The British Journal of Psychiatry (2015) 207, 429–434. doi: 10.1192/bjp.bp.114.154393

Neuroimaging distinction between neurological and psychiatric disorders{ Nicolas A. Crossley, Jessica Scott, Ian Ellison-Wright and Andrea Mechelli Background It is unclear to what extent the traditional distinction between neurological and psychiatric disorders reflects biological differences. Aims To examine neuroimaging evidence for the distinction between neurological and psychiatric disorders. Method We performed an activation likelihood estimation meta-analysis on voxel-based morphometry studies reporting decreased grey matter in 14 neurological and 10 psychiatric disorders, and compared the regional and network-level alterations for these two classes of disease. In addition, we estimated neuroanatomical heterogeneity within and between the two classes. Results Basal ganglia, insula, sensorimotor and temporal cortex showed greater impairment in neurological disorders;

The ICD-10,1 arguably the dominant classification system in use in medicine, makes a distinction between neurological and psychiatric disorders. This distinction is based on nosological criteria, such as aetiological or syndromatic similarities, but also reflects historical2 or social factors.3 It has important implications for clinical practice, since the remit of services frequently mirror the boundaries between groups of disorders, which might determine the type of treatment individual patients receive.4,5 Over the past few years, however, the distinction between psychiatric and neurological disorders has been called into question on the basis of the latest scientific data.6 It has been long known that neurological disorders can present with affective or psychotic symptoms traditionally thought to be specific to psychiatric disorders,7,8 and that psychiatric disorders present motor symptoms more frequently seen in a neurology clinic.9 More recently, brain imaging has provided an in vivo window into the human brain, and has revealed that both neurological and psychiatric disorders are associated with neuroanatomical and neurofunctional alterations.10–12 This dynamic and efficient perspective on regional changes in brain disorders can complement the histopathological information provided by neuropathological studies.13 This approach has challenged the simplistic view of neurological disorders as ‘organic’ and psychiatric disorders as ‘functional’. In addition to neuroscientific evidence, genetic studies have also begun to reveal the genetic underpinnings of neurological and psychiatric disorders. Allelic variants, copy number variants, epistatic effects and gene– environment interactions appear to play a critical role in both classes of disorders,14–16 suggesting the presence of comparable aetiological mechanisms. In a recent, thought-provoking article, White and colleagues6 suggested that the traditional distinction between disorders of the mind and disorders of the brain is a {

See editorial, pp. 373–374, this issue.

whereas cingulate, medial frontal, superior frontal and occipital cortex showed greater impairment in psychiatric disorders. The two classes of disorders affected distinct functional networks. Similarity within classes was higher than between classes; furthermore, similarity within class was higher for neurological than psychiatric disorders. Conclusions From a neuroimaging perspective, neurological and psychiatric disorders represent two distinct classes of disorders. Declaration of interest None. Copyright and usage B The Royal College of Psychiatrists 2015. This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) licence.

fundamental misconception, and called for a ‘radical rethinking’ in which psychiatric disorders should be reclassified as disorders of the central nervous system. The merging of these two categories, the authors argued, would be a logical decision given that both neurological and psychiatric disorders are rooted in the brain and are associated with a combination of both sensorimotor and psychological symptoms. However, concerns have been raised about the actual benefits patients would receive from the merging of both fields.17,18 We acknowledge that the distinction between the fields of psychiatry and neurology involves multiple factors, ranging from social and historical to biological, and that any new classification should ultimately reflect an improvement in clinical outcomes. However, it is imperative that this debate is informed by scientific evidence including the biology underpinning the two classes of disorders. In this context, we investigated whether neurological and psychiatric disorders have distinct neuroimaging correlates that arguably could reflect distinct neuropathologies. In particular, we examined whether the two classes of disorders affected different sets of regions, whether these regions were localised in different functional networks and whether neuroanatomical variability within each class of disorders is smaller than between classes. Our investigation was based on a metaanalysis of 168 published studies that used structural magnetic resonance imaging (sMRI) to investigate neuroanatomy in a total of 4227 patients and 4504 healthy controls.

Method The present meta-analysis was informed by the guidelines provided by the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) Statement (http://www. prisma-statement.org/); PRISMA flow diagrams illustrating the

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number of articles identified for each group of disorders, the number of included and excluded articles, and the reasons for exclusions, can be found in the online Fig. DS1.

Literature search and selection of studies In brief, we performed a systematic search for published studies that had employed sMRI and voxel-based morphometry (VBM)19 to examine neuroanatomy in patients with a neurological or psychiatric disorder compared with healthy controls. The list of neurological and psychiatric disorders was obtained from chapters V and VI from ICD-10 (2010 version). A total of 91 electronic searches were performed between 2 and 3 May 2012 using the PubMed database. Each electronic search comprised the following general structure: (‘voxel based’ OR morphometr* OR VBM) AND (MRI OR ‘magnetic resonance’) AND terms related to disorder as listed in the ICD-10. When a meta-analysis on a certain neurological or psychiatric disorder was found, we checked the reference list for any studies that had not been detected using our search terms. Some disorders had been examined in only a few VBM studies, whereas others had been examined in a large number of studies. We sought a pragmatic compromise between the need to include as many studies as possible to improve precision of each disorder-specific meta-analysis, and the requirement to include as many disorders as possible to obtain a representative sample of each class. This resulted in the selection of 24 different disorders that had been investigated in at least seven VBM studies (Table 1, and see online Fig. DS1 for a flow diagram of study selection). We classified disorders described in chapter V of the ICD-10 as psychiatric, and those described in chapter VI as neurological. We acknowledge that a number of disorders, in particular the Table 1 List of neurological and psychiatric disorders examined in the present investigation a Studies included/ published, n

Patients/ controls included, n

Neurological disorders Amyotrophic lateral sclerosis Dementia in Alzheimer’s disease Dementia in Parkinson’s Developmental dyslexia Dystonia Frontotemporal dementia Hereditary ataxia Huntington’s disease Juvenile myoclonic epilepsy Multiple sclerosis Parkinson’s disease Progressive supranuclear palsy Temporal lobe epilepsy – left Temporal lobe epilepsy – right

7/8 7/36 7/10 7/8 7/10 7/37 7/15 7/9 7/7 7/11 7/17 7/7 7/14 7/10

114/121 114/122 133/172 109/108 151/160 158/170 97/121 206/165 220/218 335/179 216/197 108/182 232/334 196/246

Psychiatric disorders Attention-deficit hyperactivity disorder Anorexia nervosa Autism Asperger syndrome Bipolar affective disorder Depressive disorder Obsessive–compulsive disorder Panic disorder Post-traumatic stress disorder Schizophrenia

7/13 7/10 7/12 7/9 7/18 7/24 7/14 7/7 7/14 7/51

245/214 108/130 132/129 135/177 234/270 146/205 236/211 142/133 128/126 332/414

a. For each disorder, we report the number of included and published studies and the total number of patients and healthy controls in the included studies. See Online supplement DS1 for details of the included studies.

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dementias, are included in both chapters, and therefore could be classified either as neurological or psychiatric. For the purpose of the present investigation, we classified neurodegenerative disorders as neurological; we then performed confirmatory analyses to examine how classifying dementias as psychiatric would have an impact on the results. From each study we extracted the coordinates for grey matter decreases detected in patients relative to controls using a statistical threshold of either P50.05 (whole-brain corrected) or P50.001 (uncorrected). Data were extracted independently by two researchers (N.A.C., J.S.) and any discrepancies resolved by consensus. Coordinates reported in Talairach space were transformed into Montreal Neurological Institute (MNI) coordinates.20 Meta-analysis In order to obtain a representative picture of the regions affected in neurological and psychiatric disorders, respectively, it was critical that every disorder within its class weighed the same in the final summary. This was ensured in two ways. First, we included the same number of studies per disorder (i.e. seven); if a disorder had been studied in more than seven studies, then the studies included in our investigation were selected randomly. However, the average sample size tended to be larger for those disorders investigated in a greater number of studies (for example schizophrenia) than those investigated in a smaller number of studies (for example panic disorder). This means that a random sample of seven studies for each disorder would still result in different disorders having more or less influence on the results, depending on the sample size of the individual studies. To control for this, we applied a disorder-specific weight to each of the studies included, so that the sum of the weighted sample sizes was equal across disorders. We should highlight that, using this approach, larger studies would still weigh more than smaller studies within each disorder. Selected studies from each disorder were meta-analysed using the activation likelihood estimation (ALE) method as implemented in GingerALE software (www.brainmap.org/ale),21 using a P-value of 0.05 (false-discovery rate corrected) and a cluster size threshold of 200 mm3. This method models the peak structural differences taking into account the between-subject variance, but also considers empirically informed betweenlaboratory variance. The comparison between the two classes of disorders was performed using the ALE subtraction analysis;22 this involves comparing the difference between the two ALE maps against a null distribution of differences of two similarly sized groups of studies built from random permutation (5000 iterations). Differences between the two classes of disorders were identified using P50.05 (false-discovery rate corrected) and a cluster size threshold of 200 mm3. Characterisation of network-level brain abnormalities Any significant abnormalities were characterised by mapping them onto functional networks obtained from a previous investigation using independent component analysis (ICA).23 Readers are referred to the original reference for further details on these networks, which have been made available to the neuroimaging community as ‘masks’ by the authors. We also report the anatomical coordinates of the peak weights for each network in online Table DS1). By examining the ratio between number of affected voxels within a specific network and expected number of affected voxels in that network, we were able to establish whether the number of abnormal voxels were randomly

