 2). Nonetheless, when enough time for extensive processing is available, high alexithymic individuals

can correctly label the facial expressions, though at the costs of increased reaction times (Study 3). The

accompanying increased activations in supplementary motor area and somatosensory cortex with

increasing alexithymia revealed in Study 3 indicate that an increased analysis of motor and somatosensory

aspects of the facial expression takes place. Hence, high alexithymic individuals initially acquire a less

complete affective representation as reflected in decreased limbic activation (Study 2, e.g. Reker et al.,

2010), but can make up for this by relying on physical cues of the face like the configuration of the muscles

(Study 3).

Conclusion

In summary, this dissertation shows that high alexithymic individuals’ problems in labeling facial emotions

seem to stem from difficulties in the processing of affective information. This in turn results in an

impoverished internal representation of the seen emotional face. However, with enough time and

information available, high alexithymic individuals can partly account for this by increasingly relying on

processing and interpretation of physical (motor and somatosensory) aspects of the facial expression.

61

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6 APPENDIX

71

Lebenslauf

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72

Publikationsverzeichnis

Peer‐reviewed

Artikel in Zeitschriften

Ihme K, Sacher J, Lichev V, Rosenberg N, Kugel H, Rufer M, Grabe H‐J, Pampel A, Lepsien J, Kersting A, Villringer A, Lane RD, and Suslow T. Alexithymic features and the labeling of brief emotional facial expressions – an fMRI study. Neuropsychologia, 2014.

Lichev, V, Sacher, J, Ihme, K, Rosenberg, N, Quirin, M, Lepsien, J, Pampel, A, Rufer, M, Grabe, H‐J, Kugel, H, Kersting, A, Villringer, A, Lane, RD, and Suslow, T. Automatic Emotion Processing as a Function of Trait Emotional Awareness: An fMRI Study. Social Cognitive and Affective Neuroscience, nsu104, 2014.

Fritz TH, Ciupek M, Kirkland A, Ihme K, Guha A, Hoyer J, and Villringer A. Enhanced response to music in pregnancy. Psychophysiology, 51 (9): 905‐911, 2014.

Ihme K, Sacher J, Lichev V, Rosenberg N, Kugel H, Rufer M, Grabe H‐J, Pampel A, Lepsien J, Kersting A, Villringer A, and Suslow T. Alexithymia and the labeling of facial emotions: response slowing and increased motor and somatosensory processing. BMC Neuroscience, 15 (1): 40, 2014.

Lichev V, Rufer M, Rosenberg N, Ihme K, Grabe H‐J, Kugel H, Donges U‐S, Kersting A, and Suslow T. Assessing alexithymia and emotional awareness: Relations between measures in a German non‐clinical sample. Comprehensive Psychiatry, 55 (4): 952–959, 2014.


Donges U‐S, Kugel H, Stuhrmann A, Grotegerd D, Redlich R, Lichev V, Rosenberg N, Ihme K, Suslow T, and Dannlowski U. Adult attachment anxiety is associated with enhanced automatic neural response to positive facial expression. Neuroscience, 220: 149–57, 2012.

Zander TO, Ihme K, Gaertner M, and Rötting M. A public data hub for benchmarking common BCI algorithms. Journal of Neural Engineering, 8 (2): 25021, 2011.

Zander TO, Lehne M, Ihme K, Jatzev S, Correia J, Kothe C, Picht B, and Nijboer F. A dry EEG‐system for scientific research and brain‐computer interfaces. Frontiers in Neuroscience, 5: 53, 2011.

Conference Proceedings

Protzak J, Ihme K, and Zander TO. A Passive Brain‐Computer Interface for Supporting Gaze‐Based Human‐Machine Interaction. In Stephanidis C and Antona M (Eds.), Universal Access in Human‐Computer Interaction. Design Methods, Tools, and Interaction Techniques for eInclusion, Lecture Notes in Computer Science, Volume 8009. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013: 662–671.

Ihme K and Zander TO. What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze‐Based HCI. Affective Computing and Intelligent Interaction, Lecture Notes in Computer Science, 6975: 2011.

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Lehne M, Ihme K, Brouwer A‐M, van Erp JBF, and Zander TO. Error‐related EEG patterns during tactile human‐machine interaction. Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on, 1–9, 2009.

Abstracts und Poster (Auswahl)

Lethaus F, Ihme K, Gürlük H, Rataj J, and Jipp M. Conceptual View of User‐ and Situation‐Based Adaptive Automation in Flight and Vehicle Guidance. Deutscher Luft‐ und Raumfahrtkongress, Augsburg, Germany, September 15‐18, 2014.

Ihme K, Lichev V, Rosenberg N, Sacher J, Villringer A, Kersting A, Lane R, and Suslow T. Ich fühle was, was Du nicht siehst? Alexithymie und die Erkennung von Mikroexpressionen – eine funktionelle Magnetresonanztomografiestudie. Klinische Neurophysiologie, 44 (01): P75, 2013.

