Neuroimaging Distinction Between Neurological and Psychiatric Disorders

8/17/2019 Neuroimaging Distinction Between Neurological and Psychiatric Disorders

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The ICD-10,1 arguably the dominant classification system in

use in medicine, makes a distinction between neurological andpsychiatric 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 intothe 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

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 twocategories, 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 neuroimagingcorrelates 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 neuro-

anatomical variability within each class of disorders is smaller

than between classes. Our investigation was based on a meta-

analysis 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

1

Neuroimaging distinction between neurologicaland psychiatric disordersNicolas A. Crossley, Jessica Scott, Ian Ellison-Wright and Andrea Mechelli

BackgroundIt is unclear to what extent the traditional distinction

between neurological and psychiatric disorders reflects

biological differences.

AimsTo examine neuroimaging evidence for the distinction

between neurological and psychiatric disorders.

MethodWe performed an activation likelihood estimation

meta-analysis on voxel-based morphometry studiesreporting 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.

ResultsBasal ganglia, insula, sensorimotor and temporal cortex

showed greater impairment in neurological disorders;

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.

ConclusionsFrom a neuroimaging perspective, neurological and

psychiatric disorders represent two distinct classes of

disorders.

Declaration of interestNone.

Copyright and usageB The Royal College of Psychiatrists 2015. This is an open

access article distributed under the terms of the Creative

Commons Attribution (CC BY) licence.

The British Journal of Psychiatry

1–6. doi: 10.1192/bjp.bp.114.154393


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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 studiesthat 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

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 matterdecreases detected in patients relative to controls using a statistical

threshold of either P 50.05 (whole-brain corrected) or P 50.001

(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, weincluded 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 thisapproach, 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 between-

laboratory 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 (5000iterations). Differences between the two classes of disorders were

identified using P 50.05 (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

2

Crossley et al

Table 1 List of neurological and psychiatric disorders

examined in the present investigationa

Studies

included/

published, n

Patients/

controls

included, n

Neurological disorders

Amyotrophic lateral sclerosis 7/8 114/121

Dementia in Alzheimer’s disease 7/36 114/122

Dementia in Parkinson’s 7/10 133/172

Developmental dyslexia 7/8 109/108

Dystonia 7/10 151/160

Frontotemporal dementia 7/37 158/170

Hereditary ataxia 7/15 97/121

Huntington’s disease 7/9 206/165

Juvenile myoclonic epilepsy 7/7 220/218Multiple sclerosis 7/11 335/179

Parkinson’s disease 7/17 216/197

Progressive supranuclear palsy 7/7 108/182

Temporal lobe epilepsy – left 7/14 232/334

Temporal lobe epilepsy – right 7/10 196/246

Psychiatric disorders

Attention-deficit hyperactivity disorder 7/13 245/214

Anorexia nervosa 7/10 108/130

Autism 7/12 132/129

Asperger syndrome 7/9 135/177

Bipolar affective disorder 7/18 234/270

Depressive disorder 7/24 146/205

Obsessive–compulsive disorder 7/14 236/211

Panic disorder 7/7 142/133

Post-traumatic stress disorder 7/14 128/126

Schizophrenia 7/51 332/414

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


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Neuroimaging distinction between neurological and psychiatric disorders

distributed across the different networks. To test whether

psychiatric or neurological disorders affected differentially ICA-

defined 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 themindividually, 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 abnormalin 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.

Results

Neuroanatomy of neurological and psychiatric

disorders

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 TableDS2 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

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 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) (P 50.01 and P = 0.04,

respectively, one-tailed permutation test). This was mostly driven

by neurology disorders affecting these networks less than was

expected.

3

(a)

Fig. 1 Areas affected in neurological disorders (a) and psychiatric disorders (b) (P 50.05 false-discovery rate corrected).

(b)


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Crossley et al

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 (P 50.015, t -test). In addition, the degree of

similarity was higher for neurological than psychiatric disorders

(P 51074, t -test).

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

4

Neurological 4 Psychiatric

Psychiatric 4 Neurological

Fig. 2 Differential abnormalities between neurological and psychiatric disorders (P 50.05 false-discovery rate corrected).

Psychiatric

Neurological

1

0

71

71

L e s s t h a n e x p e c t e d ( l o g )

M o r e t h a n e x p e c t e d ( l o g )

M1

Visual

M2

Visual

M3

Visual

M4

Default mode

M5

Cerebellar

M6

Sensorimotor

M7

Auditory

M8

Executive

M9Fronto-parietal

M10Fronto-parietal

Fig. 3 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 psychiatricor 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 logarithmof the ratio between observed and expected, with values below zero denoting that abnormalities are less frequent than expected and values above zero denoting that abnormalitiesare 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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Neuroimaging distinction between neurological and psychiatric disorders

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

Thisobservation 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 functionalnetworks, 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 mightbe 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 neuro-

anatomical abnormalities as spheres centred on the peak coordinates

reported by the individual studies, without taking the heterogeneous

spatial extent of these abnormalities into account.