Neuroimaging distinction between neurological and psychiatric disorders

distributed across the different networks. To test whether psychiatric or neurological disorders affected differentially ICAdefined brain networks, we compared the difference between their ratios to a null model based on permutation tests. We first randomly permuted the group label (psychiatric or neurological) of the included disorders, resulting in two new ‘random’ groups of psychiatric and neurological disorders. After meta-analysing them individually, we calculated the difference between their ratio of affected voxels within each ICA network. This process was repeated 100 times. Statistical inferences were then obtained by comparing the observed difference in each ICA network to this null-model (one-tailed). Estimating heterogeneity within and between classes In order to characterise the heterogeneity of the two classes of disorders, we computed neuroanatomical variability within each class and between classes. We first meta-analysed each single disorder and computed a measure of similarity between every pair of disorders. This measure was based on the Jaccard index, which is equal to the overlap (intersection) of abnormal voxels normalised by the union of abnormal voxels (i.e. voxels abnormal in both disorders). We then compared the similarity within neurological disorders against that within psychiatric disorders, as well as the similarity within each class against that between classes.

when dementias were classified as neurological, whereas it was primarily implicated in psychiatric disorders when dementias were classified as psychiatric (online Fig. DS2). We then identified regions showing different alterations in neurological and psychiatric disorders by directly comparing the two classes of disorders. As shown in Fig. 2, neurological disorders affected a number of regions more than psychiatric disorders did, including the basal ganglia (thalamus, caudate, putamen and globus pallidus), insula, lateral and medial temporal cortex (including the hippocampus), and sensorimotor areas. Psychiatric relative to neurological disorders showed a more restricted range of abnormalities, located in the medial frontal cortex, anterior and posterior cingulate, superior frontal gyrus and occipital cortex (bilateral lingual gyrus and left cuneus). Network-level brain abnormalities

We first identified those regions consistently affected in neurological and psychiatric disorders separately. As shown in Fig. 1, neurological disorders affected a widespread network comprising the caudate, thalamus, hippocampus, insula, anterior cingulate and sensorimotor cortex bilaterally (see online Table DS2 for details); similarly, psychiatric disorders affected a bilateral network of regions comprising the caudate, hippocampus, insula and anterior cingulate (online Table DS3). Classifying dementing illnesses (Alzheimer’s, frontotemporal and dementia in Parkinson’s Disease) as psychiatric rather than neurological did not change the overall pattern of results; however, it did result in noticeable changes throughout the temporal cortex. This area of the cortex was primarily implicated in neurological disorders

We proceeded by mapping significant abnormalities onto networks obtained from ICA. Specifically, we explored the distribution of abnormalities in each class of disorders among ten well-known networks obtained from ICA from resting state functional MRI data.23 This additional analysis provided us with information about which functional networks are disproportionately targeted by each group of disorder (for example how many more/less voxels are abnormal in a network compared with the expected number if the distribution of lesions were homogeneously spread across different networks). This revealed that both classes of disorders affected the auditory temporal network (M7), which includes language areas, and the frontal executive control network (M8), which includes cingulate and paracingulate regions, more than expected. By contrast, both neurological and psychiatric disorders tend to affect the cerebellar (M5) network less than expected. Figure 3 shows that neurological disorders appeared to affect the sensorimotor network (M6) and frontoparietal network (M9) more than psychiatric disorders. By contrast, psychiatric disorders appeared to affect visual networks (M1 and M3) and the default mode network (M4) more than neurological disorders. Permutation tests showed that the two classes of disorders significantly differed in the visual cortex (M1) and default-mode network (M4) (P50.01 and P = 0.04, respectively, one-tailed permutation test). This was mostly driven by neurology disorders affecting these networks less than was expected.

(a)

(b)

Results Neuroanatomy of neurological and psychiatric disorders

Fig. 1

Areas affected in neurological disorders (a) and psychiatric disorders (b) (P50.05 false-discovery rate corrected).

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Neurological 4 Psychiatric

Psychiatric 4 Neurological

Fig. 2

Differential abnormalities between neurological and psychiatric disorders (P50.05 false-discovery rate corrected).

Heterogeneity within and between classes We also estimated the neuroanatomical heterogeneity within and between classes (online Fig. DS3). The degree of similarity within each class of disorders was higher than the degree of similarity between classes (P50.015, t-test). In addition, the degree of similarity was higher for neurological than psychiatric disorders (P51074, t-test). M1

Less than expected (log)

More than expected (log)

Visual

M2 Visual

M3 Visual

M4

Discussion Main findings The way in which clusters of symptoms are grouped into different disorders is an important clinical issue, as it determines both diagnosis and treatment of individual patients. The aim of our M5

M6

Default mode Cerebellar Sensorimotor

M7 Auditory

M8

M9

M10

Executive

Frontoparietal

Frontoparietal

1

0

71

Psychiatric 71

Fig. 3

Neurological

Network fingerprint for neurological (white) and psychiatric (grey) disorders.

This figure illustrates the distribution of neuroimaging abnormalities across networks for psychiatric and neurological disorders respectively. In particular, it shows whether psychiatric or neurological disorders affect each of our ten networks of interest more or less than expected (based on the total number of affected voxels). Values correspond to the logarithm of the ratio between observed and expected, with values below zero denoting that abnormalities are less frequent than expected and values above zero denoting that abnormalities are more frequent than expected. The asterisk indicates a statistically significant difference between the two classes at P50.05 (one-tailed permutation tests).

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investigation was to contribute in this discussion by examining whether disorders currently classified as ‘neurological’ and ‘psychiatric’ have distinct neuroimaging correlates. We found that both types of disorders were associated with widespread alterations in cortical and subcortical areas (Fig. 1). In a previous study using a similar approach, we showed that this similarity is driven by the network organisation of the brain.24 This observation challenges the traditional distinction between disorders of the mind and disorders of the brain, and provides fresh support for a new conceptual framework in which both neurological and psychiatric disease are considered ‘disorders of the nervous system’.6 Although there were many similar brain regions affected across types of disorders, our meta-analytic techniques also showed differences between the two groups of disorders. The basal ganglia, insula, lateral and medial temporal cortex, and sensorimotor areas showed greater impairment in neurological disorders; whereas the medial frontal cortex, anterior and posterior cingulate, superior frontal gyrus and occipital cortex (bilateral lingual gyrus and left cuneus) showed greater impairment in psychiatric disorders. These structural differences between the two classes of disorders affected distinct functional networks, with the effect of neurological disorders evident in the sensorimotor and frontoparietal networks and the effect of psychiatric disorders evident in the visual and default mode networks. Although many of these structural differences are consistent with our existing knowledge of neurological and psychiatric disorders, the greater effect of psychiatric than neurological disorders in visual areas might be surprising to some readers. Closer inspection of the data indicated that this difference was mostly driven by neurological disorders affecting these areas less than was expected based on the total number of significant voxels (Fig. 3). By contrast, abnormalities in occipital areas have often been detected in studies of post-traumatic stress disorder25 or schizophrenia.26 Disorders within each class, either neurological or psychiatric, were more similar to each other in terms of neuroanatomical alterations than disorders belonging to different classes. In addition, psychiatric disorders were more dissimilar than neurological disorders, speaking of a more heterogeneous class. Taken collectively, these results provide some neuroimaging evidence for the existing distinction between neurological and psychiatric disorders as separate classes of disease. Although such neuroimaging evidence does not necessarily mean that the existing distinction is useful from a clinical perspective, it may inform the current debate on whether the current system should be reconsidered.6 The observation of neuroimaging differences between neurological and psychiatric disorders was based on group-level statistical inferences; this raises the question of whether it might be possible to use multivariate statistical learning techniques to identify individual disorders as neurological or psychiatric. We performed an exploratory analysis using a multivariate statistical learning technique known as support vector machine;27 this, however, did not yield any significant findings, suggesting that group-level differences do not necessarily allow accurate inferences at the level of the individual disorder. This might be as a result of the high degree of neuroanatomical heterogeneity within each class or, alternatively, a suboptimal methodological approach. In particular, we attempted to classify individual disorders by modelling neuroanatomical abnormalities as spheres centred on the peak coordinates reported by the individual studies, without taking the heterogeneous spatial extent of these abnormalities into account.

neurological or psychiatric disorders that had been examined in more than seven VBM studies may not have resulted in a representative random sample of each group of disorders. We tried to overcome this limitation by including as many disorders as possible in order to increase the level of representativeness within each class. Similarly, we included disorders with seven or more VBM studies. As previously described, we selected this number based on a compromise between trying to maximise the number of different disorders included, and the precision of the neuroimaging estimate of each disorders. Second, the ALE meta-analysis is based on the frequency with which an effect has been found with a selected statistical threshold, but does not consider variability in the effect size across studies.29 Third, we compared the two classes of disorders in terms of grey matter volume only. Ideally, any biologically informed classification of disease should be based on multiple domains including, for example, both brain structure and function, and should use multiple approaches such as neuroimaging, genetics and pharmacology. Finally, we did not consider the effects of age and gender in the different disorders. However, we note that the original VBM studies typically used patient and control groups that were balanced according to age and gender; this means that our results are unlikely to be a result of these confounding variables. In conclusion, we have shown some divergent neuroimaging findings in neurological and psychiatric disorders; this suggests that neurological and psychiatric disorders represent two distinct classes of disorders from a neuroimaging perspective. Nicolas A. Crossley, MRCPsych, PhD, Jessica Scott, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, London; Ian Ellison-Wright, MRCPsych, Avon and Wiltshire Mental Health Partnership NHS Trust, Salisbury; Andrea Mechelli, PhD, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, London, UK Correspondence: Andrea Mechelli, Department of Psychosis Studies, Institute of Psychiatry, King’s College London, De Crespigny Park, London SE5 8AF, UK. Email: [email protected] First received 8 Nov 2013, final revision 16 Oct 2014, accepted 19 Oct 2014

Funding A.M. is funded by a research grant from the Medical Research Council (grant ID 99859). N.C. is funded by a Wellcome Trust Clinical Research Training Fellowship (WT093907). The data used in this study were subsequently used in a collaborative project with the BrainMap database (supported by NIH/NIMH R01 MH074457) where they are now available. The sources of funding had no role in study design, data collection, analysis, interpretation, writing the report, or in the decision to submit the article for publication.