Ihme K, Lichev V, Rosenberg N, Sacher J, Villringer A, Kersting A, and Suslow T. P 59. Which brain regions are involved in the correct detection of microexpressions? Preliminary results from a functional magnetic resonance imaging study. Clinical Neurophysiology, 124 (10): e92–e93, 2013.

Lamke J‐P, Ihme K, Lehne M, and Wilutzky W. Moral in the Face of Disgust. animal emotionale II – The Evolution of Disgust. From Oral to Moral. ZiF Center for Interdisciplinary Research, Bielefeld, Germany, January 4‐7, 2012.

Vorträge

Ihme, K. Ich fühle was, was du nicht siehst? Neuronale Korrelate der Erkennung emotionaler Gesichter in Abhängigkeit des Persönlichkeitsmerkmals Alexithymie. Vortrag im Neurowissenschaftlichen Kolloquium der Deutschen Sporthochschule Köln, Juli 2013.

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Erklärung über die eigenständige Abfassung der Arbeit

Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig und ohne unzulässige Hilfe oder Benutzung

anderer als der angegebenen Hilfsmittel angefertigt habe. Ich versichere, dass Dritte von mir weder

unmittelbar noch mittelbar geldwerte Leistungen für Arbeiten erhalten haben, die im Zusammenhang mit

dem Inhalt der vorgelegten Dissertation stehen, und dass die vorgelegte Arbeit weder im Inland noch im

Ausland in gleicher oder ähnlicher Form einer anderen Prüfungsbehörde zum Zweck einer Promotion oder

eines anderen Prüfungsverfahrens vorgelegt wurde. Alles aus anderen Quellen und von anderen Personen

übernommene Material, das in der Arbeit verwendet wurde oder auf das direkt Bezug genommen wird,

wurde als solches kenntlich gemacht. Insbesondere wurden alle Personen genannt, die direkt an der

Entstehung der vorliegenden Arbeit beteiligt waren.

................................. ....................................

Datum Unterschrift

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Acknowledgement

First of all, I would like to thank my supervisor Anette Kersting and my in‐official supervisor Thomas Suslow

for providing me with the opportunity to accomplish a PhD in the exiting field of affective neuroscience.

During the last four years, especially the fruitful discussions with and support from Thomas Suslow gave me

the possibility to learn a lot about alexithymia, emotions and neuroimaging which will definitely be useful in

my future life as a researcher. In addition, I would like to thank all my colleagues and students in our lab and

at the Clinic for Psychosomatic Medicine and Psychotherapy in general for help during daily work, a nice

working atmosphere and relaxing lunch breaks. Thank you, Vladimir, Nicole, Vivien, Ulrike, Grit, Ruth, Caro,

Jule, Jana, Helge, Franzi, Anne, Katja, Katharina, Sophie, Falk, Tobias, Marc, Antje, Fr. Säuberlich and all the

ones that I forgot to mention. It was a pleasure to work with you!

I gratefully acknowledge the contribution and support of all cooperation partners and co‐authors in the

three studies. Moreover, I'm grateful to the US National Institutes of Health (NIH, grants R90DA023420 and

T90DA022761) and the Federation of European Neuroscience Societies (FENS) for travel grants to visit the

Multimodal Neuroimaging Training Program (MNTP) in Pittsburgh in 2012 and the FENS‐IBRO Imaging

Training Center in Lausanne and Geneva in 2013. The stuff I learned during these workshops was essential

for the dissertation and will certainly be useful in the future as well. Furthermore, I thank Nicole Rosenberg,

Vladimir Lichev, Rick Solis and Dominique Goltz for proofreading of the dissertation.

Last but not least, I'm truly grateful to Domi and my parents for support and advice during my education

and the rest of my life.

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Study 1: Specification of author contribution

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Study 2: Supplementary Materials

In our study, we pooled (behavioral and fMRI) data across presentation time conditions (66 vs. 100ms) to

increase statistical power. In the following, reasons are provided that justify this decision.

It has to be mentioned that in our sample there was a significant difference in labeling performance

between presentation time conditions (see Table S1 for means and standard deviations). A 4 (emotion:

happy, neutral, angry, fearful) x 2 (presentation time: 66 vs. 100 ms) repeated measures ANOVA showed

significant main effects of emotion (F(3,147) = 9.4, p < .001) and presentation time (F(1,49) = 46.4, p <

0.001), but no significant interaction effect of emotion condition and presentation time (F(3,147) = 2.2, p

= .10). This means that performance was significantly better in the 100 ms presentation time condition

compared to the 66 ms presentation time condition. However, in our study we were not interested in the

difference between performances at 66 ms versus 100 ms presentation, but in the effect of alexithymic

features on labeling of briefly presented emotional facial expressions. In our view, there are two arguments

in favor of our decision to pool data across presentation times.

