Psychiatry and Clinical Neurosciences 2015; 69: 708–716
doi:10.1111/pcn.12318
Regular Article
Brain correlates of alexithymia in eating disorders: A voxel-based morphometry study Federico D’Agata, PhD,1*† Paola Caroppo, MD, PhD,1,3† Federico Amianto, MD, PhD,1 Angela Spalatro, MD,1 Marcella M. Caglio, PhD,1 Mauro Bergui, MD,1,2 Luca Lavagnino, MD, PhD,4 Dorico Righi, MD,2 Giovanni Abbate-Daga, MD,1 Lorenzo Pinessi, MD,1 Paolo Mortara, MD1 and Secondo Fassino, MD1 1
Department of Neuroscience, University of Turin, 2Radiology Section, AOU Città della Salute e della Scienza, Turin, Besta Neurological Institute, Milan, Italy, and 4Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center (UTHealth), Houston, USA 3
Aims: Alexithymia is a personality trait that consists of difficulty in identifying and acknowledging one’s own and others’ feelings. Recent studies reported that alexithymia is present in both anorexia (AN) and bulimia nervosa (BN). Brain morphological studies on healthy subjects showed that alexithymia correlates with several brain regions involved in emotions processing. The aim of this study was to investigate the anatomical correlates of alexithymia in AN and BN. Methods: We performed a voxel-based morphometry study on 21 patients with AN and 18 with BN. Seventeen healthy subjects were used as a control group. Alexithymia, depression and anxiety were assessed with self-administered questionnaires and correlated to gray matter (GM) density in each group. Results: In BN, alexithymia was correlated with the GM of the parietal lobe, in particular of the right
ATING DISORDERS (ED) are a group of severe psychiatric disorders, including anorexia nervosa (AN) and bulimia nervosa (BN). AN is characterized by fear of weight gain and intense drive for thinness, while BN is characterized by recurrent episodes of binge eating and compensatory behaviors. Significant
E
*Correspondence: Federico D’Agata, PhD, Department of Neuroscience, University of Turin, Via Cherasco, 15-10126 Turin, Italy. Email: [email protected] † Both authors have contributed equally to this paper. Received 29 October 2014; revised 24 April 2015; accepted 11 May 2015.
708
angular gyrus. The correlation was predominantly linked with Difficulty Describing Feelings. In AN, we did not find correlations between GM and alexithymia.
Conclusions: In BN, our results support the hypothesis that this trait may represent a relevant pathogenic or maintenance factor that contributes to relational difficulties, present in this pathology. In AN, the lack of correlation between GM volume and alexithymia may be influenced by atrophy in several brain regions that in turn can be, as previously reported, a consequence of caloric restriction. Also, the nature of alexithymia may be different from that of BN and controls and this trait could be secondary to a psychopathologic process specific to AN. Key words: alexithymia, anorexia nervosa, bulimia nervosa, eating disorders, voxel-based morphometry.
relational and emotional problems have been implicated in both disorders.1 Indeed, patients with ED demonstrate deficits in emotional voices recognition, difficulty in the integration between negative and positive emotional experiences and in the recognition of social-affective stimuli.2,3 Alexithymia is a deficit in identifying, describing and reporting one’s own and other people’s feelings. Given the link between ED and emotional problems, the link between alexithymia and ED seems likely. Indeed, several authors reported higher levels of alexithymia in AN and BN patients compared to healthy subjects.4
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
Alexithymia VBM brain correlates in ED 709
Alexithymia can be diagnosed in a clinical setting using questionnaires, such as the Toronto Alexithymia Scale (TAS-20), a self-report questionnaire commonly used in clinical settings. Multiple studies have shown that it is characterized by internal consistency (Cronbach’s α ≥ 0.7, mean inter-item correlations ≥0.1 and ≤0.4), high test–retest reliability (0.9), and construct and criterion validity, both in normal and clinical samples.5–9 Using the TAS-20, different studies demonstrated high levels of alexithymia in ED, ranging from 56% to 77%.10,11 The impairment in identifying feelings may represent a negative prognostic factor for the long-term outcome and could be a target of psychotherapy in patients with ED.12,13 It has been hypothesized that the alexithymic trait ‘forces’ the patients to use their body to communicate symptoms and it has been observed that the avoidance of emotions could be one of the core elements of the disorder.14 Therefore, treatments that work specifically on emotion recognition and sharing have been proposed15,16 and seem to show greater efficacy.17 This is of particular relevance, as there is evidence that correlates higher levels of alexithymia with a worse outcome in ED. While in healthy subjects, previous studies reported an association between higher TAS-20 scores and atrophy in the anterior cingulate cortex (ACC)18–20 and in the left middle temporal gyrus,18,21,22 in ED, the correlation between alexithymia levels and regional gray matter (GM) has not yet been investigated. Given the increasing recognition of brain alterations that are correlated to alexithymia, and the established role of this dimension of psychopathology as an obstacle in the treatment of ED,12,13,17 it appears relevant to better understand the brain correlates of alexithymia in ED. Preliminary studies in AN and BN (reviewed by Nowakowski et al.23) suggested that levels of alexithymia are different between the two pathologies. This difference suggests that dissimilar brain regions could be involved and that it is necessary to study the neuroanatomical correlates of TAS-20 in AN and BN, disjointedly. Although both disorders have high levels of alexithymia, the processing of emotions is different in the two disorders, both clinically24 and in the neural responses to stimuli in functional magnetic resonance imaging (fMRI).25 In this study, we used a voxel-based morphometry (VBM) approach to correlate the levels of alexithymia and regional GM density in patients with AN and BN in order to detect the brain areas involved. Based on previous evidences, we hypothesized that a diffuse
brain network, including the ACC, posterior cingulate cortex, amygdala, insula, and parieto-temporal cortex, is included in the anatomical substrate of alexithymia. The goal of this work was to identify: (i) the brain regions that correlated with alexithymia; and (ii) whether there were differences between AN and BN.
METHODS Participants Twenty-one patients with AN (18 restricting, three binge/purging) and 18 with BN (13 purging, five not purging), were enrolled in the study from the outpatient service of the ED Pilot Centre of the Department of Neuroscience, AOU Città della Salute e della Scienza of Turin. ED was diagnosed using the Structured Clinical Interview for Diagnosis (SCID) for DSM-IV-TR, a tool that has fair to excellent inter-rater reliability on axis I and excellent on axis II diagnoses.26 The SCID was administered by two expert psychiatrists (F.A., L.L.) with the supervision of a third (S.F.). The inclusion criteria for patients were: female sex; age ranging from 16 to 30 years; right-handed; body mass index (BMI) from 15.0 to 17.5 for AN and from 19.0 to 25.0 for BN; no past or present psychiatric or neurological diseases except for the current ED; no axis II disorders; no past or present pharmacological medication; no drug or alcohol abuse; no history of diabetes or other diseases; no past or present psychotherapy treatment; and duration of symptoms less than 2 years. Ninety-seven patients were assessed in total and 39 were finally included after exclusion of 35 patients for demographic criteria (age or BMI out of range, left handedness) and 23 for clinical criteria (other psychiatric or neurological disease, psychotherapy or pharmacological treatment, more than 2 years since symptoms onset). Seventeen healthy women were recruited as controls (CN) through local advertisement. All participants were interviewed using the SCID to rule out past or present mental disorders or ED. The general inclusion criteria for CN were the same as for patients and their BMI range was as in BN patients. All participants gave their informed written consent to the study. For subjects who were minors, written informed consent was obtained from the parents. The study was conducted according to the Helsinki Declaration and approved by the Ethical Committee of San Giovanni Battista Hospital, Turin.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
710 F. D’Agata et al.
Measures All participants filled in the TAS-20 form. The total score of the scale is the sum of the three TAS-20 subscale scores, which are defined as: (i) the Difficulty Describing Feelings (DDF) subscale; (ii) the Difficulty Identifying Feelings (DIF) subscale; and (iii) the Externally Oriented Thinking (EOT) subscale, which measures the tendency of individuals to focus their attention externally. Depression and anxiety were assessed using two self-administered questionnaires: the Beck Depression Inventory (BDI) and the Symptoms Check List 90, Anxiety subscale (SCL-90 Anxiety), respectively.
MRI acquisition MRI was acquired with a scanner at 1.5 T (Achieva, Philips, Best, the Netherlands). T1-weighted 3-D Turbo Field-Echo sequences (matrix = 256 × 256; voxel size 1 × 1 × 1 mm3; number of slices: 190; repetition time: 7 ms; echo time: 3 ms; Turbo Field Echo shots = 89) were obtained with full brain coverage and isotropic voxels, equivalent to a magnetizationprepared rapid acquisition gradient echo. Acquisition time was approximately 5 min.