References 1

World Health Organization. The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. WHO, 1992.

2

Martin JB. The integration of neurology, psychiatry, and neuroscience in the 21st century. Am J Psychiatry 2002; 159: 695–704.

3

Baerlocher MO, Detsky AS. Professional monopolies in medicine. JAMA 2009; 301: 858–60.

4

Butler MA, Corboy JR, Filley CM. How the conflict between American psychiatry and neurology delayed the appreciation of cognitive dysfunction in multiple sclerosis. Neuropsychol Rev 2009; 19: 399–410.

5

Kanner AM. When did neurologists and psychiatrists stop talking to each other? Epilepsy Behav 2003; 4: 597–601.

6

White PD, Rickards H, Zeman AZ. Time to end the distinction between mental and neurological illnesses. BMJ 2012; 344: e3454.

7

Aarsland D, Pahlhagen S, Ballard CG, Ehrt U, Svenningsson P. Depression in Parkinson disease–epidemiology, mechanisms and management. Nat Rev Neurol 2012; 8: 35–47.

8

Nadkarni S, Arnedo V, Devinsky O. Psychosis in epilepsy patients. Epilepsia 2007; 48 (suppl 9): 17–9.

Limitations The present investigation has several limitations. First, our results might suffer from a selection bias.28 In particular, the inclusion of

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9 Fenton WS. Prevalence of spontaneous dyskinesia in schizophrenia. J Clin Psychiatry 2000; 61 (suppl 4): 10–4. 10 Wright IC, Rabe-Hesketh S, Woodruff PW, David AS, Murray RM, Bullmore ET. Meta-analysis of regional brain volumes in schizophrenia. Am J Psychiatry 2000; 157: 16–25. 11 Kempton MJ, Geddes JR, Ettinger U, Williams SC, Grasby PM. Meta-analysis, database, and meta-regression of 98 structural imaging studies in bipolar disorder. Arch Gen Psychiatry 2008; 65: 1017–32. 12 Gong Q, Li L, Tognin S, Wu Q, Pettersson-Yeo W, Lui S, et al. Using structural neuroanatomy to identify trauma survivors with and without post-traumatic stress disorder at the individual level. Psychol Med 2014; 44: 195–203. 13 Fornito A, Yucel M, Pantelis C. Reconciling neuroimaging and neuropathological findings in schizophrenia and bipolar disorder. Curr Opin Psychiatry 2009; 22: 312–9. 14 A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. The Huntington’s Disease Collaborative Research Group. Cell 1993; 72: 971–83. 15 Malhotra D, Sebat J. CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 2012; 148: 1223–41. 16 Tsuji S. The neurogenomics view of neurological diseases. JAMA Neurol 2013; 70: 689–94. 17 Holmes J. Minding the brain. BMJ 2012; 345: e4581. 18 Bailey S, Burn W, Craddock N, Mynors-Wallis L, Tyrer P. Suggested merger of mental and neurological illnesses is premature. BMJ 2012; 345: e4577. 19 Ashburner J, Friston KJ. Voxel-based morphometry–the methods. NeuroImage 2000; 11: 805–21. 20 Lancaster JL, Tordesillas-Gutierrez D, Martinez M, Salinas F, Evans A, Zilles K, et al. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp 2007; 28: 1194–205.

reflection

21 Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp 2009; 30: 2907–26. 22 Eickhoff SB, Bzdok D, Laird AR, Roski C, Caspers S, Zilles K, et al. Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation. NeuroImage 2011; 57: 938–49. 23 Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, et al. Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci U S A 2009; 106: 13040–5. 24 Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 2014; 137: 2382–95. 25 Li L, Wu M, Liao Y, Ouyang L, Du M, Lei D, et al. Grey matter reduction associated with posttraumatic stress disorder and traumatic stress. Neurosci Biobehav Rev 2014; 43: 163–72. 26 Javitt DC. When doors of perception close: bottom-up models of disrupted cognition in schizophrenia. Annu Rev Clin Psychol 2009; 5: 249–75. 27 Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 2012; 36: 1140–52. 28 Ioannidis JP. Excess significance bias in the literature on brain volume abnormalities. Arch Gen Psychiatry 2011; 68: 773–80. 29 Costafreda SG. Parametric coordinate-based meta-analysis: valid effect size meta-analysis of studies with differing statistical thresholds. J Neurosci Methods 2012; 210: 291–300.

On Madness and Civilisation: A History of Insanity in the Age of Reason (1961), by Michel Foucault Pat Bracken The original text of this work was published in Paris, in 1961, as Folie et De´raison: Histoire de la Folie a` l’aˆge Classique. Madness and Civilisation was the English translation (by Richard Howard) of an abridged French version from which 300 pages had been cut. A substantial number of the references from the first text were also omitted, and the deep scholarship of Foucault’s original work was not fully available to English readers until 2006, when Routledge published a comprehensive translation of the full book by Jonathan Murphy and Jean Khalfa. This delay in translation of the full text may explain the very different reactions to the work in France and in the English-speaking world. The former were positive in the main. French historians celebrated the depth of research and Foucault’s methodological originality. English-speaking historians, working with the abbreviated version only, were generally dismissive. A chorus of reviews challenged the accuracy of Foucault’s historical scholarship. In an important defence of Foucault, published in 1990, Colin Gordon argued that Histoire de la Folie was an ‘unknown book’ in the English-speaking world and went on to show how the answers to most of these historical challenges could be found in the original French version. Madness and Civilisation was Foucault’s first major work. He traced the way in which madness was understood and responded to in European societies from the medieval period up until the early part of the 19th century. Most histories of psychiatry celebrate the origins of the discipline in the European Enlightenment. Psychiatry is seen as emerging from a progressive cultural shift towards a prizing of reason and rationality. Foucault also sees psychiatry originating in this way. However, he refuses to see this as a purely benign development. He argues that the Age of Reason gave rise to a cultural sensibility which was directly responsible for the massive incarceration of people who were seen as ‘unreasonable’ across the Western world in the 19th and early 20th century. This incarceration was not a medical act, initiated by doctors. Instead, it was, in the language of today, a gigantic act of social exclusion. For Foucault, it emerged from a ‘moral condemnation of idleness’ not from a ‘desire to cure’. However, once mad people were both excluded and confined they came under the authority of medical superintendents and their staff, and this was the context in which psychiatry could grow and assume the power that it has today. The British historian Roy Porter agrees. He wrote: ‘the rise of psychological medicine was more the consequence than the cause of the rise of the insane asylum. Psychiatry could flourish once, but not before, large numbers of inmates were crowded into asylums’. The idea that our discipline emerged as the result of an act of social exclusion is not an easy one to accept. However, we are not the only profession with a questionable historical record. The challenge for us is to engage positively with the history that Foucault and other historians present to us. If we fail to do so, I believe that we will continue to struggle to develop a genuine collaboration with the growing service user movement around the world. The British Journal of Psychiatry (2015) 207, 434. doi: 10.1192/bjp.bp.115.165274

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ADHD (1-7) Alzheimer’s disease (8-14) Anorexia Nervosa (15-21) Asperger (22-28) Bipolar Affective Disorder (29-35) Dementia in Parkinson’s Disease / Lewy Body (36-42) Dyslexia (43-49) Dystonia (50-56) Frontotemporal Dementia (57-63) Ataxia (64-70) Huntington’s (71-77) Juvenile Myoclonic Epilepsy (78-84) Amyotrophic lateral sclerosis (85-91) Multiple sclerosis (92-98) OCD (99-105) Panic disorder (106-112) Parkinson (41, 113-118) Progressive Supranuclear Palsy (118-124) PTSD (125-131) Major depressive disorder (132-138) Schizophrenia (139-145) Temporal Lobe Epilepsy - Left (146-152) - Right (146, 149, 151-154)

Additional references 1. Almeida Montes LG, Ricardo-Garcell J, Barajas De La Torre LB, Prado Alcantara H, Martinez Garcia RB, Fernandez-Bouzas A, et al. Clinical correlations of grey matter reductions in the caudate nucleus of adults with attention deficit hyperactivity disorder. Journal of psychiatry & neuroscience : JPN. 2010; 35(4): 238-46. 2. Amico F, Stauber J, Koutsouleris N, Frodl T. Anterior cingulate cortex gray matter abnormalities in adults with attention deficit hyperactivity disorder: a voxel-based morphometry study. Psychiatry Research. 2011; 191(1): 31-5. 3. Carmona S, Vilarroya O, Bielsa A, Tremols V, Soliva JC, Rovira M, et al. Global and regional gray matter reductions in ADHD: a voxel-based morphometric study. Neuroscience letters. 2005; 389(2): 88-93. 4. Depue BE, Burgess GC, Bidwell LC, Willcutt EG, Banich MT. Behavioral performance predicts grey matter reductions in the right inferior frontal gyrus in young adults with combined type ADHD. Psychiatry Research. 2010; 182(3): 231-7.