Table S1. Means and standard deviations of labeling performance (sensitivity index) for the emotional expression conditions as a function of presentation time (66 and 100 ms).

happy neutral angry fearful 66 ms 100 ms 66 ms 100 ms 66 ms 100 ms 66 ms 100 msmean .82 .90 .79 .86 .79 .86 .74 .85

standard deviation .18 .15 .18 .16 .15 .13 .17 .14

1 We compared the correlation coefficients between alexithymia and labeling performance for the two

presentation time conditions using Steiger's Z (Steiger, 1980). This revealed no significant differences

between the correlation coefficients for any of the alexithymia measures. An overview of the

correlations between alexithymia scores and labeling performance as a function of (emotion condition

and) presentation time is presented in Table S2. Steiger's Z statistics comparing strength of correlation

between labeling performance at the two different presentation times (66 and 100 ms) and measures

of alexithymia are shown in Table S3.

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Table S2. Correlations between measures of alexithymia and labeling performance as a function of emotion condition and presentation time (66 vs. 100ms). The last line shows correlations between labeling performance at 66 ms and 100 ms for each emotion condition.

HA NE AN FE

66 ms 100 ms 66 ms 100 ms 66 ms 100 ms 66 ms 100 ms TAS-20 total -.24 -.21 -.16 -.25 -.28 -.31 -.24 -.16

TAS-DDF -.26 -.40 -.24 -.34 -.30 -.41 -.27 -.29 TAS-DIF -.13 -.09 -.06 -.12 -.18 -.13 -.06 .04

TSIA total -.03 .05 .09 .11 -.01 .03 -.12 .01 TSIA-DDF -.11 -.07 .03 .00 -.09 -.09 -.17 -.08 TSIA-DIF .08 .04 .19 .13 .07 .06 .00 .01

66 ~ 100 .65 .74 .80 .82 Note. TAS‐20 = 20‐Item Toronto Alexithymia Scale, TSIA = Toronto Structured Interview for Alexithymia, DDF = Difficulties Describing Feelings, DIF = Difficulties Identifying Feelings. HA = happy faces, NE = neutral faces, AN = angry faces, FE = fearful faces.

Table S3. Steiger's Z statistic comparing strength of correlation between labeling performance at the two different presentation times (66 and 100 ms) and measures of alexithymia for the four facial expression conditions. Steiger's Z was calculated using the program of Lee and Preacher (2013).

HA NE AN FE

Z p Z p Z p Z p

TAS total -.25 .80 .88 .38 .34 .73 -.94 .35 TAS-DDF 1.24 .22 -.10 .92 1.29 .20 .24 .81 TAS-DIF -.33 .74 .57 .57 .66 .51 -1.15 .25

TSIA total -.66 .51 -.10 .92 1.31 .19 -1.49 .14 TSIA-DDF -.33 .74 .29 .78 -.43 .66 -1.04 .29 TSIA-DIF -.33 .74 -.58 .56 0 1 -.23 .82

Note. TAS‐20 = 20‐Item Toronto Alexithymia Scale, TSIA = Toronto Structured Interview for Alexithymia, DDF = Difficulties Describing Feelings, DIF = Difficulties Identifying Feelings. HA = happy faces, NE = neutral faces, AN = angry faces, FE = fearful faces.

2 In a recent review on alexithymia and processing of emotional facial expressions, which was published

after we have started our data collection, Grynberg et al. (2012) come to the conclusion that

alexithymic individuals should have difficulties in labeling facial expressions especially when these are

presented below 300 ms. Both of our presentation times are clearly below this value and therefore one

can expect that there should be no substantial differences in the association of alexithymia and

labeling performance between the two presentation time conditions (66 and 100 ms).

References

Grynberg, D., Chang, B., Corneille, O., Maurage, P., Vermeulen, N., Berthoz, S., & Luminet, O. (2012). Alexithymia and the Processing of Emotional Facial Expressions (EFEs): Systematic Review, Unanswered Questions and Further Perspectives. PloS One, 7(8), e42429.

Lee, I. A., & Preacher, K. J. (2013). Calculation for the test of the difference between two dependent correlations with one variable in common. http://quantpsy.org/corrtest/corrtest2.htm, accessed August 2014.

Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87(2), 245–251.