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
were computed for all subjects and compared between groups. The MRIcron software (http://www .mccauslandcenter.sc.edu/mricro/mricron) with the Automated Anatomical Labeling and the Brodmann areas templates were used to find the anatomical localization of significant cluster peaks. Results were reported in the Montreal Neurological Institute coordinates system. Anxiety and depression dimensions were correlated with TAS-20 total and subscale scores to estimate their influence on alexithymia; furthermore, the alexithymia scores were compared between the three groups controlling for anxiety and depression levels. Statistical analysis was performed using SPSS 17 (SPSS, Chicago, IL, USA; http://www.spss.com): correlations between variables (Kendall tau-b) and mean comparisons among the three groups (analysis of variance [ANOVA] and analysis of covariance [ANCOVA] with age, anxiety and depression as covariates, using Tukey’s honest significant difference test for post-hoc comparison when needed). The threshold for significant results was P < 0.05.
RESULTS Clinical and sociodemographic analysis
VBM analysis Structural data were analyzed with FSL-VBM27 4.1 (FSL – FMRIB’s Software Library, The University of Oxford), an optimized VBM protocol, following the steps described in the manual (available online: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM). Voxelwise general linear model analysis was carried out using the FSL-Randomise 2.9 tool, using 5000 permutations with the threshold-free cluster enhancement option. We compared GM between groups and in each group we correlated GM with the TAS-20 (including subscales) score, age, BMI, anxiety, and depression. The TAS-20 correlations were then repeated using depression, anxiety and age as nuisance covariates. Significant results (pcorr < 0.05 corrected for multiple comparisons and P < 0.005 uncorrected), with a cluster extent (Ke) > 50 or size > 400 mm3, were reported. The correction for multiple comparisons was applied using a corrected cluster size level of pcorr < 0.05. Global GM, white matter (WM), cerebrospinal fluid (CSF) and total intracranial volumes in cm3
Clinical and sociodemographic data are summarized in Table 1. Patients and CN did not differ in age, education or Mini Mental State Examination scores. The BMI levels of patients with AN were significantly lower than those in the BN and CN groups. BDI depression scores did not significantly differ between AN and BN patients, but all patients with ED were more depressed and anxious than CN. TAS-20 total score significantly differed between patients and CN, particularly the DDF and DIF subscales (Table 1). Correlations between TAS-20, anxiety (SCL-90) and depression (BDI) are shown in Table 2. In AN, DIF correlated with anxiety and depression, and total TAS-20 score correlated with depression. In BN, DIF, EOT and total TAS-20 score correlated with anxiety, while no correlations were found in CN. No significant correlations were found among TAS-20 scores, age and BMI.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
Alexithymia VBM brain correlates in ED 711
Table 1. Demographic and clinical data Variable
AN (n = 21)
BN (n = 18)
CN (n = 17)
P
Post-hoc
Age Education Body mass index MMSE < 26% Alexithymia DIF Alexithymia DDF Alexithymia EOT Alexithymia TOT Depression Anxiety
21 ± 5 years 14 ± 2 years 16.1 ± 0.9 kg/m2 0 24 ± 7 18 ± 5 18 ± 4 60 ± 11 17 ± 10 18 ± 8
22 ± 5 years 15 ± 2 years 22.0 ± 2.3 kg/m2 0 24 ± 6 17 ± 5 20 ± 8 61 ± 16 21 ± 12 21 ± 8
23 ± 4 years 16 ± 2 years 21.5 ± 2.3 kg/m2 0 12 ± 5 11 ± 5 17 ± 6 39 ± 11 4±3 7±5
0.549 0.234 CN) and EOT did not differ among the three groups (ANCOVA, EOT P = 0.678).
There were no differences in global GM, WM or total intracranial volumes among the groups (see Table S1), but CSF volumes were increased in the anorectic patients (AN = 210 ± 21 cm3, BN = 193 ± 23 cm3, CN = 188 ± 19 cm3, P = 0.008 post-hoc AN < CN, controlling for age, anxiety and depression, P = 0.005, post-hoc AN < BN/CN).
VBM results
GM voxel-wise comparisons among groups
We looked at GM differences between groups to better understand the differences in the correlations with TAS-20 dimensions.
Significant differences were found between AN and CN (Figs 1a,S1 and Tables S2,S3), and between AN and BN (Figs 1b,S2 and Tables S4,S5).
Global GM and WM comparison among groups
Table 2. Correlations between alexithymia, depression and anxiety scales Mood
TAS-20
AN anxiety
AN depression
BN anxiety
BN depression
CN anxiety
CN depression
Alexithymia DIF Alexithymia DDF Alexithymia EOT TAS-20
0.537* 0.037 −0.291 0.222
0.441* 0.293 −0.003 0.487*
0.718** 0.247 0.489* 0.689**
0.355 −0.155 0.426 0.289
0.195 0.217 −0.308 −0.018
0.184 −0.373 0.430 −0.032
Spearman rho, in bold *P < 0.05, **P < 0.01. AN, anorexia nervosa; BN, bulimia nervosa; CN, healthy controls; DDF, Difficulty Describing Feelings; DIF, Difficulty Identifying Feelings; EOT, Externally Oriented Thinking; TAS-20, Toronto Alexithymia Scale.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
712 F. D’Agata et al.
(a)
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
R
AN < CN
0.95
1 - pcorr
1.00
R (b)
L AN < BN
0.95
1 - pcorr
1.00
The AN group showed a diffuse atrophy in frontotemporo-parietal cortices, with a greater medial superior involvement, when compared to CN (Figs S1,S2). Also, the thalamus and cerebellum showed differences. Atrophy is wider and with the cerebellar anterior–superior areas more involved in the comparison with CN than in the comparison with BN (Figs S1,S2). Some of these results were significant with pcorr < 0.05 corrected for multiple comparisons, in particular the supplementary motor area and the bilateral cerebellum (HVI) in AN < CN (Fig. 1a and Table S3) and the bilateral cerebellum (more posterior–inferior HVIII) in AN < BN (Fig. 1b and Table S5). Significant differences were found between BN and CN in the bilateral caudate (Fig. S3 and Table S6). This result did not survive to multiple comparisons correction.
GM correlations in AN, BN and CN In AN and CN, no significant correlations between GM and alexithymia survived to multiple comparisons correction (see Supplementary Results, Figs S4–S9 and Tables S7–S14). In BN, the DDF score inversely correlated with several brain regions (Fig. 2 and Table S9,S10): bilateral postcentral gyrus, occipital gyrus, bilateral insula, cingulate, cerebellum, amygdala and parahippocampal gyrus, and some other clusters in the frontal,
L
Figure 1. Atrophy pattern of patients with anorexia. (a) In red (pcorr < 0.05), gray matter (GM) volumetric reduction in anorexia nervosa (AN), compared to healthy controls (CN) in bilateral cerebellum and supplementary motor area. (b) In blue (pcorr < 0.05), volumetric GM reduction in AN compared to bulimia nervosa (BN) in posterior–inferior cerebellum. L, left; R, right.
temporal and parietal lobes. The DDF clusters (right angular gyrus and nearby parietal areas) survived to multiple comparisons.
Result changes controlling for confounding variables Corrected probability of VBM results did not change after controlling for anxiety and depression, except for small changes in the extension of the clusters.
DISCUSSION In this study, we assessed the brain correlates of alexithymia in patients with ED using a VBM approach. As expected, patients showed high levels of alexithymia. In particular, the DIF and DDF subscales were significantly higher in ED subjects (Table 1). These findings support previous observations of a lesser stability and reliability of the EOT subscale score.28 DIF and DDF alterations could reflect a disturbance in emotional processing rather than problems in thought orientation. In this study, alexithymia correlated with depression and anxiety in our patients (Table 2), confirming an altered emotional processing13,29 and we confirmed our previous results on GM atrophy in ED patients.30 In patients with BN, the pattern of inverse correlation of GM and DDF was very extensive, including several brain regions in frontal, temporal and parietal lobes. The DDF dimension of alexithymia was less
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
Alexithymia VBM brain correlates in ED 713
BN µ DDF
Figure 2. Brain correlates with alexithymia in patients with bulimia. Correlation (P < 0.005 uncorrected in blue, pcorr < 0.05 in magenta) between gray matter volume and Difficult to Describe Feelings (DDF) in patients with bulimia nervosa (BN). L, left; R, right.