5. Sasayama D, Hayashida A, Yamasue H, Harada Y, Kaneko T, Kasai K, et al. Neuroanatomical correlates of attention-deficit-hyperactivity disorder accounting for comorbid oppositional defiant disorder and conduct disorder. Psychiatry Clin Neurosci. 2010; 64(4): 394-402. 6. Seidman LJ, Biederman J, Liang L, Valera EM, Monuteaux MC, Brown A, et al. Gray matter alterations in adults with attention-deficit/hyperactivity disorder identified by voxel based morphometry. Biological Psychiatry. 2011; 69(9): 85766. 7. Yang P, Wang PN, Chuang KH, Jong YJ, Chao TC, Wu MT. Absence of gender effect on children with attention-deficit/hyperactivity disorder as assessed by optimized voxel-based morphometry. Psychiatry Research. 2008; 164(3): 24553. 8. Agosta F, Pievani M, Sala S, Geroldi C, Galluzzi S, Frisoni GB, et al. White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology. 2011; 258(3): 853-63. 9. Baron JC, Chetelat G, Desgranges B, Perchey G, Landeau B, de la Sayette V, et al. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. NeuroImage. 2001; 14(2): 298-309. 10. Waragai M, Okamura N, Furukawa K, Tashiro M, Furumoto S, Funaki Y, et al. Comparison study of amyloid PET and voxel-based morphometry analysis in mild cognitive impairment and Alzheimer's disease. J Neurol Sci. 2009; 285(1-2): 100-8. 11. Matsuda H, Kitayama N, Ohnishi T, Asada T, Nakano S, Sakamoto S, et al. Longitudinal evaluation of both morphologic and functional changes in the same individuals with Alzheimer's disease. J Nucl Med. 2002; 43(3): 304-11. 12. Farrow TF, Thiyagesh SN, Wilkinson ID, Parks RW, Ingram L, Woodruff PW. Fronto-temporal-lobe atrophy in early-stage Alzheimer's disease identified using an improved detection methodology. Psychiatry Research. 2007; 155(1): 11-9. 13. Rami L, Gomez-Anson B, Monte GC, Bosch B, Sanchez-Valle R, Molinuevo JL. Voxel based morphometry features and follow-up of amnestic patients at high risk for Alzheimer's disease conversion. Int J Geriatr Psychiatry. 2009; 24(8): 875-84. 14. Zahn R, Buechert M, Overmans J, Talazko J, Specht K, Ko CW, et al. Mapping of temporal and parietal cortex in progressive nonfluent aphasia and Alzheimer's disease using chemical shift imaging, voxel-based morphometry and positron emission tomography. Psychiatry Research. 2005; 140(2): 115-31. 15. Boghi A, Sterpone S, Sales S, D'Agata F, Bradac GB, Zullo G, et al. In vivo evidence of global and focal brain alterations in anorexia nervosa. Psychiatry Research. 2011; 192(3): 154-9. 16. Friederich HC, Walther S, Bendszus M, Biller A, Thomann P, Zeigermann S, et al. Grey matter abnormalities within cortico-limbic-striatal circuits in acute and weight-restored anorexia nervosa patients. NeuroImage. 2012; 59(2): 110613. 17. Gaudio S, Nocchi F, Franchin T, Genovese E, Cannata V, Longo D, et al. Gray matter decrease distribution in the early stages of Anorexia Nervosa restrictive type in adolescents. Psychiatry Research. 2011; 191(1): 24-30.

18. Joos A, Kloppel S, Hartmann A, Glauche V, Tuscher O, Perlov E, et al. Voxelbased morphometry in eating disorders: correlation of psychopathology with grey matter volume. Psychiatry Research. 2010; 182(2): 146-51. 19. Suchan B, Busch M, Schulte D, Gronemeyer D, Herpertz S, Vocks S. Reduction of gray matter density in the extrastriate body area in women with anorexia nervosa. Behavioural brain research. 2010; 206(1): 63-7. 20. Brooks SJ, Barker GJ, O'Daly OG, Brammer M, Williams SC, Benedict C, et al. Restraint of appetite and reduced regional brain volumes in anorexia nervosa: a voxel-based morphometric study. BMC Psychiatry. 2011; 11: 179. 21. Mainz V, Schulte-Ruther M, Fink GR, Herpertz-Dahlmann B, Konrad K. Structural brain abnormalities in adolescent anorexia nervosa before and after weight recovery and associated hormonal changes. Psychosom Med. 2012; 74(6): 574-82. 22. Ecker C, Rocha-Rego V, Johnston P, Mourao-Miranda J, Marquand A, Daly EM, et al. Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. NeuroImage. 2010; 49(1): 44-56. 23. Brieber S, Neufang S, Bruning N, Kamp-Becker I, Remschmidt H, HerpertzDahlmann B, et al. Structural brain abnormalities in adolescents with autism spectrum disorder and patients with attention deficit/hyperactivity disorder. J Child Psychol Psychiatry. 2007; 48(12): 1251-8. 24. Abell F, Krams M, Ashburner J, Passingham R, Friston K, Frackowiak R, et al. The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans. Neuroreport. 1999; 10(8): 1647-51. 25. Kwon H, Ow AW, Pedatella KE, Lotspeich LJ, Reiss AL. Voxel-based morphometry elucidates structural neuroanatomy of high-functioning autism and Asperger syndrome. Dev Med Child Neurol. 2004; 46(11): 760-4. 26. McAlonan GM, Daly E, Kumari V, Critchley HD, van Amelsvoort T, Suckling J, et al. Brain anatomy and sensorimotor gating in Asperger's syndrome. Brain : a journal of neurology. 2002; 125(Pt 7): 1594-606. 27. McAlonan GM, Suckling J, Wong N, Cheung V, Lienenkaemper N, Cheung C, et al. Distinct patterns of grey matter abnormality in high-functioning autism and Asperger's syndrome. J Child Psychol Psychiatry. 2008; 49(12): 1287-95. 28. Toal F, Daly EM, Page L, Deeley Q, Hallahan B, Bloemen O, et al. Clinical and anatomical heterogeneity in autistic spectrum disorder: a structural MRI study. Psychological Medicine. 2010; 40(7): 1171-81. 29. Haldane M, Cunningham G, Androutsos C, Frangou S. Structural brain correlates of response inhibition in Bipolar Disorder I. Journal of Psychopharmacology. 2008; 22(2): 138-43. 30. Chen X, Wen W, Malhi GS, Ivanovski B, Sachdev PS. Regional gray matter changes in bipolar disorder: a voxel-based morphometric study. The Australian and New Zealand journal of psychiatry. 2007; 41(4): 327-36. 31. Ha TH, Ha K, Kim JH, Choi JE. Regional brain gray matter abnormalities in patients with bipolar II disorder: a comparison study with bipolar I patients and healthy controls. Neuroscience letters. 2009; 456(1): 44-8. 32. Scherk H, Kemmer C, Usher J, Reith W, Falkai P, Gruber O. No change to grey and white matter volumes in bipolar I disorder patients. European Archives of Psychiatry and Clinical Neuroscience. 2008; 258(6): 345-9.

33. Stanfield AC, Moorhead TW, Job DE, McKirdy J, Sussmann JE, Hall J, et al. Structural abnormalities of ventrolateral and orbitofrontal cortex in patients with familial bipolar disorder. Bipolar disorders. 2009; 11(2): 135-44. 34. Almeida JR, Akkal D, Hassel S, Travis MJ, Banihashemi L, Kerr N, et al. Reduced gray matter volume in ventral prefrontal cortex but not amygdala in bipolar disorder: significant effects of gender and trait anxiety. Psychiatry Research. 2009; 171(1): 54-68. 35. McDonald C, Bullmore E, Sham P, Chitnis X, Suckling J, MacCabe J, et al. Regional volume deviations of brain structure in schizophrenia and psychotic bipolar disorder: computational morphometry study. The British journal of psychiatry : the journal of mental science. 2005; 186: 369-77. 36. Takahashi R, Ishii K, Miyamoto N, Yoshikawa T, Shimada K, Ohkawa S, et al. Measurement of gray and white matter atrophy in dementia with Lewy bodies using diffeomorphic anatomic registration through exponentiated lie algebra: A comparison with conventional voxel-based morphometry. AJNR Am J Neuroradiol. 2010; 31(10): 1873-8. 37. Ash S, McMillan C, Gross RG, Cook P, Morgan B, Boller A, et al. The organization of narrative discourse in Lewy body spectrum disorder. Brain and language. 2011; 119(1): 30-41. 38. Sanchez-Castaneda C, Rene R, Ramirez-Ruiz B, Campdelacreu J, Gascon J, Falcon C, et al. Correlations between gray matter reductions and cognitive deficits in dementia with Lewy Bodies and Parkinson's disease with dementia. Mov Disord. 2009; 24(12): 1740-6. 39. Beyer MK, Janvin CC, Larsen JP, Aarsland D. A magnetic resonance imaging study of patients with Parkinson's disease with mild cognitive impairment and dementia using voxel-based morphometry. J Neurol Neurosurg Psychiatry. 2007; 78(3): 254-9. 40. Nagano-Saito A, Washimi Y, Arahata Y, Kachi T, Lerch JP, Evans AC, et al. Cerebral atrophy and its relation to cognitive impairment in Parkinson disease. Neurology. 2005; 64(2): 224-9. 41. Summerfield C, Junque C, Tolosa E, Salgado-Pineda P, Gomez-Anson B, Marti MJ, et al. Structural brain changes in Parkinson disease with dementia: a voxel-based morphometry study. Arch Neurol. 2005; 62(2): 281-5. 42. Burton EJ, McKeith IG, Burn DJ, Williams ED, O'Brien JT. Cerebral atrophy in Parkinson's disease with and without dementia: a comparison with Alzheimer's disease, dementia with Lewy bodies and controls. Brain : a journal of neurology. 2004; 127(Pt 4): 791-800. 43. Brambati SM, Termine C, Ruffino M, Stella G, Fazio F, Cappa SF, et al. Regional reductions of gray matter volume in familial dyslexia. Neurology. 2004; 63(4): 742-5. 44. Eckert MA, Leonard CM, Wilke M, Eckert M, Richards T, Richards A, et al. Anatomical signatures of dyslexia in children: unique information from manual and voxel based morphometry brain measures. Cortex; a journal devoted to the study of the nervous system and behavior. 2005; 41(3): 304-15. 45. Hoeft F, Meyler A, Hernandez A, Juel C, Taylor-Hill H, Martindale JL, et al. Functional and morphometric brain dissociation between dyslexia and reading ability. P Natl Acad Sci USA. 2007; 104(10): 4234-9.