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Study 3: Supplementary Materials

Table S1. Brain activation in the three main contrasts at a threshold of t=3.27, k=10.

cluster peak localization

k pfwe x y z Z pfwe hem. region HA>NE 1 429 <.001 -42 -76 31 5.10 <.01 left middle occipital gyrus,

middle temporal gyrus

2 571 <.001 -6 53 2 4.99 <.05 left middle orbital gyrus, superior frontal gyrus, superior medial gyrus, anterior cingulate gyrus

3 109 <.01 -30 26 49 4.43 .16 left middle frontal gyrus, superior frontal gyrus

4 33 .24 -6 -58 19 3.93 .52 left precuneus

5 14 .70 -24 -7 -20 3.93 .52 left amygdala

6 46 .11 -9 -46 34 3.88 .58 left middle cingulate gyrus

7 20 .52 -27 -34 -14 3.74 .74 left parahippocampal gyurs

8 17 .60 -63 -25 -14 3.66 .83 left middle temporal gyrus

AN>NE 1 140 <.01 42 -49 -14 4.63 .05 right fusiform gyrus

2 103 <.01 36 -91 4 4.98 .11 right inferior occipital gyrus, middle occipital gyrus, lingual gyrus

3 219 <.001 -39 -73 -8 4.40 .13 left fusiform gyrus, inferior temporal gyrus

4 77 <.05 -30 -94 7 3.92 .52 left middle occipital gyrus

5 45 .13 21 -10 -14 4.84 <.05 right amygdala, hippocampus

6 24 .42 51 -37 4 4.29 .19 right middle temporal gyrus

7 37 .20 -39 32 -5 4.28 .18 left inferior frontal gyrus

8 17 .61 51 35 1 4.09 .34 right inferior frontal gyrus

9 37 .20 -21 -7 17 4.03 .41 left amygdala

10 20 .52 -3 -31 -11 3.96 .48 left not found in probability map

11 62 .05 -51 5 28 3.89 .57 left precentral gyrus, inferior frontal gyrus

12 29 .31 -3 -13 4 3.76 .71 left thalamus

13 13 .74 -6 53 31 3.47 .95 left superior medial gyrus

FE>NE 1 721 <.001 -45 14 22 5.93 <.001 left inferior frontal gyrus, pars triangularis

2 203 <.001 36 -91 2 5.58 <.001 right inferior occipital gyrus

3 350 <.001 -36 -73 -8 5.03 <.01 left fusiform gyrus, inferior occipital gyrus

4 130 <.01 -57 -55 7 4.62 .05 left middle temporal gyrus

5 145 <.01 15 -79 -35 4.58 .06 right cerebellum, lobule VIIb

6 53 .09 -6 23 49 4.29 .13 left supplementary motor area

7 63 .06 48 -37 4 4.15 .27 right middle temporal gyrus

8 20 .53 42 17 25 3.98 .44 right inferior frontal gyrus

9 14 .71 -6 -10 4 3.91 .52 left thalamus

10 24 .44 42 -49 14 3.65 .81 right inferior temporal gyrus

Note. The region refers to brain areas through which the cluster is spanning. HA>NE = happy versus neutral faces, AN>NE = angry versus neutral faces, FE>NE = fearful versus neutral faces, hem. = hemisphere, x,y and z are in MNI space.

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Table S2. Brain activation related to measures of alexithymia in the three contrasts at a threshold of t=3.27, k=10.

cluster peak localization

k pfwe x y z Z pfwe hem. region

HA>NE TSIA-DDF+ 14 .70 30 -31 34 3.92 .52 right not assigned (close to

posterior gyrus)

TSIA-DDF- 16 .64 -3 53 4 3.86 .60 left middle orbital gyrus

AN>NE TSIA-DDF+ 61 .05 30 -37 40 4.76 <.05 right Area 3a, S1

87 <.01 -6 -1 61 4.20 .24 left Area 6, supplementary motor area

16 .64 54 -28 7 3.78 .69 right superior parietal lobule

23 .44 -18 -58 52 3.71 .76 left superior temporal gyrus

11 .80 -21 -49 49 3.67 .81 right Area 6, precentral gyrus

TSIA-DDF-: no suprathreshold activation

FE>NE TSIA-DDF+ 11 .80 -54 11 19 3.78 .67 left Area 44, inferior frontal gyrus

11 .80 9 -4 61 3.39 .96 right Area 6, supplementary motor area

TSIA-DDF- 15 .68 -9 -4 25 3.62 .83 left caudate nucleus

Note. No suprathreshold clusters were revealed in relation to the subscale DDF of the 20‐item Toronto Alexithymia Scale. The region refers to brain areas through which the cluster is spanning. HA>NE = happy versus neutral faces, AN>NE = angry versus neutral faces, FE>NE = fearful versus neutral faces, TSIA = Toronto Structured Interview for Alexithymia. DDF = subscale difficulties describing feelings, + = positive relationship between measure and brain activation, ‐ = negative relationship between measure and brain activation, hem. = hemisphere, x,y and z are in MNI space.

Functional and structural neuroimaging of facial emotion ... · 80 Seiten, 139 Literaturangaben 1,...

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Transcript of Functional and structural neuroimaging of facial emotion ... · 80 Seiten, 139 Literaturangaben 1,...