L
0.995
influenced by anxiety and depression levels (Table 2), supporting the hypothesis that it is the dominant dimension of alexithymia in BN. The difficulty of describing emotions in BN involves different functional areas related to emotions’ perception, identifying, association, storage and processing, such as amygdala, insula, cingulate cortex, parietal lobe and temporal cortex. Our results supported the hypothesis that alexithymia impairs the complex integration between cognition and emotion and may represent a relevant pathogenic or maintenance factor31 related to particular relational difficulties.32 Among these areas, the angular gyrus seems to represent the most significant. This region is involved in semantic processing, attention and social cognition and is a central node for the integration of multisensory information.33 Our results are supported by a previous study in healthy female subjects by Borsci and co-workers,18 which found a correlation of alexithymia and several anterior, middle frontal and temporal areas that are critical in emotion processing, such as the cingulate cortex, particularly the ACC.20,34 Previous fMRI studies showed a dysfunction of the ACC and of its connected areas during emotional stimuli processing.35 ACC is a key hub connected with many brain areas that we found that are part of emotional, reward, and interoceptive networks, such as the amygdala, nucleus accumbens and insula.20 ACC is also involved in many physiological processes through connections to the hypothalamus, which is altered in ED.36
1-p
1.000
1 - pcorr > 0.95
R
Other remarkable results were the insula and cerebellum, important for the integration of body interoception and emotions’ processing.37,38 In patients with AN, no correlations were found between alexithymia and GM. Some authors found brain regions atrophy in AN and interpreted it as a consequence of caloric restriction.39 In the present study, many regions that correlated with alexithymia in BN were atrophic in AN (e.g. parieto-temporal areas, ACC30). Consequently, the lack of correlation between GM volume and alexithymia may be influenced by these anatomical changes.40 Another explanation of the lack of correlation could be that the origin of alexithymia in women with AN is different from that of subjects with BN and CN. In particular, in AN this trait could be secondary to specific psychopathologic process. Some researchers found an inconsistency between the self-reported emotional disturbances and the results of the assessment of meta-emotional processing in subjects with AN. Hence, patients suffering from AN maintain a substantial ability in reading others’ emotions and sufficient or even good metacognitive skills,41 but they may be overridden by the pathologic over-control of worry that is typical of these subjects.42 According to this hypothesis, at least two processes may produce high alexithymia and should be accounted for in the assessment of subjects with ED. Also, the correlation pattern of TAS-20 with the other self-administered scales in subjects with AN were different from that of subjects with BN and CN (see Table 2). In particular, the correlation of DIF with
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
714 F. D’Agata et al.
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
depression in AN, but not in BN and CN, would further support the different nature of this trait. Nevertheless further studies with larger samples and a comprehensive psychometric assessment are necessary to confirm this interpretation. The correlations between psychopathological measures in our data substantially confirm previous evidences of a strong relation of alexithymia with anxiety and depression in ED.43 Nevertheless, the absence of any significant correlation between anxiety or depression with GM contradicts the hypothesis that these psychopathology traits are the primary determinants of alexithymia. As alexithymia displays an autonomous and specific anatomical substrate, the emotional disturbances may represent core features or possibly prodromal aspects of anxiety and depressive disorders, at least in patients with BN. Cognitive behavior therapy (CBT) and interpersonal psychotherapy are the therapeutic approaches that have received the most empirical evidence for ED. In particular, in BN, CBT seems to achieve better and quicker results,44 while in AN, no specific approach has shown superiority45 and a combination of nutritional therapy and psychotherapy, such as specialist supportive clinical management, CBT, and interpersonal psychotherapy, is recommended.46 Our results could support the need for therapies that work on emotions and relationships. In AN, approaches with an adjunctive focus on emotional processing have not shown superiority to standard treatment, and nor have they shown superior outcomes in the specific measures of emotion functionality.16,47 Our results might offer some insight into why simple or short emotional training is often ineffective in patients with AN and might suggest that a better understanding of the neurobiological underpinnings of alexithymia is needed to develop more effective treatments that can address this important component. In BN, a careful comparison between CBT with and without adjunctive focus on emotions needs to be performed.
Our results should be considered with caution for the AN group, as GM atrophy could be a relevant confounding factor in the correlation analyses.
Limitations Alexithymia distribution in a population with ED is not homogeneous;48 therefore, our results obtained in a limited sample should be confirmed with a larger sample in future studies. Moreover, our strict inclusion and exclusion criteria could be a potential limitation to the generalizability of our results to the entire population.
Conclusions The role of alexithymia in the pathogenesis and expression of mental disorders is still under debate,49 and more than one alexithymia subtype may be found in ED. In BN, the alexithymic traits relate to a specific brain network, while no such relation was recognizable in AN: in these patients, alexithymia scores may be a consequence of an active strategy of self-isolation from feelings.29 These different patterns of correlations suggest that the brain functionality of these disorders is partly distinguishable, as previously evidenced.50 The need for personalized therapeutic approaches to alexithymia in ED could be useful: subjects with BN could benefit from skill training to lower alexithymia levels, but in subjects with AN, it could be necessary to address a primary psychopathological factor underlying alexithymia instead of targeting it directly during treatment. A psychotherapy approach respectful of the emotional inhibition underlying the disorder could be more beneficial.
ACKNOWLEDGMENT The authors thank Steve Valeri for his contribution in editing the manuscript. The authors have no competing interests to report.
REFERENCES 1. Caglar-Nazali HP, Corfield F, Cardi V et al. A systematic review and meta-analysis of ‘Systems for Social Processes’ in eating disorders. Neurosci. Biobehav. Rev. 2014; 42: 55–92. 2. Kucharska-Pietura K, Nikolaou V, Masiak M, Treasure J. The recognition of emotion in the faces and voice of anorexia nervosa. Int. J. Eat. Disord. 2004; 35: 42–47. 3. Nandrino J-L, Doba K, Lesne A, Christophe V, Pezard L. Autobiographical memory deficit in anorexia nervosa: Emotion regulation and effect of duration of illness. J. Psychosom. Res. 2006; 61: 537–543. 4. Speranza M, Loas G, Wallier J, Corcos M. Predictive value of alexithymia in patients with eating disorders: A 3-year prospective study. J. Psychosom. Res. 2007; 63: 365–371. 5. Parker JDA, Taylor GJ, Bagby RM. The 20-Item Toronto Alexithymia Scale: III. Reliability and factorial validity in a community population. J. Psychosom. Res. 2003; 55: 269– 275.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
Alexithymia VBM brain correlates in ED 715
6. Taylor GJ, Bagby RM, Parker JDA. The 20-Item Toronto Alexithymia Scale: IV. Reliability and factorial validity in different languages and cultures. J. Psychosom. Res. 2003; 55: 277–283. 7. Bagby RM, Taylor GJ, Parker JD. The Twenty-item Toronto Alexithymia Scale – II. Convergent, discriminant, and concurrent validity. J. Psychosom. Res. 1994; 38: 33–40. 8. Bagby RM, Parker JD, Taylor GJ. The twenty-item Toronto Alexithymia Scale – I. Item selection and cross-validation of the factor structure. J. Psychosom. Res. 1994; 38: 23–32. 9. Bressi C, Taylor G, Parker J et al. Cross validation of the factor structure of the 20-item Toronto Alexithymia Scale: An Italian multicenter study. J. Psychosom. Res. 1996; 41: 551–559. 10. Espina Eizaguirre A, Ortego Saenz de Cabezón A, Ochoa de Alda I, Joaristi Olariaga L, Juaniz M. Alexithymia and its relationships with anxiety and depression in eating disorders. Pers. Individ. Dif. 2004; 36: 321–331. 11. Zonnevylle-Bender MJS, van Goozen SHM, CohenKettenis PT, van Elburg TA, van Engeland H. Emotional functioning in adolescent anorexia nervosa patients – a controlled study. Eur. Child Adolesc. Psychiatry 2004; 13: 28–34. 12. Speranza M, Loas G, Guilbaud O, Corcos M. Are treatment options related to alexithymia in eating disorders? Results from a three-year naturalistic longitudinal study. Biomed. Pharmacother. 2011; 65: 585–589. 13. Abbate-Daga G, Delsedime N, Nicotra B et al. Psychosomatic syndromes and anorexia nervosa. BMC Psychiatry 2013; 13: 14. 14. Treasure J. Emotion in eating disorders. Eur. Eat. Disord. Rev. 2012; 20: 429–430. 15. Wildes JE, Marcus MD. Development of emotion acceptance behavior therapy for anorexia nervosa: A case series. Int. J. Eat. Disord. 2011; 44: 421–427. 16. Davies H, Fox J, Naumann U, Treasure J, Schmidt U, Tchanturia K. Cognitive remediation and emotion skills training for anorexia nervosa: An observational study using neuropsychological outcomes. Eur. Eat. Disord. Rev. 2012; 20: 211–217. 17. Pinna F, Sanna L, Carpiniello B. Alexithymia in eating disorders: Therapeutic implications. Psychol. Res. Behav. Manag. 2015; 8: 1–15. 18. Borsci G, Boccardi M, Rossi R et al. Alexithymia in healthy women: A brain morphology study. J. Affect. Disord. 2009; 114: 208–215. 19. Gündel H, López-Sala A, Ceballos-Baumann AO et al. Alexithymia correlates with the size of the right anterior cingulate. Psychosom. Med. 2004; 66: 132–140. 20. Grabe HJ, Wittfeld K, Hegenscheid K et al. Alexithymia and brain gray matter volumes in a general population sample. Hum. Brain Mapp. 2014; 35: 5932–5945. 21. Ihme K, Dannlowski U, Lichev V et al. Alexithymia is related to differences in gray matter volume: A voxel-based morphometry study. Brain Res. 2013; 1491: 60–67.