46. Kronbichler M, Wimmer H, Staffen W, Hutzler F, Mair A, Ladurner G. Developmental dyslexia: gray matter abnormalities in the occipitotemporal cortex. Human Brain Mapping. 2008; 29(5): 613-25. 47. Menghini D, Hagberg GE, Petrosini L, Bozzali M, Macaluso E, Caltagirone C, et al. Structural correlates of implicit learning deficits in subjects with developmental dyslexia. Ann N Y Acad Sci. 2008; 1145: 212-21. 48. Silani G, Frith U, Demonet JF, Fazio F, Perani D, Price C, et al. Brain abnormalities underlying altered activation in dyslexia: a voxel based morphometry study. Brain : a journal of neurology. 2005; 128(Pt 10): 2453-61. 49. Steinbrink C, Vogt K, Kastrup A, Muller HP, Juengling FD, Kassubek J, et al. The contribution of white and gray matter differences to developmental dyslexia: insights from DTI and VBM at 3.0 T. Neuropsychologia. 2008; 46(13): 3170-8. 50. Pantano P, Totaro P, Fabbrini G, Raz E, Contessa GM, Tona F, et al. A transverse and longitudinal MR imaging voxel-based morphometry study in patients with primary cervical dystonia. AJNR Am J Neuroradiol. 2011; 32(1): 814. 51. Egger K, Mueller J, Schocke M, Brenneis C, Rinnerthaler M, Seppi K, et al. Voxel based morphometry reveals specific gray matter changes in primary dystonia. Mov Disord. 2007; 22(11): 1538-42. 52. Etgen T, Muhlau M, Gaser C, Sander D. Bilateral grey-matter increase in the putamen in primary blepharospasm. J Neurol Neurosurg Psychiatry. 2006; 77(9): 1017-20. 53. Obermann M, Yaldizli O, De Greiff A, Lachenmayer ML, Buhl AR, Tumczak F, et al. Morphometric changes of sensorimotor structures in focal dystonia. Mov Disord. 2007; 22(8): 1117-23. 54. Delmaire C, Vidailhet M, Elbaz A, Bourdain F, Bleton JP, Sangla S, et al. Structural abnormalities in the cerebellum and sensorimotor circuit in writer's cramp. Neurology. 2007; 69(4): 376-80. 55. Draganski B, Thun-Hohenstein C, Bogdahn U, Winkler J, May A. "Motor circuit" gray matter changes in idiopathic cervical dystonia. Neurology. 2003; 61(9): 1228-31. 56. Garraux G, Bauer A, Hanakawa T, Wu T, Kansaku K, Hallett M. Changes in brain anatomy in focal hand dystonia. Annals of neurology. 2004; 55(5): 736-9. 57. Grossman M, McMillan C, Moore P, Ding L, Glosser G, Work M, et al. What's in a name: voxel-based morphometric analyses of MRI and naming difficulty in Alzheimer's disease, frontotemporal dementia and corticobasal degeneration. Brain : a journal of neurology. 2004; 127(Pt 3): 628-49. 58. Libon DJ, McMillan C, Gunawardena D, Powers C, Massimo L, Khan A, et al. Neurocognitive contributions to verbal fluency deficits in frontotemporal lobar degeneration. Neurology. 2009; 73(7): 535-42. 59. Wilson SM, Henry ML, Besbris M, Ogar JM, Dronkers NF, Jarrold W, et al. Connected speech production in three variants of primary progressive aphasia. Brain : a journal of neurology. 2010; 133(Pt 7): 2069-88. 60. Kanda T, Ishii K, Uemura T, Miyamoto N, Yoshikawa T, Kono AK, et al. Comparison of grey matter and metabolic reductions in frontotemporal dementia using FDG-PET and voxel-based morphometric MR studies. Eur J Nucl Med Mol Imaging. 2008; 35(12): 2227-34.

61. Whitwell JL, Warren JD, Josephs KA, Godbolt AK, Revesz T, Fox NC, et al. Voxel-based morphometry in tau-positive and tau-negative frontotemporal lobar degenerations. Neurodegener Dis. 2004; 1(4-5): 225-30. 62. Gorno-Tempini ML, Dronkers NF, Rankin KP, Ogar JM, Phengrasamy L, Rosen HJ, et al. Cognition and anatomy in three variants of primary progressive aphasia. Annals of neurology. 2004; 55(3): 335-46. 63. Rosen HJ, Kramer JH, Gorno-Tempini ML, Schuff N, Weiner M, Miller BL. Patterns of cerebral atrophy in primary progressive aphasia. Am J Geriatr Psychiatry. 2002; 10(1): 89-97. 64. Alcauter S, Barrios FA, Diaz R, Fernandez-Ruiz J. Gray and white matter alterations in spinocerebellar ataxia type 7: an in vivo DTI and VBM study. NeuroImage. 2011; 55(1): 1-7. 65. Brenneis C, Bosch SM, Schocke M, Wenning GK, Poewe W. Atrophy pattern in SCA2 determined by voxel-based morphometry. Neuroreport. 2003; 14(14): 1799-802. 66. Goel G, Pal PK, Ravishankar S, Venkatasubramanian G, Jayakumar PN, Krishna N, et al. Gray matter volume deficits in spinocerebellar ataxia: an optimized voxel based morphometric study. Parkinsonism Relat Disord. 2011; 17(7): 521-7. 67. Lukas C, Schols L, Bellenberg B, Rub U, Przuntek H, Schmid G, et al. Dissociation of grey and white matter reduction in spinocerebellar ataxia type 3 and 6: a voxel-based morphometry study. Neuroscience letters. 2006; 408(3): 230-5. 68. Della Nave R, Ginestroni A, Tessa C, Cosottini M, Giannelli M, Salvatore E, et al. Brain structural damage in spinocerebellar ataxia type 2. A voxel-based morphometry study. Mov Disord. 2008; 23(6): 899-903. 69. Della Nave R, Ginestroni A, Giannelli M, Tessa C, Salvatore E, Salvi F, et al. Brain structural damage in Friedreich's ataxia. J Neurol Neurosurg Psychiatry. 2008; 79(1): 82-5. 70. Reetz K, Lencer R, Hagenah JM, Gaser C, Tadic V, Walter U, et al. Structural changes associated with progression of motor deficits in spinocerebellar ataxia 17. Cerebellum. 2010; 9(2): 210-7. 71. Henley SM, Wild EJ, Hobbs NZ, Scahill RI, Ridgway GR, Macmanus DG, et al. Relationship between CAG repeat length and brain volume in premanifest and early Huntington's disease. J Neurol. 2009; 256(2): 203-12. 72. Ille R, Schafer A, Scharmuller W, Enzinger C, Schoggl H, Kapfhammer HP, et al. Emotion recognition and experience in Huntington disease: a voxel-based morphometry study. Journal of psychiatry & neuroscience : JPN. 2011; 36(6): 383-90. 73. Peinemann A, Schuller S, Pohl C, Jahn T, Weindl A, Kassubek J. Executive dysfunction in early stages of Huntington's disease is associated with striatal and insular atrophy: a neuropsychological and voxel-based morphometric study. J Neurol Sci. 2005; 239(1): 11-9. 74. Kassubek J, Juengling FD, Kioschies T, Henkel K, Karitzky J, Kramer B, et al. Topography of cerebral atrophy in early Huntington's disease: a voxel based morphometric MRI study. J Neurol Neurosurg Psychiatry. 2004; 75(2): 213-20. 75. Wolf RC, Sambataro F, Vasic N, Wolf ND, Thomann PA, Landwehrmeyer GB, et al. Longitudinal functional magnetic resonance imaging of cognition in preclinical Huntington's disease. Exp Neurol. 2011; 231(2): 214-22.