Limitations

The present investigation has several limitations. First, our results

might suffer from a selection bias.28 In particular, the inclusion of

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 numberbased on a compromise between trying to maximise the number

of different disorders included, and the precision of the neuro-

imaging 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 ageand 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 PsychosisStudies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London,

London; Ian Ellison-Wright, MRCPsych, Avon and Wiltshire Mental HealthPartnership NHS Trust, Salisbury; Andrea Mechelli, PhD, Department of PsychosisStudies, 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 theBrainMap 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.

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Crossley et al. Neuroimaging distinction between neurological and psychiatric

disorders. Br J Psychiatrydoi: 10.1192/bjp.bp.114.154393

Online supplement DS1

Details of included studies (references)

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)

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Table DS1 Peak voxels within each ICA network.

ICA Network MNI coordinates

x y z

M1 Visual -6 -74 8

M2 Visual -10 -104 -4

-8 -14 4

M3 Visual 46 -66 -10

-46 -76 -4

-28 -70 -20

30 -48 -18

M4 Default Mode 2 -58 30

-44 -60 24

2 56 -4

54 -62 28

M5 Cerebellar -4 -50 -34M6 Sensori-motor 44 -16 48

-38 -26 56

0 -12 50

M7 Auditory -62 -24 14

60 0 -2

M8 Executive 0 36 22

-28 54 14

30 52 14

4 -18 8

12 -76 34-6 -78 34

M9 Fronto-parietal 56 -50 46

52 12 42

6 32 40

-48 -52 50

M10 Fronto-parietal -42 50 -2

-32 -68 48

-62 -54 -8

-4 24 42


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Table DS2 Abnormalities consistently found across neurological disorders.

Volume

MNI

coordinates

Cluster (mm3) x y z Label

1 142496 10 4 8 R Caudate

0 -16 10 L Thalamus

-28 -38 -2 L Hippocampus

-10 10 8 L Caudate

-30 -16 -18 L Parahippocampus

42 -10 8 R Insula

28 -6 -18 R Parahippocampus

50 12 24 R Inferior frontal gyrus

52 -10 38 R Precentral gyrus

12 -32 2 R Thalamus

2 2 -12 L Anterior cingulate

-40 6 4 L Insula

-48 10 24 L Inferior frontal gyrus

38 -24 -14 R Hippocampus

-54 -14 44 L Postcentral gyrus

56 -20 46 R Postcentral gyrus

-46 10 48 L Middle frontal gyrus

-24 0 10 L Putamen

24 4 8 R Putamen

54 -16 -8 R Superior temporal gyrus

-58 -4 4 L Precentral gyrus

-48 16 -18 L Superior temporal gyrus

-40 -14 -32 L fusiform

2 4240 0 38 24 Left cingulate

-2 46 24 L medial frontal gyrus

4 48 14 R medial frontal gyrus

-4 30 16 L anterior cingulate

3 3640 -46 -30 46 L inferior parietal lobe

4 2672 -32 -20 60 L Precentral gyrus

-32 -34 62 L inferior parietal lobe5 2504 -40 -64 -12 L fusiform

6 1864 58 -52 -10

Right inferior temporal

gyrus

7 1336 4 -10 30 R Cingulate gyrus

8 1168 20 -72 24 R Cuneus

9 1120 52 -64 40 R Parietal lobe

11 1016 8 -24 54 R paracentral gyrus

0 -32 52 L precuneus gyrus

8 -24 44 R cingulate gyrus

12 848 -28 -88 20 L middle occipital gyrus13 808 -28 44 18 L Middle frontal gyrus


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14 720 -54 -52 34 L parietal gyrus

15 640 -60 -52 6 L middle temporal gyrus

16 616 -24 -76 40 L precuneus gyrus

17 600 26 -4 50 R middle frontal gyrus

18 592 -42 54 -10 L Inferior frontal gyrus

19 568 30 -86 30 R cuneus

32 -90 20 R middle occipital gyrus

20 560 46 -58 52 R superior parietal gyrus

21 552 12 -50 24 R posterior cingulate gyrus

22 536 -4 8 34 L cingulate gyrus

23 488 62 -42 12 R superior temporal gyrus

24 456 8 -6 58 R medial frontal gyrus

25 424 54 26 14 R Inferior frontal gyrus

26 392 28 52 20 R superior frontal gyrus

27 336 32 32 40 R middle frontal gyrus

28 288 18 34 32 R medial frontal gyrus

29 272 -48 -32 2 L Superior temporal gyrus

30 272 -64 -34 14 L Superior temporal gyrus



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Table DS3 Abnormalities consistently found across psychiatric disorders.