22. Heinzel A, Minnerop M, Schäfer R, Müller HW, Franz M, Hautzel H. Alexithymia in healthy young men: A voxelbased morphometric study. J. Affect. Disord. 2012; 136: 1252–1256. 23. Nowakowski ME, McFarlane T, Cassin S. Alexithymia and eating disorders: A critical review of the literature. J. Eat. Disord. 2013; 1: 21. 24. Krug I, Root T, Bulik C et al. Redefining phenotypes in eating disorders based on personality: A latent profile analysis. Psychiatry Res. 2011; 188: 439–445. 25. Brooks SJ, O’Daly OG, Uher R et al. Differential neural responses to food images in women with bulimia versus anorexia nervosa. PLoS ONE 2011; 6: e22259. 26. Lobbestael J, Leurgans M, Arntz A. Inter-rater reliability of the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID I) and Axis II Disorders (SCID II). Clin. Psychol. Psychother. 2011; 18: 75–79. 27. Douaud G, Smith S, Jenkinson M et al. Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 2007; 130: 2375– 2386. 28. Culhane SE, Morera OF, Watson PJ, Millsap RE. Assessing measurement and predictive invariance of the Toronto Alexithymia Scale-20 in U.S. Anglo and U.S. Hispanic student samples. J. Pers. Assess. 2009; 91: 387–395. 29. Parling T, Mortazavi M, Ghaderi A. Alexithymia and emotional awareness in anorexia nervosa: Time for a shift in the measurement of the concept? Eat. Behav. 2010; 11: 205–210. 30. Amianto F, Caroppo P, D’Agata F et al. Brain volumetric abnormalities in patients with anorexia and bulimia nervosa: A voxel-based morphometry study. Psychiatry Res. 2013; 213: 210–216. 31. De Groot JM, Rodin G, Olmsted MP. Alexithymia, depression, and treatment outcome in bulimia nervosa. Compr. Psychiatry 1995; 36: 53–60. 32. Amianto F, Siccardi S, Abbate-Daga G, Marech L, Barosio M, Fassino S. Does anger mediate between personality and eating symptoms in bulimia nervosa? Psychiatry Res. 2012; 200: 502–512. 33. Seghier ML. The angular gyrus: Multiple functions and multiple subdivisions. Neuroscientist 2013; 19: 43–61. 34. Adolphs R. Neural systems for recognizing emotion. Curr. Opin. Neurobiol. 2002; 12: 169–177. 35. Kugel H, Eichmann M, Dannlowski U et al. Alexithymic features and automatic amygdala reactivity to facial emotion. Neurosci. Lett. 2008; 435: 40–44. 36. Hudson JI, Hudson MS. Endocrine dysfunction in anorexia nervosa and bulimia: Comparison with abnormalities in other psychiatric disorders and disturbances due to metabolic factors. Psychiatr. Dev. 1984; 2: 237–272. 37. Brooks SJ, Rask-Andersen M, Benedict C, Schiöth HB. A debate on current eating disorder diagnoses in light of neurobiological findings: Is it time for a spectrum model? BMC Psychiatry 2012; 12: 76.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
716 F. D’Agata et al.
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
38. Magal A. A hypothetical universal model of cerebellar function: Reconsideration of the current dogma. Cerebellum 2013; 12: 758–772. 39. Boghi A, Sterpone S, Sales S et al. In vivo evidence of global and focal brain alterations in anorexia nervosa. Psychiatry Res. 2011; 192: 154–159. 40. Mattar L, Huas C, Duclos J, Apfel A, Godart N. Relationship between malnutrition and depression or anxiety in anorexia nervosa: A critical review of the literature. J. Affect. Disord. 2011; 132: 311–318. 41. Woolrich RA, Cooper MJ, Turner HM. Metacognition in patients with anorexia nervosa, dieting and non-dieting women: A preliminary study. Eur. Eat. Disord. Rev. 2008; 16: 11–20. 42. Startup H, Lavender A, Oldershaw A et al. Worry and rumination in anorexia nervosa. Behav. Cogn. Psychother. 2013; 41: 301–316. 43. Montebarocci O, Codispoti M, Surcinelli P, Franzoni E, Baldaro B, Rossi N. Alexithymia in female patients with eating disorders. Eat. Weight Disord. 2006; 11: 14–21. 44. Poulsen S, Lunn S, Daniel SIF et al. A randomized controlled trial of psychoanalytic psychotherapy or cognitivebehavioral therapy for bulimia nervosa. Am. J. Psychiatry 2014; 171: 109–116. 45. Zipfel S, Wild B, Grob G et al. Focal psychodynamic therapy, cognitive behaviour therapy, and optimised treatment as usual in outpatients with anorexia nervosa (ANTOP study): Randomised controlled trial. Lancet 2014; 383: 127–137. 46. Watson HJ, Bulik CM. Update on the treatment of anorexia nervosa: Review of clinical trials, practice guidelines and emerging interventions. Psychol. Med. 2013; 43: 2477–2500. 47. Dalle Grave R, Calugi S, Conti M, Doll H, Fairburn CG. Inpatient cognitive behaviour therapy for anorexia nervosa: A randomized controlled trial. Psychother. Psychosom. 2013; 82: 390–398. 48. Speranza M, Corcos M, Loas G et al. Depressive personality dimensions and alexithymia in eating disorders. Psychiatry Res. 2005; 135: 153–163. 49. Joyce AS, Fujiwara E, Cristall M, Ruddy C, Ogrodniczuk JS. Clinical correlates of alexithymia among patients with personality disorder. Psychother. Res. 2013; 23: 690–704. 50. Amianto F, D’Agata F, Lavagnino L et al. Intrinsic connectivity networks within cerebellum and beyond in eating disorders. Cerebellum 2013; 12: 623–631.
Figure S1. Gray matter difference: Anorexia < healthy controls. Figure S2. Gray matter difference: Anorexia < bulimia. Figure S3. Gray matter difference: Bulimia < healthy controls. Figure S4. Anorexia gray matter negative correlation with age. Figure S5. Anorexia gray matter positive correlation with body mass index. Figure S6. Bulimia gray matter negative correlation with age. Figure S7. Controls gray matter negative correlation with anxiety. Figure S8. Controls gray matter negative correlation with depression. Figure S9. Controls gray matter negative correlation with alexithymia. Table S1. Global volumetric comparisons between groups. Table S2. Gray matter difference: Anorexia < healthy controls. Table S3. Corrected gray matter difference: Anorexia < healthy controls. Table S4. Gray matter difference: Anorexia < bulimia. Table S5. Corrected gray matter difference: Anorexia < bulimia. Table S6. Gray matter difference: Bulimia < healthy controls. Table S7. Anorexia gray matter negative correlation with age. Table S8. Anorexia gray matter correlation with body mass index. Table S9. Bulimia gray matter negative correlation with Difficulty to Describe Feelings. Table S10. Corrected bulimia gray matter negative correlation with Difficulty to Describe Feelings. Table S11. Bulimia gray matter negative correlation with age. Table S12. Controls gray matter negative correlation with anxiety. Table S13. Controls gray matter negative correlation with depression. Table S14. Controls gray matter negative correlation with alexithymia.
SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher’s web-site:
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
doi:10.1111/pcn.12318
Regular Article
Brain correlates of alexithymia in eating disorders: A voxel-based morphometry study Federico D’Agata, PhD,1*† Paola Caroppo, MD, PhD,1,3† Federico Amianto, MD, PhD,1 Angela Spalatro, MD,1 Marcella M. Caglio, PhD,1 Mauro Bergui, MD,1,2 Luca Lavagnino, MD, PhD,4 Dorico Righi, MD,2 Giovanni Abbate-Daga, MD,1 Lorenzo Pinessi, MD,1 Paolo Mortara, MD1 and Secondo Fassino, MD1 1
Department of Neuroscience, University of Turin, 2Radiology Section, AOU Città della Salute e della Scienza, Turin, Besta Neurological Institute, Milan, Italy, and 4Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center (UTHealth), Houston, USA 3
Aims: Alexithymia is a personality trait that consists of difficulty in identifying and acknowledging one’s own and others’ feelings. Recent studies reported that alexithymia is present in both anorexia (AN) and bulimia nervosa (BN). Brain morphological studies on healthy subjects showed that alexithymia correlates with several brain regions involved in emotions processing. The aim of this study was to investigate the anatomical correlates of alexithymia in AN and BN. Methods: We performed a voxel-based morphometry study on 21 patients with AN and 18 with BN. Seventeen healthy subjects were used as a control group. Alexithymia, depression and anxiety were assessed with self-administered questionnaires and correlated to gray matter (GM) density in each group. Results: In BN, alexithymia was correlated with the GM of the parietal lobe, in particular of the right
ATING DISORDERS (ED) are a group of severe psychiatric disorders, including anorexia nervosa (AN) and bulimia nervosa (BN). AN is characterized by fear of weight gain and intense drive for thinness, while BN is characterized by recurrent episodes of binge eating and compensatory behaviors. Significant
E
*Correspondence: Federico D’Agata, PhD, Department of Neuroscience, University of Turin, Via Cherasco, 15-10126 Turin, Italy. Email: [email protected] † Both authors have contributed equally to this paper. Received 29 October 2014; revised 24 April 2015; accepted 11 May 2015.
708
angular gyrus. The correlation was predominantly linked with Difficulty Describing Feelings. In AN, we did not find correlations between GM and alexithymia.
Conclusions: In BN, our results support the hypothesis that this trait may represent a relevant pathogenic or maintenance factor that contributes to relational difficulties, present in this pathology. In AN, the lack of correlation between GM volume and alexithymia may be influenced by atrophy in several brain regions that in turn can be, as previously reported, a consequence of caloric restriction. Also, the nature of alexithymia may be different from that of BN and controls and this trait could be secondary to a psychopathologic process specific to AN. Key words: alexithymia, anorexia nervosa, bulimia nervosa, eating disorders, voxel-based morphometry.
relational and emotional problems have been implicated in both disorders.1 Indeed, patients with ED demonstrate deficits in emotional voices recognition, difficulty in the integration between negative and positive emotional experiences and in the recognition of social-affective stimuli.2,3 Alexithymia is a deficit in identifying, describing and reporting one’s own and other people’s feelings. Given the link between ED and emotional problems, the link between alexithymia and ED seems likely. Indeed, several authors reported higher levels of alexithymia in AN and BN patients compared to healthy subjects.4
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
Alexithymia VBM brain correlates in ED 709
Alexithymia can be diagnosed in a clinical setting using questionnaires, such as the Toronto Alexithymia Scale (TAS-20), a self-report questionnaire commonly used in clinical settings. Multiple studies have shown that it is characterized by internal consistency (Cronbach’s α ≥ 0.7, mean inter-item correlations ≥0.1 and ≤0.4), high test–retest reliability (0.9), and construct and criterion validity, both in normal and clinical samples.5–9 Using the TAS-20, different studies demonstrated high levels of alexithymia in ED, ranging from 56% to 77%.10,11 The impairment in identifying feelings may represent a negative prognostic factor for the long-term outcome and could be a target of psychotherapy in patients with ED.12,13 It has been hypothesized that the alexithymic trait ‘forces’ the patients to use their body to communicate symptoms and it has been observed that the avoidance of emotions could be one of the core elements of the disorder.14 Therefore, treatments that work specifically on emotion recognition and sharing have been proposed15,16 and seem to show greater efficacy.17 This is of particular relevance, as there is evidence that correlates higher levels of alexithymia with a worse outcome in ED. While in healthy subjects, previous studies reported an association between higher TAS-20 scores and atrophy in the anterior cingulate cortex (ACC)18–20 and in the left middle temporal gyrus,18,21,22 in ED, the correlation between alexithymia levels and regional gray matter (GM) has not yet been investigated. Given the increasing recognition of brain alterations that are correlated to alexithymia, and the established role of this dimension of psychopathology as an obstacle in the treatment of ED,12,13,17 it appears relevant to better understand the brain correlates of alexithymia in ED. Preliminary studies in AN and BN (reviewed by Nowakowski et al.23) suggested that levels of alexithymia are different between the two pathologies. This difference suggests that dissimilar brain regions could be involved and that it is necessary to study the neuroanatomical correlates of TAS-20 in AN and BN, disjointedly. Although both disorders have high levels of alexithymia, the processing of emotions is different in the two disorders, both clinically24 and in the neural responses to stimuli in functional magnetic resonance imaging (fMRI).25 In this study, we used a voxel-based morphometry (VBM) approach to correlate the levels of alexithymia and regional GM density in patients with AN and BN in order to detect the brain areas involved. Based on previous evidences, we hypothesized that a diffuse
brain network, including the ACC, posterior cingulate cortex, amygdala, insula, and parieto-temporal cortex, is included in the anatomical substrate of alexithymia. The goal of this work was to identify: (i) the brain regions that correlated with alexithymia; and (ii) whether there were differences between AN and BN.
METHODS Participants Twenty-one patients with AN (18 restricting, three binge/purging) and 18 with BN (13 purging, five not purging), were enrolled in the study from the outpatient service of the ED Pilot Centre of the Department of Neuroscience, AOU Città della Salute e della Scienza of Turin. ED was diagnosed using the Structured Clinical Interview for Diagnosis (SCID) for DSM-IV-TR, a tool that has fair to excellent inter-rater reliability on axis I and excellent on axis II diagnoses.26 The SCID was administered by two expert psychiatrists (F.A., L.L.) with the supervision of a third (S.F.). The inclusion criteria for patients were: female sex; age ranging from 16 to 30 years; right-handed; body mass index (BMI) from 15.0 to 17.5 for AN and from 19.0 to 25.0 for BN; no past or present psychiatric or neurological diseases except for the current ED; no axis II disorders; no past or present pharmacological medication; no drug or alcohol abuse; no history of diabetes or other diseases; no past or present psychotherapy treatment; and duration of symptoms less than 2 years. Ninety-seven patients were assessed in total and 39 were finally included after exclusion of 35 patients for demographic criteria (age or BMI out of range, left handedness) and 23 for clinical criteria (other psychiatric or neurological disease, psychotherapy or pharmacological treatment, more than 2 years since symptoms onset). Seventeen healthy women were recruited as controls (CN) through local advertisement. All participants were interviewed using the SCID to rule out past or present mental disorders or ED. The general inclusion criteria for CN were the same as for patients and their BMI range was as in BN patients. All participants gave their informed written consent to the study. For subjects who were minors, written informed consent was obtained from the parents. The study was conducted according to the Helsinki Declaration and approved by the Ethical Committee of San Giovanni Battista Hospital, Turin.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
710 F. D’Agata et al.
Measures All participants filled in the TAS-20 form. The total score of the scale is the sum of the three TAS-20 subscale scores, which are defined as: (i) the Difficulty Describing Feelings (DDF) subscale; (ii) the Difficulty Identifying Feelings (DIF) subscale; and (iii) the Externally Oriented Thinking (EOT) subscale, which measures the tendency of individuals to focus their attention externally. Depression and anxiety were assessed using two self-administered questionnaires: the Beck Depression Inventory (BDI) and the Symptoms Check List 90, Anxiety subscale (SCL-90 Anxiety), respectively.
MRI acquisition MRI was acquired with a scanner at 1.5 T (Achieva, Philips, Best, the Netherlands). T1-weighted 3-D Turbo Field-Echo sequences (matrix = 256 × 256; voxel size 1 × 1 × 1 mm3; number of slices: 190; repetition time: 7 ms; echo time: 3 ms; Turbo Field Echo shots = 89) were obtained with full brain coverage and isotropic voxels, equivalent to a magnetizationprepared rapid acquisition gradient echo. Acquisition time was approximately 5 min.