76. Gomez-Anson B, Alegret M, Munoz E, Monte GC, Alayrach E, Sanchez A, et al. Prefrontal cortex volume reduction on MRI in preclinical Huntington's disease relates to visuomotor performance and CAG number. Parkinsonism Relat Disord. 2009; 15(3): 213-9. 77. Muhlau M, Weindl A, Wohlschlager AM, Gaser C, Stadtler M, Valet M, et al. Voxel-based morphometry indicates relative preservation of the limbic prefrontal cortex in early Huntington disease. J Neural Transm. 2007; 114(3): 367-72. 78. de Araujo Filho GM, Jackowski AP, Lin K, Guaranha MS, Guilhoto LM, da Silva HH, et al. Personality traits related to juvenile myoclonic epilepsy: MRI reveals prefrontal abnormalities through a voxel-based morphometry study. Epilepsy Behav. 2009; 15(2): 202-7. 79. O'Muircheartaigh J, Vollmar C, Barker GJ, Kumari V, Symms MR, Thompson P, et al. Focal structural changes and cognitive dysfunction in juvenile myoclonic epilepsy. Neurology. 2011; 76(1): 34-40. 80. Kim JH, Lee JK, Koh SB, Lee SA, Lee JM, Kim SI, et al. Regional grey matter abnormalities in juvenile myoclonic epilepsy: a voxel-based morphometry study. NeuroImage. 2007; 37(4): 1132-7. 81. Tae WS, Hong SB, Joo EY, Han SJ, Cho JW, Seo DW, et al. Structural brain abnormalities in juvenile myoclonic epilepsy patients: volumetry and voxelbased morphometry. Korean J Radiol. 2006; 7(3): 162-72. 82. Roebling R, Scheerer N, Uttner I, Gruber O, Kraft E, Lerche H. Evaluation of cognition, structural, and functional MRI in juvenile myoclonic epilepsy. Epilepsia. 2009; 50(11): 2456-65. 83. Lin K, Jackowski AP, Carrete H, Jr., de Araujo Filho GM, Silva HH, Guaranha MS, et al. Voxel-based morphometry evaluation of patients with photosensitive juvenile myoclonic epilepsy. Epilepsy Res. 2009; 86(2-3): 138-45. 84. Mory SB, Betting LE, Fernandes PT, Lopes-Cendes I, Guerreiro MM, Guerreiro CA, et al. Structural abnormalities of the thalamus in juvenile myoclonic epilepsy. Epilepsy Behav. 2011; 21(4): 407-11. 85. Chang JL, Lomen-Hoerth C, Murphy J, Henry RG, Kramer JH, Miller BL, et al. A voxel-based morphometry study of patterns of brain atrophy in ALS and ALS/FTLD. Neurology. 2005; 65(1): 75-80. 86. Mezzapesa DM, Ceccarelli A, Dicuonzo F, Carella A, De Caro MF, Lopez M, et al. Whole-brain and regional brain atrophy in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol. 2007; 28(2): 255-9. 87. Senda J, Kato S, Kaga T, Ito M, Atsuta N, Nakamura T, et al. Progressive and widespread brain damage in ALS: MRI voxel-based morphometry and diffusion tensor imaging study. Amyotroph Lateral Scler. 2011; 12(1): 59-69. 88. Thivard L, Pradat PF, Lehericy S, Lacomblez L, Dormont D, Chiras J, et al. Diffusion tensor imaging and voxel based morphometry study in amyotrophic lateral sclerosis: relationships with motor disability. J Neurol Neurosurg Psychiatry. 2007; 78(8): 889-92. 89. Ellis CM, Suckling J, Amaro E, Jr., Bullmore ET, Simmons A, Williams SC, et al. Volumetric analysis reveals corticospinal tract degeneration and extramotor involvement in ALS. Neurology. 2001; 57(9): 1571-8. 90. Grosskreutz J, Kaufmann J, Fradrich J, Dengler R, Heinze HJ, Peschel T. Widespread sensorimotor and frontal cortical atrophy in Amyotrophic Lateral Sclerosis. BMC Neurol. 2006; 6: 17.

91. Cosottini M, Pesaresi I, Piazza S, Diciotti S, Cecchi P, Fabbri S, et al. Structural and functional evaluation of cortical motor areas in Amyotrophic Lateral Sclerosis. Exp Neurol. 2012; 234(1): 169-80. 92. Audoin B, Zaaraoui W, Reuter F, Rico A, Malikova I, Confort-Gouny S, et al. Atrophy mainly affects the limbic system and the deep grey matter at the first stage of multiple sclerosis. J Neurol Neurosurg Psychiatry. 2010; 81(6): 690-5. 93. Mesaros S, Rovaris M, Pagani E, Pulizzi A, Caputo D, Ghezzi A, et al. A magnetic resonance imaging voxel-based morphometry study of regional gray matter atrophy in patients with benign multiple sclerosis. Arch Neurol. 2008; 65(9): 1223-30. 94. Prinster A, Quarantelli M, Orefice G, Lanzillo R, Brunetti A, Mollica C, et al. Grey matter loss in relapsing-remitting multiple sclerosis: a voxel-based morphometry study. NeuroImage. 2006; 29(3): 859-67. 95. Prinster A, Quarantelli M, Lanzillo R, Orefice G, Vacca G, Carotenuto B, et al. A voxel-based morphometry study of disease severity correlates in relapsing-remitting multiple sclerosis. Mult Scler. 2010; 16(1): 45-54. 96. Sepulcre J, Sastre-Garriga J, Cercignani M, Ingle GT, Miller DH, Thompson AJ. Regional gray matter atrophy in early primary progressive multiple sclerosis: a voxel-based morphometry study. Arch Neurol. 2006; 63(8): 1175-80. 97. Ceccarelli A, Rocca MA, Valsasina P, Rodegher M, Pagani E, Falini A, et al. A multiparametric evaluation of regional brain damage in patients with primary progressive multiple sclerosis. Human Brain Mapping. 2009; 30(9): 3009-19. 98. Spano B, Cercignani M, Basile B, Romano S, Mannu R, Centonze D, et al. Multiparametric MR investigation of the motor pyramidal system in patients with 'truly benign' multiple sclerosis. Mult Scler. 2010; 16(2): 178-88. 99. Gilbert AR, Mataix-Cols D, Almeida JR, Lawrence N, Nutche J, Diwadkar V, et al. Brain structure and symptom dimension relationships in obsessivecompulsive disorder: a voxel-based morphometry study. J Affect Disord. 2008; 109(1-2): 117-26. 100. Gilbert AR, Keshavan MS, Diwadkar V, Nutche J, Macmaster F, Easter PC, et al. Gray matter differences between pediatric obsessive-compulsive disorder patients and high-risk siblings: a preliminary voxel-based morphometry study. Neuroscience letters. 2008; 435(1): 45-50. 101. van den Heuvel OA, Remijnse PL, Mataix-Cols D, Vrenken H, Groenewegen HJ, Uylings HB, et al. The major symptom dimensions of obsessive-compulsive disorder are mediated by partially distinct neural systems. Brain : a journal of neurology. 2009; 132(Pt 4): 853-68. 102. Pujol J, Soriano-Mas C, Alonso P, Cardoner N, Menchon JM, Deus J, et al. Mapping structural brain alterations in obsessive-compulsive disorder. Archives of general psychiatry. 2004; 61(7): 720-30. 103. Riffkin J, Yucel M, Maruff P, Wood SJ, Soulsby B, Olver J, et al. A manual and automated MRI study of anterior cingulate and orbito-frontal cortices, and caudate nucleus in obsessive-compulsive disorder: comparison with healthy controls and patients with schizophrenia. Psychiatry Research. 2005; 138(2): 99113. 104. Szeszko PR, Christian C, Macmaster F, Lencz T, Mirza Y, Taormina SP, et al. Gray matter structural alterations in psychotropic drug-naive pediatric obsessive-compulsive disorder: an optimized voxel-based morphometry study. The American journal of psychiatry. 2008; 165(10): 1299-307.

105. Valente AA, Jr., Miguel EC, Castro CC, Amaro E, Jr., Duran FL, Buchpiguel CA, et al. Regional gray matter abnormalities in obsessive-compulsive disorder: a voxel-based morphometry study. Biological Psychiatry. 2005; 58(6): 479-87. 106. Asami T, Yamasue H, Hayano F, Nakamura M, Uehara K, Otsuka T, et al. Sexually dimorphic gray matter volume reduction in patients with panic disorder. Psychiatry Research. 2009; 173(2): 128-34. 107. Massana G, Serra-Grabulosa JM, Salgado-Pineda P, Gasto C, Junque C, Massana J, et al. Parahippocampal gray matter density in panic disorder: a voxelbased morphometric study. The American journal of psychiatry. 2003; 160(3): 566-8. 108. Uchida RR, Del-Ben CM, Busatto GF, Duran FL, Guimaraes FS, Crippa JA, et al. Regional gray matter abnormalities in panic disorder: a voxel-based morphometry study. Psychiatry Research. 2008; 163(1): 21-9. 109. Yoo HK, Kim MJ, Kim SJ, Sung YH, Sim ME, Lee YS, et al. Putaminal gray matter volume decrease in panic disorder: an optimized voxel-based morphometry study. Eur J Neurosci. 2005; 22(8): 2089-94. 110. Sobanski T, Wagner G, Peikert G, Gruhn U, Schluttig K, Sauer H, et al. Temporal and right frontal lobe alterations in panic disorder: a quantitative volumetric and voxel-based morphometric MRI study. Psychological Medicine. 2010; 40(11): 1879-86. 111. Lai CH, Hsu YY, Wu YT. First episode drug-naive major depressive disorder with panic disorder: gray matter deficits in limbic and default network structures. Eur Neuropsychopharmacol. 2010; 20(10): 676-82. 112. Lai CH, Wu YT. Fronto-temporo-insula gray matter alterations of firstepisode, drug-naive and very late-onset panic disorder patients. J Affect Disord. 2012; 140(3): 285-91. 113. Camicioli R, Gee M, Bouchard TP, Fisher NJ, Hanstock CC, Emery DJ, et al. Voxel-based morphometry reveals extra-nigral atrophy patterns associated with dopamine refractory cognitive and motor impairment in parkinsonism. Parkinsonism Relat Disord. 2009; 15(3): 187-95. 114. Dalaker TO, Zivadinov R, Larsen JP, Beyer MK, Cox JL, Alves G, et al. Gray matter correlations of cognition in incident Parkinson's disease. Mov Disord. 2010; 25(5): 629-33. 115. Kostic VS, Agosta F, Petrovic I, Galantucci S, Spica V, Jecmenica-Lukic M, et al. Regional patterns of brain tissue loss associated with depression in Parkinson disease. Neurology. 2010; 75(10): 857-63. 116. Melzer TR, Watts R, MacAskill MR, Pitcher TL, Livingston L, Keenan RJ, et al. Grey matter atrophy in cognitively impaired Parkinson's disease. J Neurol Neurosurg Psychiatry. 2012; 83(2): 188-94. 117. Ramirez-Ruiz B, Marti MJ, Tolosa E, Gimenez M, Bargallo N, Valldeoriola F, et al. Cerebral atrophy in Parkinson's disease patients with visual hallucinations. Eur J Neurol. 2007; 14(7): 750-6. 118. Cordato NJ, Duggins AJ, Halliday GM, Morris JG, Pantelis C. Clinical deficits correlate with regional cerebral atrophy in progressive supranuclear palsy. Brain : a journal of neurology. 2005; 128(Pt 6): 1259-66. 119. Agosta F, Kostic VS, Galantucci S, Mesaros S, Svetel M, Pagani E, et al. The in vivo distribution of brain tissue loss in Richardson's syndrome and PSPparkinsonism: a VBM-DARTEL study. Eur J Neurosci. 2010; 32(4): 640-7.