Volume

MNI

Coordinates

Cluster (mm3) x y z Label

1 14576 8 14 0 R Caudate

-20 -6 -22 L amygdala

-2 8 -2 L caudate head

-18 4 12 L putamen

-34 -26 -28

L parahippocampal

gyrus

-30 -12 -18 L hippocampus

-16 2 -10 L globus pallidum

-36 -14 -38 L uncus

4 18 12 R caudate

2 10104 -6 48 -8 L anterior cingulate

-2 48 26 L medial frontal gyrus

10 60 26 R superior frontal gyrus

6 48 -2 R anterior cingulate

16 64 -2 R medial frontal gyrus

2 38 44 L superior frontal gyrus

3 7056 54 6 6 R precentral gyrus

16 26 -18 R inferior frontal gyrus

36 20 4 R insula

34 40 -16 R middle frontal gyrus

4 4504 44 -76 8 R middle occipital gyrus

44 -78 -12 R fusiform gyrus

56 -64 -2

R inferior temporal

gyrus

5 4456 -22 -20 70 L precentral gyrus

-12 -28 60 L paracentral gyrus

6 3752 -12 -66 20 L posterior cingulate

-12 -50 2 L lingual gyrus

-16 -40 6

L parahippocampal

gyrus

7 3712 2 -82 8 L lingual gyrus

-10 -88 12 L cuneus

8 2952 18 -6 -24

R parahippocampal

gyrus

34 -6 -26 R amygdala

9 2840 -56 2 -4 L superior frontal gyrus

-46 8 -10 L insula

10 2464 -36 22 -2 L insula

11 2032 -4 16 26 L cingulate gyrus

12 1936 38 46 16 R middle frontal gyrus

13 1680 32 -22 -16 R hippocampus


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14 1504 12 42 -30 R inferior frontal gyrus

4 38 -26 R medial frontal gyrus

-2 42 -32 L orbital gyrus

-2 36 -34 L rectal gyrus

15 1368 -44 -68 10

L middle temporal

gyrus

16 1288 42 -52 -20 R fusiform gyrus

17 1208 64 -4 0

R superior temporal

gyrus

18 1064 2 -6 8 L thalamus

4 -14 10 R thalamus

19 1016 -46 4 34 L precentral gyrus

20 976 28 -96 -4 R lingual gyrus

20 -92 -4

R inferior occipital

gyrus

21 976 -66 -20 6

L superior temporal

gyrus

-64 -32 10

L superior temporal

gyrus

22 944 -46 38 -16 L middle frontal gyrus

23 840 42 28 36 R precentral gyrus

24 824 -58 22 14 L inferior frontal gyrus

25 808 48 -18 12 R insula

26 800 -24 -58 -2

L parahippocampal

gyrus

27 728 12 -26 44 R cingulate gyrus

28 712 50 36 -14 R middle frontal gyrus

29 704 40 -14 -38 R uncus

30 624 10 28 50 R superior frontal gyrus

31 528 -22 -2 -46 L uncus

32 528 -18 54 24 L superior frontal gyrus

33 504 58 -38 -14

R middle temporal

gyrus

34 504 -8 2 54 L medial frontal gyrus

35 488 54 6 -20

R superior temporal

gyrus

36 480 30 -52 46

R superior parietal

lobule

32 -52 56 R precuneus

37 456 -52 -32 24 L inferior parietal gyrus

38 424 -22 4 68 L superior frontal gyrus

39 384 -4 -30 74 L paracentral lobule

40 376 30 4 2 R putamen

41 352 -20

-

102 14 L middle occipital gyrus

42 352 -16 42 16 L medial frontal gyrus


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43 344 -10 12 -22 L medial frontal gyrus


16 40 16 R anterior cingulate

45 280 50 14 -34

R superior temporal

gyrus

46 272 -32 34 32 L middle frontal gyrus

47 264 -34 -92 -8 L inferior occipital gyrus

48 256 -16 -54 36 L cingulate gyrus

-10 -58 32 L precuneus

49 232 2 -12 56 L medial frontal gyrus

50 208 -8 -94 -2 L inferior frontal gyrus


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Figure DS1. Flow diagram of study selection.

A. Neurological Disorders


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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


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10.1192/bjp.bp.114.154393published online June 4, 2015 Access the most recent version at DOI: BJPNicolas A. Crossley, Jessica Scott, Ian Ellison-Wright and Andrea Mechellidisorders

Neuroimaging distinction between neurological and psychiatric

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Neuroimaging Distinction Between Neurological and Psychiatric Disorders

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