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
were computed for all subjects and compared between groups. The MRIcron software (http://www .mccauslandcenter.sc.edu/mricro/mricron) with the Automated Anatomical Labeling and the Brodmann areas templates were used to find the anatomical localization of significant cluster peaks. Results were reported in the Montreal Neurological Institute coordinates system. Anxiety and depression dimensions were correlated with TAS-20 total and subscale scores to estimate their influence on alexithymia; furthermore, the alexithymia scores were compared between the three groups controlling for anxiety and depression levels. Statistical analysis was performed using SPSS 17 (SPSS, Chicago, IL, USA; http://www.spss.com): correlations between variables (Kendall tau-b) and mean comparisons among the three groups (analysis of variance [ANOVA] and analysis of covariance [ANCOVA] with age, anxiety and depression as covariates, using Tukey’s honest significant difference test for post-hoc comparison when needed). The threshold for significant results was P < 0.05.
RESULTS Clinical and sociodemographic analysis
VBM analysis Structural data were analyzed with FSL-VBM27 4.1 (FSL – FMRIB’s Software Library, The University of Oxford), an optimized VBM protocol, following the steps described in the manual (available online: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM). Voxelwise general linear model analysis was carried out using the FSL-Randomise 2.9 tool, using 5000 permutations with the threshold-free cluster enhancement option. We compared GM between groups and in each group we correlated GM with the TAS-20 (including subscales) score, age, BMI, anxiety, and depression. The TAS-20 correlations were then repeated using depression, anxiety and age as nuisance covariates. Significant results (pcorr < 0.05 corrected for multiple comparisons and P < 0.005 uncorrected), with a cluster extent (Ke) > 50 or size > 400 mm3, were reported. The correction for multiple comparisons was applied using a corrected cluster size level of pcorr < 0.05. Global GM, white matter (WM), cerebrospinal fluid (CSF) and total intracranial volumes in cm3
Clinical and sociodemographic data are summarized in Table 1. Patients and CN did not differ in age, education or Mini Mental State Examination scores. The BMI levels of patients with AN were significantly lower than those in the BN and CN groups. BDI depression scores did not significantly differ between AN and BN patients, but all patients with ED were more depressed and anxious than CN. TAS-20 total score significantly differed between patients and CN, particularly the DDF and DIF subscales (Table 1). Correlations between TAS-20, anxiety (SCL-90) and depression (BDI) are shown in Table 2. In AN, DIF correlated with anxiety and depression, and total TAS-20 score correlated with depression. In BN, DIF, EOT and total TAS-20 score correlated with anxiety, while no correlations were found in CN. No significant correlations were found among TAS-20 scores, age and BMI.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
Alexithymia VBM brain correlates in ED 711
Table 1. Demographic and clinical data Variable
AN (n = 21)
BN (n = 18)
CN (n = 17)
P
Post-hoc
Age Education Body mass index MMSE < 26% Alexithymia DIF Alexithymia DDF Alexithymia EOT Alexithymia TOT Depression Anxiety
21 ± 5 years 14 ± 2 years 16.1 ± 0.9 kg/m2 0 24 ± 7 18 ± 5 18 ± 4 60 ± 11 17 ± 10 18 ± 8
22 ± 5 years 15 ± 2 years 22.0 ± 2.3 kg/m2 0 24 ± 6 17 ± 5 20 ± 8 61 ± 16 21 ± 12 21 ± 8
23 ± 4 years 16 ± 2 years 21.5 ± 2.3 kg/m2 0 12 ± 5 11 ± 5 17 ± 6 39 ± 11 4±3 7±5
0.549 0.234 CN) and EOT did not differ among the three groups (ANCOVA, EOT P = 0.678).
There were no differences in global GM, WM or total intracranial volumes among the groups (see Table S1), but CSF volumes were increased in the anorectic patients (AN = 210 ± 21 cm3, BN = 193 ± 23 cm3, CN = 188 ± 19 cm3, P = 0.008 post-hoc AN < CN, controlling for age, anxiety and depression, P = 0.005, post-hoc AN < BN/CN).
VBM results
GM voxel-wise comparisons among groups
We looked at GM differences between groups to better understand the differences in the correlations with TAS-20 dimensions.
Significant differences were found between AN and CN (Figs 1a,S1 and Tables S2,S3), and between AN and BN (Figs 1b,S2 and Tables S4,S5).
Global GM and WM comparison among groups
Table 2. Correlations between alexithymia, depression and anxiety scales Mood
TAS-20
AN anxiety
AN depression
BN anxiety
BN depression
CN anxiety
CN depression
Alexithymia DIF Alexithymia DDF Alexithymia EOT TAS-20
0.537* 0.037 −0.291 0.222
0.441* 0.293 −0.003 0.487*
0.718** 0.247 0.489* 0.689**
0.355 −0.155 0.426 0.289
0.195 0.217 −0.308 −0.018
0.184 −0.373 0.430 −0.032
Spearman rho, in bold *P < 0.05, **P < 0.01. AN, anorexia nervosa; BN, bulimia nervosa; CN, healthy controls; DDF, Difficulty Describing Feelings; DIF, Difficulty Identifying Feelings; EOT, Externally Oriented Thinking; TAS-20, Toronto Alexithymia Scale.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
712 F. D’Agata et al.
(a)
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
R
AN < CN
0.95
1 - pcorr
1.00
R (b)
L AN < BN
0.95
1 - pcorr
1.00
The AN group showed a diffuse atrophy in frontotemporo-parietal cortices, with a greater medial superior involvement, when compared to CN (Figs S1,S2). Also, the thalamus and cerebellum showed differences. Atrophy is wider and with the cerebellar anterior–superior areas more involved in the comparison with CN than in the comparison with BN (Figs S1,S2). Some of these results were significant with pcorr < 0.05 corrected for multiple comparisons, in particular the supplementary motor area and the bilateral cerebellum (HVI) in AN < CN (Fig. 1a and Table S3) and the bilateral cerebellum (more posterior–inferior HVIII) in AN < BN (Fig. 1b and Table S5). Significant differences were found between BN and CN in the bilateral caudate (Fig. S3 and Table S6). This result did not survive to multiple comparisons correction.
GM correlations in AN, BN and CN In AN and CN, no significant correlations between GM and alexithymia survived to multiple comparisons correction (see Supplementary Results, Figs S4–S9 and Tables S7–S14). In BN, the DDF score inversely correlated with several brain regions (Fig. 2 and Table S9,S10): bilateral postcentral gyrus, occipital gyrus, bilateral insula, cingulate, cerebellum, amygdala and parahippocampal gyrus, and some other clusters in the frontal,
L
Figure 1. Atrophy pattern of patients with anorexia. (a) In red (pcorr < 0.05), gray matter (GM) volumetric reduction in anorexia nervosa (AN), compared to healthy controls (CN) in bilateral cerebellum and supplementary motor area. (b) In blue (pcorr < 0.05), volumetric GM reduction in AN compared to bulimia nervosa (BN) in posterior–inferior cerebellum. L, left; R, right.
temporal and parietal lobes. The DDF clusters (right angular gyrus and nearby parietal areas) survived to multiple comparisons.
Result changes controlling for confounding variables Corrected probability of VBM results did not change after controlling for anxiety and depression, except for small changes in the extension of the clusters.
DISCUSSION In this study, we assessed the brain correlates of alexithymia in patients with ED using a VBM approach. As expected, patients showed high levels of alexithymia. In particular, the DIF and DDF subscales were significantly higher in ED subjects (Table 1). These findings support previous observations of a lesser stability and reliability of the EOT subscale score.28 DIF and DDF alterations could reflect a disturbance in emotional processing rather than problems in thought orientation. In this study, alexithymia correlated with depression and anxiety in our patients (Table 2), confirming an altered emotional processing13,29 and we confirmed our previous results on GM atrophy in ED patients.30 In patients with BN, the pattern of inverse correlation of GM and DDF was very extensive, including several brain regions in frontal, temporal and parietal lobes. The DDF dimension of alexithymia was less
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
Alexithymia VBM brain correlates in ED 713
BN µ DDF
Figure 2. Brain correlates with alexithymia in patients with bulimia. Correlation (P < 0.005 uncorrected in blue, pcorr < 0.05 in magenta) between gray matter volume and Difficult to Describe Feelings (DDF) in patients with bulimia nervosa (BN). L, left; R, right.