120. Boxer AL, Geschwind MD, Belfor N, Gorno-Tempini ML, Schauer GF, Miller BL, et al. Patterns of brain atrophy that differentiate corticobasal degeneration syndrome from progressive supranuclear palsy. Arch Neurol. 2006; 63(1): 81-6. 121. Brenneis C, Seppi K, Schocke M, Benke T, Wenning GK, Poewe W. Voxel based morphometry reveals a distinct pattern of frontal atrophy in progressive supranuclear palsy. J Neurol Neurosurg Psychiatry. 2004; 75(2): 246-9. 122. Padovani A, Borroni B, Brambati SM, Agosti C, Broli M, Alonso R, et al. Diffusion tensor imaging and voxel based morphometry study in early progressive supranuclear palsy. J Neurol Neurosurg Psychiatry. 2006; 77(4): 457-63. 123. Lehericy S, Hartmann A, Lannuzel A, Galanaud D, Delmaire C, Bienaimee MJ, et al. Magnetic resonance imaging lesion pattern in Guadeloupean parkinsonism is distinct from progressive supranuclear palsy. Brain : a journal of neurology. 2010; 133(Pt 8): 2410-25. 124. Takahashi R, Ishii K, Kakigi T, Yokoyama K, Mori E, Murakami T. Brain alterations and mini-mental state examination in patients with progressive supranuclear palsy: voxel-based investigations using f-fluorodeoxyglucose positron emission tomography and magnetic resonance imaging. Dement Geriatr Cogn Dis Extra. 2011; 1(1): 381-92. 125. Chen S, Xia W, Li L, Liu J, He Z, Zhang Z, et al. Gray matter density reduction in the insula in fire survivors with posttraumatic stress disorder: a voxel-based morphometric study. Psychiatry Research. 2006; 146(1): 65-72. 126. Eckart C, Stoppel C, Kaufmann J, Tempelmann C, Hinrichs H, Elbert T, et al. Structural alterations in lateral prefrontal, parietal and posterior midline regions of men with chronic posttraumatic stress disorder. Journal of psychiatry & neuroscience : JPN. 2011; 36(3): 176-86. 127. Nardo D, Hogberg G, Looi JC, Larsson S, Hallstrom T, Pagani M. Gray matter density in limbic and paralimbic cortices is associated with trauma load and EMDR outcome in PTSD patients. Journal of Psychiatric Research. 2010; 44(7): 477-85. 128. Thomaes K, Dorrepaal E, Draijer N, de Ruiter MB, van Balkom AJ, Smit JH, et al. Reduced anterior cingulate and orbitofrontal volumes in child abuserelated complex PTSD. The Journal of clinical psychiatry. 2010; 71(12): 1636-44. 129. Yamasue H, Kasai K, Iwanami A, Ohtani T, Yamada H, Abe O, et al. Voxelbased analysis of MRI reveals anterior cingulate gray-matter volume reduction in posttraumatic stress disorder due to terrorism. P Natl Acad Sci USA. 2003; 100(15): 9039-43. 130. Zhang J, Tan Q, Yin H, Zhang X, Huan Y, Tang L, et al. Decreased gray matter volume in the left hippocampus and bilateral calcarine cortex in coal mine flood disaster survivors with recent onset PTSD. Psychiatry Research. 2011; 192(2): 84-90. 131. Tavanti M, Battaglini M, Borgogni F, Bossini L, Calossi S, Marino D, et al. Evidence of diffuse damage in frontal and occipital cortex in the brain of patients with post-traumatic stress disorder. Neurol Sci. 2012; 33(1): 59-68. 132. Abe O, Yamasue H, Kasai K, Yamada H, Aoki S, Inoue H, et al. Voxel-based analyses of gray/white matter volume and diffusion tensor data in major depression. Psychiatry Research. 2010; 181(1): 64-70.

133. Amico F, Meisenzahl E, Koutsouleris N, Reiser M, Moller HJ, Frodl T. Structural MRI correlates for vulnerability and resilience to major depressive disorder. Journal of psychiatry & neuroscience : JPN. 2011; 36(1): 15-22. 134. Leung KK, Lee TM, Wong MM, Li LS, Yip PS, Khong PL. Neural correlates of attention biases of people with major depressive disorder: a voxel-based morphometric study. Psychological Medicine. 2009; 39(7): 1097-106. 135. Peng J, Liu J, Nie B, Li Y, Shan B, Wang G, et al. Cerebral and cerebellar gray matter reduction in first-episode patients with major depressive disorder: a voxel-based morphometry study. Eur J Radiol. 2011; 80(2): 395-9. 136. Shah PJ, Ebmeier KP, Glabus MF, Goodwin GM. Cortical grey matter reductions associated with treatment-resistant chronic unipolar depression. Controlled magnetic resonance imaging study. The British journal of psychiatry : the journal of mental science. 1998; 172: 527-32. 137. Tang Y, Wang F, Xie G, Liu J, Li L, Su L, et al. Reduced ventral anterior cingulate and amygdala volumes in medication-naive females with major depressive disorder: A voxel-based morphometric magnetic resonance imaging study. Psychiatry Research. 2007; 156(1): 83-6. 138. Treadway MT, Grant MM, Ding Z, Hollon SD, Gore JC, Shelton RC. Early adverse events, HPA activity and rostral anterior cingulate volume in MDD. PLoS One. 2009; 4(3): e4887. 139. Koutsouleris N, Gaser C, Jager M, Bottlender R, Frodl T, Holzinger S, et al. Structural correlates of psychopathological symptom dimensions in schizophrenia: a voxel-based morphometric study. NeuroImage. 2008; 39(4): 1600-12. 140. Moorhead TW, Job DE, Whalley HC, Sanderson TL, Johnstone EC, Lawrie SM. Voxel-based morphometry of comorbid schizophrenia and learning disability: analyses in normalized and native spaces using parametric and nonparametric statistical methods. NeuroImage. 2004; 22(1): 188-202. 141. Neckelmann G, Specht K, Lund A, Ersland L, Smievoll AI, Neckelmann D, et al. Mr morphometry analysis of grey matter volume reduction in schizophrenia: association with hallucinations. Int J Neurosci. 2006; 116(1): 9-23. 142. Sigmundsson T, Suckling J, Maier M, Williams S, Bullmore E, Greenwood K, et al. Structural abnormalities in frontal, temporal, and limbic regions and interconnecting white matter tracts in schizophrenic patients with prominent negative symptoms. The American journal of psychiatry. 2001; 158(2): 234-43. 143. Suzuki M, Nohara S, Hagino H, Kurokawa K, Yotsutsuji T, Kawasaki Y, et al. Regional changes in brain gray and white matter in patients with schizophrenia demonstrated with voxel-based analysis of MRI. Schizophrenia Research. 2002; 55(1-2): 41-54. 144. Ortiz-Gil J, Pomarol-Clotet E, Salvador R, Canales-Rodriguez EJ, Sarro S, Gomar JJ, et al. Neural correlates of cognitive impairment in schizophrenia. The British journal of psychiatry : the journal of mental science. 2011; 199(3): 20210. 145. Watson DR, Anderson JM, Bai F, Barrett SL, McGinnity TM, Mulholland CC, et al. A voxel based morphometry study investigating brain structural changes in first episode psychosis. Behavioural brain research. 2012; 227(1): 91-9. 146. Keller SS, Wieshmann UC, Mackay CE, Denby CE, Webb J, Roberts N. Voxel based morphometry of grey matter abnormalities in patients with medically

intractable temporal lobe epilepsy: effects of side of seizure onset and epilepsy duration. J Neurol Neurosurg Psychiatry. 2002; 73(6): 648-55. 147. Pail M, Brazdil M, Marecek R, Mikl M. An optimized voxel-based morphometric study of gray matter changes in patients with left-sided and rightsided mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE/HS). Epilepsia. 2010; 51(4): 511-8. 148. Bonilha L, Rorden C, Castellano G, Pereira F, Rio PA, Cendes F, et al. Voxelbased morphometry reveals gray matter network atrophy in refractory medial temporal lobe epilepsy. Arch Neurol. 2004; 61(9): 1379-84. 149. Bernasconi N, Duchesne S, Janke A, Lerch J, Collins DL, Bernasconi A. Whole-brain voxel-based statistical analysis of gray matter and white matter in temporal lobe epilepsy. NeuroImage. 2004; 23(2): 717-23. 150. Pell GS, Briellmann RS, Pardoe H, Abbott DF, Jackson GD. Composite voxel-based analysis of volume and T2 relaxometry in temporal lobe epilepsy. NeuroImage. 2008; 39(3): 1151-61. 151. Riederer F, Lanzenberger R, Kaya M, Prayer D, Serles W, Baumgartner C. Network atrophy in temporal lobe epilepsy: a voxel-based morphometry study. Neurology. 2008; 71(6): 419-25. 152. Santana MT, Jackowski AP, da Silva HH, Caboclo LO, Centeno RS, Bressan RA, et al. Auras and clinical features in temporal lobe epilepsy: a new approach on the basis of voxel-based morphometry. Epilepsy Res. 2010; 89(2-3): 327-38. 153. McMillan AB, Hermann BP, Johnson SC, Hansen RR, Seidenberg M, Meyerand ME. Voxel-based morphometry of unilateral temporal lobe epilepsy reveals abnormalities in cerebral white matter. NeuroImage. 2004; 23(1): 16774. 154. Keller SS, Baker G, Downes JJ, Roberts N. Quantitative MRI of the prefrontal cortex and executive function in patients with temporal lobe epilepsy. Epilepsy Behav. 2009; 15(2): 186-95.

Table DS1 Peak voxels within each ICA network. ICA Network

M1 M2

Visual Visual

M4

Default Mode

M5 M6

Cerebellar Sensori-motor

M7

Auditory

M3

Visual

M8

Executive

M9

Fronto-parietal

M10

Fronto-parietal

x

MNI coordinates y

z

-6 -10 -8 46 -46 -28 30 2 -44 2 54 -4 44 -38 0 -62 60 0 -28 30 4 12 -6 56 52 6 -48 -42 -32 -62 -4

-74 -104 -14 -66 -76 -70 -48 -58 -60 56 -62 -50 -16 -26 -12 -24 0 36 54 52 -18 -76 -78 -50 12 32 -52 50 -68 -54 24

8 -4 4 -10 -4 -20 -18 30 24 -4 28 -34 48 56 50 14 -2 22 14 14 8 34 34 46 42 40 50 -2 48 -8 42

Table DS2 Abnormalities consistently found across neurological disorders. Cluster 1

Volume (mm3) 142496

2

4240

3 4

3640 2672

5

2504

6 7 8 9 11

1864 1336 1168 1120 1016

12 13

848 808

MNI coordinates x y z 10 4 8 0 -16 10 -28 -38 -2 -10 10 8 -30 -16 -18 42 -10 8 28 -6 -18 50 12 24 52 -10 38 12 -32 2 2 2 -12 -40 6 4 -48 10 24 38 -24 -14 -54 -14 44 56 -20 46 -46 10 48 -24 0 10 24 4 8 54 -16 -8 -58 -4 4 -48 16 -18 -40 -14 -32 0 38 24 -2 46 24 4 48 14 -4 30 16 -46 -30 46 -32 -20 60 -32 -34 62 -40 -64 -12 58 4 20 52 8 0 8 -28 -28

-52 -10 -72 -64 -24 -32 -24 -88 44

Label R Caudate L Thalamus L Hippocampus L Caudate L Parahippocampus R Insula R Parahippocampus R Inferior frontal gyrus R Precentral gyrus R Thalamus L Anterior cingulate L Insula L Inferior frontal gyrus R Hippocampus L Postcentral gyrus R Postcentral gyrus L Middle frontal gyrus L Putamen R Putamen R Superior temporal gyrus L Precentral gyrus L Superior temporal gyrus L fusiform Left cingulate L medial frontal gyrus R medial frontal gyrus L anterior cingulate L inferior parietal lobe L Precentral gyrus L inferior parietal lobe L fusiform Right inferior temporal -10 gyrus 30 R Cingulate gyrus 24 R Cuneus 40 R Parietal lobe 54 R paracentral gyrus 52 L precuneus gyrus 44 R cingulate gyrus 20 L middle occipital gyrus 18 L Middle frontal gyrus

14 15 16 17 18 19

720 640 616 600 592 568

20 21 22 23 24 25 26 27 28 29 30 31

560 552 536 488 456 424 392 336 288 272 272 264

-54 -60 -24 26 -42 30 32 46 12 -4 62 8 54 28 32 18 -48 -64 6

-52 -52 -76 -4 54 -86 -90 -58 -50 8 -42 -6 26 52 32 34 -32 -34 10

34 6 40 50 -10 30 20 52 24 34 12 58 14 20 40 32 2 14 56

L parietal gyrus L middle temporal gyrus L precuneus gyrus R middle frontal gyrus L Inferior frontal gyrus R cuneus R middle occipital gyrus R superior parietal gyrus R posterior cingulate gyrus L cingulate gyrus R superior temporal gyrus R medial frontal gyrus R Inferior frontal gyrus R superior frontal gyrus R middle frontal gyrus R medial frontal gyrus L Superior temporal gyrus L Superior temporal gyrus R medial frontal gyrus

Table DS3 Abnormalities consistently found across psychiatric disorders. Cluster 1

Volume (mm3) 14576

2

10104

3

7056

4

4504

5

4456

6

3752

7

3712

8

2952

9

2840

10 11 12 13

2464 2032 1936 1680

MNI Coordinates x y z 8 14 0 -20 -6 -22 -2 8 -2 -18 4 12 -34 -30 -16 -36 4 -6 -2 10 6 16 2 54 16 36 34 44 44

-26 -12 2 -14 18 48 48 60 48 64 38 6 26 20 40 -76 -78

-28 -18 -10 -38 12 -8 26 26 -2 -2 44 6 -18 4 -16 8 -12

56 -22 -12 -12 -12

-64 -20 -28 -66 -50

-2 70 60 20 2

-16 2 -10

-40 -82 -88

6 8 12

18 34 -56 -46 -36 -4 38 32

-6 -6 2 8 22 16 46 -22

-24 -26 -4 -10 -2 26 16 -16

Label R Caudate L amygdala L caudate head L putamen L parahippocampal gyrus L hippocampus L globus pallidum L uncus R caudate L anterior cingulate L medial frontal gyrus R superior frontal gyrus R anterior cingulate R medial frontal gyrus L superior frontal gyrus R precentral gyrus R inferior frontal gyrus R insula R middle frontal gyrus R middle occipital gyrus R fusiform gyrus R inferior temporal gyrus L precentral gyrus L paracentral gyrus L posterior cingulate L lingual gyrus L parahippocampal gyrus L lingual gyrus L cuneus R parahippocampal gyrus R amygdala L superior frontal gyrus L insula L insula L cingulate gyrus R middle frontal gyrus R hippocampus

14

1504

12 4 -2 -2

42 38 42 36

-30 -26 -32 -34

15 16

1368 1288

-44 42

-68 -52

10 -20

17 18

1208 1064

19 20

1016 976

64 2 4 -46 28

-4 -6 -14 4 -96

0 8 10 34 -4

20

-92

-4

976

-66

-20

6

22 23 24 25

944 840 824 808

-64 -46 42 -58 48

-32 38 28 22 -18

10 -16 36 14 12

26 27 28 29 30 31 32

800 728 712 704 624 528 528

-24 12 50 40 10 -22 -18

-58 -26 36 -14 28 -2 54

-2 44 -14 -38 50 -46 24

33 34

504 504

58 -8

-38 2

-14 54

35

488

54

6

-20

36

480

37 38 39 40

456 424 384 376

30 32 -52 -22 -4 30

46 56 24 68 74 2

41 42

352 352

-20 -16

-52 -52 -32 4 -30 4 102 42

21

R inferior frontal gyrus R medial frontal gyrus L orbital gyrus L rectal gyrus L middle temporal gyrus R fusiform gyrus R superior temporal gyrus L thalamus R thalamus L precentral gyrus R lingual gyrus R inferior occipital gyrus L superior temporal gyrus L superior temporal gyrus L middle frontal gyrus R precentral gyrus L inferior frontal gyrus R insula L parahippocampal gyrus R cingulate gyrus R middle frontal gyrus R uncus R superior frontal gyrus L uncus L superior frontal gyrus R middle temporal gyrus L medial frontal gyrus R superior temporal gyrus R superior parietal lobule R precuneus L inferior parietal gyrus L superior frontal gyrus L paracentral lobule R putamen

14 L middle occipital gyrus 16 L medial frontal gyrus

43 44

344 344

-10 24 16

12 46 40

45 46 47 48

280 272 264 256

49 50

232 208

50 -32 -34 -16 -10 2 -8

14 34 -92 -54 -58 -12 -94

-22 L medial frontal gyrus 18 R medial frontal gyrus 16 R anterior cingulate R superior temporal -34 gyrus 32 L middle frontal gyrus -8 L inferior occipital gyrus 36 L cingulate gyrus 32 L precuneus 56 L medial frontal gyrus -2 L inferior frontal gyrus

Figure DS1. Flow diagram of study selection. A. Neurological Disorders

Abbreviations : ALS= Am yotrophic laterals cleros is ; ALZH= Alzheim er’s ; DEM PARK = Dem entia inPark ins on’s ; DYSL= Dys lexia; DYST= Dys tonia; FTD= Frontotem poraldem entia; ATAX= Ataxia; HU NT= Hu nting ton’s dis eas e; J M E= J u venile m yoclonic epileps y; M S= M u ltiple s cleros is ; PARK = Park ins on’s ; PSP= Prog res s ive s u pranu clear pals y; TLEl= Tem porallobe epileps y, left; TLEr= Tem porallobe epileps y, rig ht.

B. Psychiatric Disorders

Abbreviations : ADHD= Attentiondeficit hyperactivity dis order; ANOREX= Anorexia nervos a; ASPERG = As perg er s yndrom e; BPAD= Bipolar affective dis order; DEPR= M ajor depres s ive dis order; OCD= Obs es s ive com pu ls ive dis order; PANIC= Panic dis order; PTSD= Pos t-trau m atic s tres s dis order; SCZ= Schizophrenia.

Figure DS2 Effect of classifying dementias (Alzheimer's, Dementia in Parkinson's, Frontotemporal) as neurological or psychiatric disorders. Note that differences between the two statistical analyses are mostly limited to temporal lobe regions; these regions tend to be primarily implicated in the class of disorders that includes the dementias.

Figure DS3 Heatmap of similarity between disorders. Note that neurological disorders were significantly more similar than psychiatric disorders (P