L
0.995
influenced by anxiety and depression levels (Table 2), supporting the hypothesis that it is the dominant dimension of alexithymia in BN. The difficulty of describing emotions in BN involves different functional areas related to emotions’ perception, identifying, association, storage and processing, such as amygdala, insula, cingulate cortex, parietal lobe and temporal cortex. Our results supported the hypothesis that alexithymia impairs the complex integration between cognition and emotion and may represent a relevant pathogenic or maintenance factor31 related to particular relational difficulties.32 Among these areas, the angular gyrus seems to represent the most significant. This region is involved in semantic processing, attention and social cognition and is a central node for the integration of multisensory information.33 Our results are supported by a previous study in healthy female subjects by Borsci and co-workers,18 which found a correlation of alexithymia and several anterior, middle frontal and temporal areas that are critical in emotion processing, such as the cingulate cortex, particularly the ACC.20,34 Previous fMRI studies showed a dysfunction of the ACC and of its connected areas during emotional stimuli processing.35 ACC is a key hub connected with many brain areas that we found that are part of emotional, reward, and interoceptive networks, such as the amygdala, nucleus accumbens and insula.20 ACC is also involved in many physiological processes through connections to the hypothalamus, which is altered in ED.36
1-p
1.000
1 - pcorr > 0.95
R
Other remarkable results were the insula and cerebellum, important for the integration of body interoception and emotions’ processing.37,38 In patients with AN, no correlations were found between alexithymia and GM. Some authors found brain regions atrophy in AN and interpreted it as a consequence of caloric restriction.39 In the present study, many regions that correlated with alexithymia in BN were atrophic in AN (e.g. parieto-temporal areas, ACC30). Consequently, the lack of correlation between GM volume and alexithymia may be influenced by these anatomical changes.40 Another explanation of the lack of correlation could be that the origin of alexithymia in women with AN is different from that of subjects with BN and CN. In particular, in AN this trait could be secondary to specific psychopathologic process. Some researchers found an inconsistency between the self-reported emotional disturbances and the results of the assessment of meta-emotional processing in subjects with AN. Hence, patients suffering from AN maintain a substantial ability in reading others’ emotions and sufficient or even good metacognitive skills,41 but they may be overridden by the pathologic over-control of worry that is typical of these subjects.42 According to this hypothesis, at least two processes may produce high alexithymia and should be accounted for in the assessment of subjects with ED. Also, the correlation pattern of TAS-20 with the other self-administered scales in subjects with AN were different from that of subjects with BN and CN (see Table 2). In particular, the correlation of DIF with
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
714 F. D’Agata et al.
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
depression in AN, but not in BN and CN, would further support the different nature of this trait. Nevertheless further studies with larger samples and a comprehensive psychometric assessment are necessary to confirm this interpretation. The correlations between psychopathological measures in our data substantially confirm previous evidences of a strong relation of alexithymia with anxiety and depression in ED.43 Nevertheless, the absence of any significant correlation between anxiety or depression with GM contradicts the hypothesis that these psychopathology traits are the primary determinants of alexithymia. As alexithymia displays an autonomous and specific anatomical substrate, the emotional disturbances may represent core features or possibly prodromal aspects of anxiety and depressive disorders, at least in patients with BN. Cognitive behavior therapy (CBT) and interpersonal psychotherapy are the therapeutic approaches that have received the most empirical evidence for ED. In particular, in BN, CBT seems to achieve better and quicker results,44 while in AN, no specific approach has shown superiority45 and a combination of nutritional therapy and psychotherapy, such as specialist supportive clinical management, CBT, and interpersonal psychotherapy, is recommended.46 Our results could support the need for therapies that work on emotions and relationships. In AN, approaches with an adjunctive focus on emotional processing have not shown superiority to standard treatment, and nor have they shown superior outcomes in the specific measures of emotion functionality.16,47 Our results might offer some insight into why simple or short emotional training is often ineffective in patients with AN and might suggest that a better understanding of the neurobiological underpinnings of alexithymia is needed to develop more effective treatments that can address this important component. In BN, a careful comparison between CBT with and without adjunctive focus on emotions needs to be performed.
Our results should be considered with caution for the AN group, as GM atrophy could be a relevant confounding factor in the correlation analyses.
Limitations Alexithymia distribution in a population with ED is not homogeneous;48 therefore, our results obtained in a limited sample should be confirmed with a larger sample in future studies. Moreover, our strict inclusion and exclusion criteria could be a potential limitation to the generalizability of our results to the entire population.
Conclusions The role of alexithymia in the pathogenesis and expression of mental disorders is still under debate,49 and more than one alexithymia subtype may be found in ED. In BN, the alexithymic traits relate to a specific brain network, while no such relation was recognizable in AN: in these patients, alexithymia scores may be a consequence of an active strategy of self-isolation from feelings.29 These different patterns of correlations suggest that the brain functionality of these disorders is partly distinguishable, as previously evidenced.50 The need for personalized therapeutic approaches to alexithymia in ED could be useful: subjects with BN could benefit from skill training to lower alexithymia levels, but in subjects with AN, it could be necessary to address a primary psychopathological factor underlying alexithymia instead of targeting it directly during treatment. A psychotherapy approach respectful of the emotional inhibition underlying the disorder could be more beneficial.
ACKNOWLEDGMENT The authors thank Steve Valeri for his contribution in editing the manuscript. The authors have no competing interests to report.
REFERENCES 1. Caglar-Nazali HP, Corfield F, Cardi V et al. A systematic review and meta-analysis of ‘Systems for Social Processes’ in eating disorders. Neurosci. Biobehav. Rev. 2014; 42: 55–92. 2. Kucharska-Pietura K, Nikolaou V, Masiak M, Treasure J. The recognition of emotion in the faces and voice of anorexia nervosa. Int. J. Eat. Disord. 2004; 35: 42–47. 3. Nandrino J-L, Doba K, Lesne A, Christophe V, Pezard L. Autobiographical memory deficit in anorexia nervosa: Emotion regulation and effect of duration of illness. J. Psychosom. Res. 2006; 61: 537–543. 4. Speranza M, Loas G, Wallier J, Corcos M. Predictive value of alexithymia in patients with eating disorders: A 3-year prospective study. J. Psychosom. Res. 2007; 63: 365–371. 5. Parker JDA, Taylor GJ, Bagby RM. The 20-Item Toronto Alexithymia Scale: III. Reliability and factorial validity in a community population. J. Psychosom. Res. 2003; 55: 269– 275.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
Alexithymia VBM brain correlates in ED 715
6. Taylor GJ, Bagby RM, Parker JDA. The 20-Item Toronto Alexithymia Scale: IV. Reliability and factorial validity in different languages and cultures. J. Psychosom. Res. 2003; 55: 277–283. 7. Bagby RM, Taylor GJ, Parker JD. The Twenty-item Toronto Alexithymia Scale – II. Convergent, discriminant, and concurrent validity. J. Psychosom. Res. 1994; 38: 33–40. 8. Bagby RM, Parker JD, Taylor GJ. The twenty-item Toronto Alexithymia Scale – I. Item selection and cross-validation of the factor structure. J. Psychosom. Res. 1994; 38: 23–32. 9. Bressi C, Taylor G, Parker J et al. Cross validation of the factor structure of the 20-item Toronto Alexithymia Scale: An Italian multicenter study. J. Psychosom. Res. 1996; 41: 551–559. 10. Espina Eizaguirre A, Ortego Saenz de Cabezón A, Ochoa de Alda I, Joaristi Olariaga L, Juaniz M. Alexithymia and its relationships with anxiety and depression in eating disorders. Pers. Individ. Dif. 2004; 36: 321–331. 11. Zonnevylle-Bender MJS, van Goozen SHM, CohenKettenis PT, van Elburg TA, van Engeland H. Emotional functioning in adolescent anorexia nervosa patients – a controlled study. Eur. Child Adolesc. Psychiatry 2004; 13: 28–34. 12. Speranza M, Loas G, Guilbaud O, Corcos M. Are treatment options related to alexithymia in eating disorders? Results from a three-year naturalistic longitudinal study. Biomed. Pharmacother. 2011; 65: 585–589. 13. Abbate-Daga G, Delsedime N, Nicotra B et al. Psychosomatic syndromes and anorexia nervosa. BMC Psychiatry 2013; 13: 14. 14. Treasure J. Emotion in eating disorders. Eur. Eat. Disord. Rev. 2012; 20: 429–430. 15. Wildes JE, Marcus MD. Development of emotion acceptance behavior therapy for anorexia nervosa: A case series. Int. J. Eat. Disord. 2011; 44: 421–427. 16. Davies H, Fox J, Naumann U, Treasure J, Schmidt U, Tchanturia K. Cognitive remediation and emotion skills training for anorexia nervosa: An observational study using neuropsychological outcomes. Eur. Eat. Disord. Rev. 2012; 20: 211–217. 17. Pinna F, Sanna L, Carpiniello B. Alexithymia in eating disorders: Therapeutic implications. Psychol. Res. Behav. Manag. 2015; 8: 1–15. 18. Borsci G, Boccardi M, Rossi R et al. Alexithymia in healthy women: A brain morphology study. J. Affect. Disord. 2009; 114: 208–215. 19. Gündel H, López-Sala A, Ceballos-Baumann AO et al. Alexithymia correlates with the size of the right anterior cingulate. Psychosom. Med. 2004; 66: 132–140. 20. Grabe HJ, Wittfeld K, Hegenscheid K et al. Alexithymia and brain gray matter volumes in a general population sample. Hum. Brain Mapp. 2014; 35: 5932–5945. 21. Ihme K, Dannlowski U, Lichev V et al. Alexithymia is related to differences in gray matter volume: A voxel-based morphometry study. Brain Res. 2013; 1491: 60–67.
22. Heinzel A, Minnerop M, Schäfer R, Müller HW, Franz M, Hautzel H. Alexithymia in healthy young men: A voxelbased morphometric study. J. Affect. Disord. 2012; 136: 1252–1256. 23. Nowakowski ME, McFarlane T, Cassin S. Alexithymia and eating disorders: A critical review of the literature. J. Eat. Disord. 2013; 1: 21. 24. Krug I, Root T, Bulik C et al. Redefining phenotypes in eating disorders based on personality: A latent profile analysis. Psychiatry Res. 2011; 188: 439–445. 25. Brooks SJ, O’Daly OG, Uher R et al. Differential neural responses to food images in women with bulimia versus anorexia nervosa. PLoS ONE 2011; 6: e22259. 26. Lobbestael J, Leurgans M, Arntz A. Inter-rater reliability of the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID I) and Axis II Disorders (SCID II). Clin. Psychol. Psychother. 2011; 18: 75–79. 27. Douaud G, Smith S, Jenkinson M et al. Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 2007; 130: 2375– 2386. 28. Culhane SE, Morera OF, Watson PJ, Millsap RE. Assessing measurement and predictive invariance of the Toronto Alexithymia Scale-20 in U.S. Anglo and U.S. Hispanic student samples. J. Pers. Assess. 2009; 91: 387–395. 29. Parling T, Mortazavi M, Ghaderi A. Alexithymia and emotional awareness in anorexia nervosa: Time for a shift in the measurement of the concept? Eat. Behav. 2010; 11: 205–210. 30. Amianto F, Caroppo P, D’Agata F et al. Brain volumetric abnormalities in patients with anorexia and bulimia nervosa: A voxel-based morphometry study. Psychiatry Res. 2013; 213: 210–216. 31. De Groot JM, Rodin G, Olmsted MP. Alexithymia, depression, and treatment outcome in bulimia nervosa. Compr. Psychiatry 1995; 36: 53–60. 32. Amianto F, Siccardi S, Abbate-Daga G, Marech L, Barosio M, Fassino S. Does anger mediate between personality and eating symptoms in bulimia nervosa? Psychiatry Res. 2012; 200: 502–512. 33. Seghier ML. The angular gyrus: Multiple functions and multiple subdivisions. Neuroscientist 2013; 19: 43–61. 34. Adolphs R. Neural systems for recognizing emotion. Curr. Opin. Neurobiol. 2002; 12: 169–177. 35. Kugel H, Eichmann M, Dannlowski U et al. Alexithymic features and automatic amygdala reactivity to facial emotion. Neurosci. Lett. 2008; 435: 40–44. 36. Hudson JI, Hudson MS. Endocrine dysfunction in anorexia nervosa and bulimia: Comparison with abnormalities in other psychiatric disorders and disturbances due to metabolic factors. Psychiatr. Dev. 1984; 2: 237–272. 37. Brooks SJ, Rask-Andersen M, Benedict C, Schiöth HB. A debate on current eating disorder diagnoses in light of neurobiological findings: Is it time for a spectrum model? BMC Psychiatry 2012; 12: 76.
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
716 F. D’Agata et al.
Psychiatry and Clinical Neurosciences 2015; 69: 708–716
38. Magal A. A hypothetical universal model of cerebellar function: Reconsideration of the current dogma. Cerebellum 2013; 12: 758–772. 39. Boghi A, Sterpone S, Sales S et al. In vivo evidence of global and focal brain alterations in anorexia nervosa. Psychiatry Res. 2011; 192: 154–159. 40. Mattar L, Huas C, Duclos J, Apfel A, Godart N. Relationship between malnutrition and depression or anxiety in anorexia nervosa: A critical review of the literature. J. Affect. Disord. 2011; 132: 311–318. 41. Woolrich RA, Cooper MJ, Turner HM. Metacognition in patients with anorexia nervosa, dieting and non-dieting women: A preliminary study. Eur. Eat. Disord. Rev. 2008; 16: 11–20. 42. Startup H, Lavender A, Oldershaw A et al. Worry and rumination in anorexia nervosa. Behav. Cogn. Psychother. 2013; 41: 301–316. 43. Montebarocci O, Codispoti M, Surcinelli P, Franzoni E, Baldaro B, Rossi N. Alexithymia in female patients with eating disorders. Eat. Weight Disord. 2006; 11: 14–21. 44. Poulsen S, Lunn S, Daniel SIF et al. A randomized controlled trial of psychoanalytic psychotherapy or cognitivebehavioral therapy for bulimia nervosa. Am. J. Psychiatry 2014; 171: 109–116. 45. Zipfel S, Wild B, Grob G et al. Focal psychodynamic therapy, cognitive behaviour therapy, and optimised treatment as usual in outpatients with anorexia nervosa (ANTOP study): Randomised controlled trial. Lancet 2014; 383: 127–137. 46. Watson HJ, Bulik CM. Update on the treatment of anorexia nervosa: Review of clinical trials, practice guidelines and emerging interventions. Psychol. Med. 2013; 43: 2477–2500. 47. Dalle Grave R, Calugi S, Conti M, Doll H, Fairburn CG. Inpatient cognitive behaviour therapy for anorexia nervosa: A randomized controlled trial. Psychother. Psychosom. 2013; 82: 390–398. 48. Speranza M, Corcos M, Loas G et al. Depressive personality dimensions and alexithymia in eating disorders. Psychiatry Res. 2005; 135: 153–163. 49. Joyce AS, Fujiwara E, Cristall M, Ruddy C, Ogrodniczuk JS. Clinical correlates of alexithymia among patients with personality disorder. Psychother. Res. 2013; 23: 690–704. 50. Amianto F, D’Agata F, Lavagnino L et al. Intrinsic connectivity networks within cerebellum and beyond in eating disorders. Cerebellum 2013; 12: 623–631.
Figure S1. Gray matter difference: Anorexia < healthy controls. Figure S2. Gray matter difference: Anorexia < bulimia. Figure S3. Gray matter difference: Bulimia < healthy controls. Figure S4. Anorexia gray matter negative correlation with age. Figure S5. Anorexia gray matter positive correlation with body mass index. Figure S6. Bulimia gray matter negative correlation with age. Figure S7. Controls gray matter negative correlation with anxiety. Figure S8. Controls gray matter negative correlation with depression. Figure S9. Controls gray matter negative correlation with alexithymia. Table S1. Global volumetric comparisons between groups. Table S2. Gray matter difference: Anorexia < healthy controls. Table S3. Corrected gray matter difference: Anorexia < healthy controls. Table S4. Gray matter difference: Anorexia < bulimia. Table S5. Corrected gray matter difference: Anorexia < bulimia. Table S6. Gray matter difference: Bulimia < healthy controls. Table S7. Anorexia gray matter negative correlation with age. Table S8. Anorexia gray matter correlation with body mass index. Table S9. Bulimia gray matter negative correlation with Difficulty to Describe Feelings. Table S10. Corrected bulimia gray matter negative correlation with Difficulty to Describe Feelings. Table S11. Bulimia gray matter negative correlation with age. Table S12. Controls gray matter negative correlation with anxiety. Table S13. Controls gray matter negative correlation with depression. Table S14. Controls gray matter negative correlation with alexithymia.
SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher’s web-site:
© 2015 The Authors Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology
