ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

Abstract

Abstract This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

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Thompson et al. Translational Psychiatry (2020) 10:100
https://doi.org/10.1038/s41398-020-0705-1
T
ranslational Psychiatry
REVIEW ARTICLE Open Access
ENIGMA and global neuroscience: A decade
of large-scale studies of the brain in health
and disease across more than 40 countries
Abstract
This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta
Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health
and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated
with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise
to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on
specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences,
or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized
analyses of “big data”(i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These
international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major
depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-
deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent
ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating
disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here,
we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges
encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for
testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical
syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial
factors.
Introduction
The ENIGMA (Enhancing NeuroImaging Genetics
through Meta Analysis) Consortium is a collaboration of
more than 1400 scientists from 43 countries studying the
human brain. ENIGMA started 10 years ago, in 2009, with
the initial aim of performing a large-scale neuroimaging
genetic study, and has since diversified into 50 working
groups (WGs), pooling worldwide data, resources and
expertise to answer fundamental questions in neu-
roscience, psychiatry, neurology, and genetics (Fig. 1
shows a world map of participating sites, broken down by
working group). Thirty of the ENIGMA WGs focus on
specific psychiatric and neurologic conditions. Four study
different aspects of development and aging. Others study
key transdiagnostic constructs, such as irritability, and the
importance of evolutionarily interesting genomic regions
in shaping human brain structure and function. Central to
the success of these WGs are the efforts of dedicated
methods development groups within ENIGMA. There are
currently 12 WGs that develop and disseminate multi-
scale and ‘big data’analysis pipelines to facilitate harmo-
nized analyses using genetic and epigenetic data,
multimodal (anatomical, diffusion, functional) magnetic
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Correspondence: Paul M. Thompson (pthomp@usc.edu)
Full list of author information is available at the end of the article.
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resonance imaging (MRI) and spectroscopy (MRS) mea-
sures, in combination with genetic and epigenetic data,
and data from electroencephalography (EEG).
The Consortium has been a formidable force for dis-
covery and innovation in human brain imaging, sup-
porting more than 200 active studies. The disorder-
specific WGs have published the largest neuroimaging
studies to date in schizophrenia (SCZ; total N=9572;
4474 cases)
1
, bipolar disorder (BD; total N=6503; 2447
cases)
2
, major depressive disorder (MDD; total N=
10,105; 2148 cases)
3
, post-traumatic stress disorder
(PTSD; total N=1868; 794 cases)
4
, substance use dis-
orders (SUD; total N=3240; 2140 cases)
5
, obsessive-
compulsive disorder (OCD; total N=3665; 1905 cases)
6
,
attention-deficit/hyperactivity disorder (ADHD; total N=
4180; 2246 cases)
7
, autism spectrum disorders (ASD; total
N=3222; 1571 cases)
8
, epilepsy (N=total 3876; 2149
cases)
9
, and 22q11.2 deletion syndrome (22q11DS; total
N=944; 474 cases)
10
. Key results of these studies are
summarized in Table 1. Building on this work, the focus
of the ENIGMA disorder-specific WGs now goes beyond
traditional diagnostic boundaries. As these first large-scale
studies are being completed, ENIGMA is beginning to
identify shared and distinct neuroimaging patterns in
brain disorders with known genetic or clinical over-
lap
11,12
, and to delineate the role of transdiagnostic risk
factors (e.g., childhood trauma) and clinical phenomena
(e.g., suicidal thoughts and behaviors). In addition,
ENIGMA’s genetic studies are now analyzing imaging and
genetics data from more than 50,000 people to uncover
genetic markers that most robustly associated with brain
structure and function, or imaging derived neurobiologi-
cal traits related to various disease conditions
13–16
.
As we detail in this review, the ENIGMA Consortium
has made multiple, seminal contributions to neuroscience
and psychiatry, including (a) characterization of robust
neuroimaging profiles for various brain disorders, (b)
standardization of metrics used to assess clinical symp-
toms of patients across multiple research sites, and (c) use
of dimensional approaches that go beyond the
case–control comparisons of individuals with categorical
diagnoses, and further enable the investigation of specific
genetic, and environmental features or neurobiological
markers associated with disorder risk and treatment
Fig. 1 World Map of ENIGMA’s Working Groups. The ENIGMA Consortium has grown to include over 1400 participating scientists from over 200
institutions, across 43 countries worldwide. ENIGMA is organized as a set of 50 WGs, studying 26 major brain diseases (see color key). Each group
works closely with the others and consists of worldwide teams of experts in each brain disorder as well as experts in the major methods used to
study each disorder. The diseases studied include major depressive disorder, bipolar disorder, schizophrenia, substance use disorder, post-traumatic
stress disorder, attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, and autism spectrum disorder, and several neurological
disorders, including Parkinson’s disease, epilepsy, ataxia, and stroke. In recent years, new WGs were created that grew into worldwide consortia on
epilepsy (Whelan et al.
9
), eating disorders (King et al.
104
), anxiety disorders (Groenewold et al.
107
), antisocial behavior, and infant neuroimaging.
Thompson et al. Translational Psychiatry (2020) 10:100 Page 2 of 28

Table 1 A Selection of key findings from ENIGMA’s Working Groups, along with key papers and current sample sizes.
Working
group
Number of
datasets
Total N
(patient N)
Age range
(in years)
Relevant publications Main findings
Clinical
22Q11DS 14 863 (533) 6–56 Villalón-Reina et al.
17
; Sun et al.
10
Widespread reductions in diffusivity,
pronounced in regions with major cortico-
cortical and cortico-thalamic fibers; thicker
cortical gray matter overall, but focal thickness
reduction in temporal and cingulate cortex;
cortical surface area showed pervasive
reductions; lower cortical surface area in
individuals with larger microdeletion; 22q-
related psychosis associated with lower
cortical thickness and significantly overlapped
with findings from ENIGMA-SCZ group.
Addiction/
SUDs
118 18,823 (6,592) 7–68 Mackey et al.
5,84
; Conrod et al.
86
Common neural substrate shared in
dependence; differential patterns of regional
volume as biomarkers of dependence on
alcohol and nicotine; lower volume or
thickness observed, with greatest effects
associated with alcohol use disorder; insula
and medial orbitofrontal cortex affected,
regardless of dependence.
ADHD 37 4180 (2246) 4–63 Hoogman et al.
7,91
; Klein et al.
47
;
Zhang-James
94
; Hess et al.
92
Reduction in bilateral amygdala, striatal, and
hippocampal volumes in the ADHD
population, especially in children; lower
cortical surface area values found in children
with ADHD, but not in adolescents or adults;
lower surface area associated with ADHD
symptoms in the general population in
childhood; genetic association studies suggest
that genes involved in neurite outgrowth play
a role in findings of reduced volume in ADHD;
gene-expression studies imply that structural
brain alterations in ADHD can also be
explained in part by the differential
vulnerability of these regions to mechanisms
mediating apoptosis, oxidative stress, and
autophagy.
ASD 54 3583 (1774) 2–64 Postema et al.
97
; van Rooij et al.
8
Altered morphometry in the cognitive and
affective parts of the striatum, frontal cortex
and temporal cortex in ASD.
BD 44 11,100 (3100) 8–86 Favre et al.
69
; Nunes et al.
23
;
Hibar et al.
2,68
Volumetric reductions in hippocampus and
thalamus and enlarged lateral ventricles in
patients; thinner cortical gray matter in
bilateral frontal, temporal and parietal regions;
strongest effects on left pars opercularis,
fusiform gyrus and rostral middle frontal
cortex in BD.
Thompson et al. Translational Psychiatry (2020) 10:100 Page 3 of 28

Table 1 continued
Working
group
Number of
datasets
Total N
(patient N)
Age range
(in years)
Relevant publications Main findings
Eating
Disorders
28 anorexia
nervosa (AN);
12 bulimia
nervosa (BN)
2531 (897 AN;
307 BN)
10–50 AN;
12–46 BN
Walton et al.
48
Signs of inverse concordance between greater
thalamus volume and risk for anorexia nervosa
(AN); variation in gene DRD2 significantly
associated with AN only after conditioning on
its association with caudate volume; genetic
variant linked to LRRC4C reached significance
after conditioning on hippocampal volume.
Epilepsy 24 3876 (2149) 18–55 Whelan et al.
9
Patients with IGE showed volume reductions
in the right thalamus and lower thickness in
the bilateral precentral gyri; both MTLE
subgroups showed volume reductions in the
ipsilateral hippocampus, and lower thickness
in extrahippocampal cortical regions,
including the precentral and paracentral gyri;
lower subcortical volume and cortical
thickness were associated with a longer
duration of epilepsy in the all-epilepsies and
right MTLE groups.
HIV 12 1044 (all
patients)
22–81 Nir et al.
124,169,170
; Fouche et al.
171
In the full group, subcortical volume
associations implicated the limbic system:
lower current CD4+counts were associated
with smaller hippocampal and thalamic
volumes; a detectable viral load was
associated with smaller hippocampal and
amygdala volumes; limbic effects were largely
driven by participants on cART; in subset of
participants not on cART, smaller putamen
volumes were associated with lower CD4+
count.
MDD 38 14,249 (4379) 10–89 van Velzen et al.
67
; Tozzi et al.
75
; Han
et al.
72
; Frodl et al.
74
; Renteria et al.
172
;
Schmaal et al.
3,70
; Ho et al.
137
;
Saemann et al.
83
Significantly lower hippocampal volumes;
thinner orbitofrontal cortex, anterior and
posterior cingulate, insula and temporal lobes
cortex in adult MDD patients; lower total
surface area and regional reductions in frontal
regions and primary and higher-order visual,
somatosensory and motor areas in
adoloescent MDD patients; greater exposure
to childhood adversity associated with smaller
caudate volumes in females, independent of
MDD; patients reporting suicidal plans or
attempts showed a smaller ICV volume
compared to controls.
OCD 38 3665 (1905) 5–65 Boedhoe et al.
6,88,167
; Hibar et al.
45
Subcortical abnormalities in pediatric and
adult patients; pallidum (bigger) and
hippocampus (smaller) key in adults, and
thalamus (bigger) key in (unmedicated)
pediatric group; parietal cortex consistently
Thompson et al. Translational Psychiatry (2020) 10:100 Page 4 of 28

Table 1 continued
Working
group
Number of
datasets
Total N
(patient N)
Age range
(in years)
Relevant publications Main findings
implicated both in children and adults; more
widespread cortical thickness abnormalities in
medicated adults, and more pronounced
surface area deficits (mainly in frontal regions)
in medicated pediatric OCD patients.
PTSD 16 3118 (1288) 17–85 Dennis et al.
76
; Salminen et al.
80
;
Logue et al.
4
;O’Leary et al.
78
Significantly smaller hippocampi, on average,
in individuals with current PTSD compared
with trauma-exposed control subjects, and
smaller amygdalae.
Schizophrenia 39 9572 (4474) 18–77 Holleran et al.
57
; van Erp et al.
1,54,55
;
Kelly et al.
56
; Walton et al.
62,63
;
Kochunov et al.
66
Positive symptom severity was negatively
related to bilateral STG thickness; widespread
thinner cortex and smaller surface area, largest
effect sizes in frontal and temporal lobe
regions; smaller hippocampus, amygdala,
thalamus, accumbens and intracranial
volumes; larger pallidum and lateral ventricle
volumes; widespread reductions in FA, esp. in
anterior corona radiata and corpus callosum;
higher mean and radial diffusivity; left MOFC
thickness significantly associated with
negative symptom severity; link between
prefrontal thinning and negative symptom
severity in schizophrenia.
CNV 37 16,889 (24
16p11.2 distal
and 125 15q11.2
CNV carriers)
3–90 van der Meer et al.
100
; Sonderby
53
16p11.2 distal CNV: Negative dose-response
associations with copy number on intracranial
volume and regional caudate, pallidum and
putamen volumes. 15q11.2 CNV: Decrease in
accumbens and cortical surface area in
deletion carriers and negative dose response
on cortical thickness.
Non-clinical
EEG 5 8425 5–73 Smit et al.
40
Identified several novel genetic variants
associated with oscillatory brain activity;
replicated and advanced understanding of
previously known genes associated with
psychopathology (i.e., schizophrenia and
alcohol use disorders); these
psychopathological liability genes affect brain
functioning, linking the genes’expression to
specific cortical/subcortical brain regions.
GWAS 34 22,456 3–91 Satizabal et al.
14
; Grasby et al.
13
;
Hibar et al.
25,173
; Adams et al.
169
Over 200 genetic loci where common
variation is associated with cortical thickness
or surface area; over 40 common genetic
variants associated with subcortical volumes.
Laterality 99 17,141 3–90 de Kovel et al.
71
; Kong et al.
90,154
;
Postema et al.
97
; Guadalupe et al.
174
Average patterns of left-right anatomical
asymmetry of the healthy brain were mapped,
Thompson et al. Translational Psychiatry (2020) 10:100 Page 5 of 28

outcome. The large scale and inclusivity of these analyses
—in terms of populations, sample sizes, numbers of
coordinating centers, and diversity of imaging and genetic
data—has been instrumental for demonstrating robust
associations between clinical factors and brain alterations,
and for stratifying patients with the same diagnosis
according to differential treatment outcomes
10,17
. Thus, a
valuable aspect of the existing ENIGMA studies is the
ability to identify the most robust pattern of non-
invasively measured neurobiological features involved in
clinical syndromes across multiple samples that are more
representative of the global population. This also results
in robust effect size estimates, without the confounds of
literature-based meta-analyses based on published data
with possible publication bias (as noted in Kong et al.)
18
.
These data also provide a unique opportunity to assess
Table 1 continued
Working
group
Number of
datasets
Total N
(patient N)
Age range
(in years)
Relevant publications Main findings
as regards cortical regional surface areas,
thicknesses, and subcortical volumes; fronto-
occipital gradient in cortical thickness
asymmetry was found, with frontal regions
generally thicker on the left, and occipital
regions on the right; asymmetries of various
structural measures were significantly
heritable, indicating genetic effects that differ
between the two sides; age, sex and
intracranial volume affected some
asymmetries, but handedness did not;
disorder case–control analyses revealed subtle
reductions of regional cortical thickness
asymmetries in ASD, as well as altered
orbitofrontal surface area asymmetry; little
evidence for altered anatomical asymmetry
was found in MDD; pediatric patients with
OCD showed evidence for altered asymmetry
of the thalamus and pallidum.
Lifespan 91 14,904 healthy
individuals
2–92 Dima et al.
175
; Frangou et al.
176
Thickness in almost all cortical regions
decreased prominently in the first two to three
decades of life, with an attenuated or
plateaued slope afterwards; exceptions to this
pattern were entorhinal and temporopolar
cortices whose thickness showed an
attenuated inverse U-shaped relation with age,
and anterior cingulate cortex, which showed a
U-shaped association with age; age at peak
cortical thickness was 6–7 years for most brain
regions.
Plasticity 36 10,199 (2242) 6–97 Brouwer et al.
38,39
Heritability estimates of change rates were
generally higher in adults than in children
suggesting an increasing influence of genetic
factors explaining individual differences in
brain structural changes with age; for some
structures, the genetic factors influencing
change were different from those influencing
the volume itself, suggesting the existence of
genetic variants specific for brain plasticity.
Thompson et al. Translational Psychiatry (2020) 10:100 Page 6 of 28

important sources of disease heterogeneity, including key
genetic, environmental, demographic, and psychosocial
factors. Here, we provide a synopsis of the first decade of
ENIGMA’s activities and highlight the successes and
challenges encountered along the way.
History
ENIGMA was launched in December 2009 to help
‘break the logjam’in genetic studies of the brain. At the
time, most neuroimaging genetics studies were assessing
historically candidate genetic variations, mostly in very
small samples of a few tens to hundreds of participants
(e.g., COMT,5-HTTLPR, BDNF). These studies typically
reported ‘candidate gene’effects that did not replicate
when tested in independent cohorts
19–21
. It became
apparent that very large numbers of genetic loci con-
tributed to variation in complex neurological or psychia-
tric traits, including imaging-derived brain measures—
each with a very small effect size—and only a few genetic
loci accounted for more than 1% of the variance in any
complex brain condition or measure
22
. Thus, scientists
began to recognize the need to pool multiple datasets
worldwide to perform better-powered studies of these
traits. In response, the ENIGMA Consortium’s initial plan
was to merge two ‘big data’sources—neuroimaging and
genetics—with the aim of discovering the impact of
genetic factors on brain systems, to determine whether
these genetic factors underlie manifestation of disorders
within the brain, and to identify diagnostic and prognostic
neuroimaging biomarkers. A further goal was to improve
on previous literature-based meta-analyses by using har-
monized processing and analysis protocols on an unpre-
cedented scale. This was the impetus that launched
ENIGMA’s early studies.
In 2014, the NIH Big Data to Knowledge (BD2K) pro-
gram awarded a consortium grant to ENIGMA with seed
funding for WGs on nine disorders: SCZ, BD, MDD,
OCD, ADHD, ASD, SUD, 22q11DS, and the effects of the
human immunodeficiency virus (HIV) on the brain. This
support led to the largest neuroimaging studies for the
nine targeted disorders, with results reported in over 50
manuscripts. These initial successes provided the driving
force to establish an additional 21 disease WGs (see
Working Group chart, Fig. 2).
Following the model established by the Psychiatric
Genomics Consortium (PGC), which emphasized har-
monization of genomic analysis protocols across sites, the
ENIGMA Consortium created harmonized protocols to
analyze brain structure and function, along with genetic,
and clinical data across its WGs. Instead of centralizing
data, ENIGMA opted to work as a ‘distributed con-
sortium’, asking groups to run standardized protocols
themselves, rather than the approach used in the PGC,
where data are centralized. At the time, ENIGMA design
was important for the rapid acceptance of the consortium
in the field, as it made contribution very easy; further, the
memoranda of understanding provided the basic
Fig. 2 ENIGMA’s Working Group Flowchart. ENIGMA’s working
groups are divided into technical groups that work on testing
harmonized methods, and clinical groups that study different
disorders and conditions across psychiatry and neurology, as well as
some behaviors (e.g., schizotypy and antisocial behaviors). The use of
harmonized analysis methods across all the working groups has
enabled cross-disorder comparisons (e.g., in the affective/psychosis
spectrum of depression to bipolar disorder to schizophrenia), and
transdiagnostic analyses of risk factors such as childhood trauma
across a number of disorders (such as major depressive disorder
(MDD) and post-traumatic stress disorder (PTSD)). Several working
groups, such as brain trauma and anxiety, consist of several subgroups
examining subtypes (e.g., panic disorder or social anxiety), and allow
analyses of overlap and differences (e.g., between military and civilian
brain trauma).
Thompson et al. Translational Psychiatry (2020) 10:100 Page 7 of 28

guidelines for the trusted collaborative networks to
develop. In the meantime—with views on data sharing
having changed quite considerably—many ENIGMA
WGs now also share (derived) individual data, allowing
for more in-depth analyses.
In ENIGMA’s genetic studies, many participating cen-
ters use different genotyping chips, so data were first
imputed to common genomic references (such as the
1000 Genomes reference panel), allowing each partici-
pating site to perform the same association tests between
brain measures and genetic variation at over 10 million
loci across the genome. Furthermore, the ENIGMA
Consortium standardized procedures for the extraction
of brain metrics (such as cortical thickness, cortical sur-
face area, and subcortical volume) from raw neuroimaging
data, implemented consensus protocols for data quality
control and outlier handling, and pioneered new
meta-analytic methods for the analysis of aggregated sta-
tistical information (http://enigma.ini.usc.edu/protocols/).
ENIGMA’s meta-analyses estimated the size and precision
of the effects after pooling evidence from multiple cohorts,
and they also ranked the neuroimaging effect sizes of
findings emerging from case–control comparisons,
thereby setting the stage for deeper, secondary analyses
aiming to explore potential moderators of psychiatric and
neurological disease. More recently, many ENIGMA
groups have moved beyond cohort level meta-analyses to
pooled, or ‘mega’-analyses (Using brain volumetric data
from ENIGMA’s OCD, ADHD, and ASD working groups,
Boedhoe et al.
12
compared meta-analysis to mega-analyses
that model site or cohort effects as random effects,
showing broad agreement. Mega-analyses allow more
sophisticated statistical adjustments as they pool more
information across cohorts; meta-analyses tend to be more
efficient when ethical, legal or logistic constraints govern
or restrict individual-level data transfer (e.g., genome-wide
genetic data).), where anonymized and unidentifiable
individual-level data are aggregated in a central location,
allowing more flexible statistical designs, such as machine
learning analyses
23
, reliable estimation of interaction
effects, and examination of polygenic risk scores. The type
and amount of data transferred for each analysis is chosen
pragmatically for each study. Distributed analyses promote
scientific engagement from many groups worldwide and
take advantage of distributed computing resources that
scale up as the network grows; here the data transferred is
mainly aggregate measures such as quality control metrics
and the statistical metrics derived from agreed-upon
analytical tests. On the other hand, the centralized ana-
lyses are preferable when a variable of interest is sparsely
distributed across sites, (e.g., individuals with 22q11DS
exhibiting psychotic symptoms) or when a specific method
is being developed, and computational power or expertise
is available at only a few sites; here the data transferred
usually include unidentifiable derived imaging metrics
(e.g., hippocampal volume) and demographic or clinical
information (age at scan, sex, diagnostic status, etc.);
however, this form of analysis may limit participation and
requires individual data transfer agreements with partici-
pating sites. We note, because of these required agree-
ments with potentially clinically sensitive patient
information, and the project-specific design of the ‘cen-
tralized’approaches, ENIGMA does not curate a database
for repeated or open access, and each cohort PI approves
of each project for which they contribute data.
ENIGMA’s genetic studies
Uncovering the genetic basis of brain morphometric
variation
The first demonstration of the value of the ENIGMA
approach was the identification of genetic loci associated
with variation in subcortical volumes including the cau-
date, putamen, and hippocampus (see Fig. 3)
14,24,25
. These
genome-wide association studies (GWAS) yielded intri-
guing new leads regarding the genetic architecture of the
human brain that were only possible because ENIGMA
afforded increased power to detect subtle effects. More
recently, ENIGMA identified more than 200 individual
loci that significantly contribute to variation in brain
measures, with p-values reaching 10
−180
; each single locus
accounted for only 0.1–1% of phenotypic variance, but up
to 20% of the variance in aggregate. For this effort
ENIGMA had partnered with the CHARGE Consortium
and UK Biobank on a series of studies of 70 cortical
measures, including regional cortical thickness and sur-
face area
13
. These discoveries resulted in an annotated
atlas of common genetic variants that contribute to
shaping the human cerebral cortex. Of particular interest,
we found that genetic loci affecting brain morphology
show enrichment for developmentally regulated genes
13
and human-specific regulatory elements
26,27
. Ongoing
efforts are beginning to map these genetic effects at a
finer-grained spatial resolution using shape analysis, sur-
face- and voxel-based analyses
28–31
. Moving beyond the
mass univariate methods, which analyze each brain
measure separately, ENIGMA has begun to use multi-
variate methods to meet the challenge of quantifying the
complex relationships between brain networks—or ‘con-
nectomes’—and the genome
32–34
.
Current ENIGMA sample sizes (which now exceed
50,000) are sufficiently large to identify genetic associa-
tions at a pace comparable to that of GWAS for other
phenotypes. In a recent analysis, Holland
35
contrasted
rates of discovery of genetic loci by ENIGMA and the
PGC and noted the distribution of effect sizes for some
brain measures (e.g., putamen volume) may indeed be
enriched for slightly larger effects compared to behavioral
traits (see also Le and Stein
36
and Franke et al.
37
). Still, a
Thompson et al. Translational Psychiatry (2020) 10:100 Page 8 of 28

central understanding gained from the ENIGMA asso-
ciation screens is that neuroimaging genetics studies—just
like analyses of behavioral measures, require tens (perhaps
hundreds) of thousands of participants to obtain robust
and reproducible effects of common polymorphisms.
Most individual effect sizes are very small explaining
<0.2% of variance, as for other complex human traits.
GWAS of multiple imaging measures may offer a way to
parcellate the brain into clusters or sectors with
overlapping genetic drivers, perhaps boosting the power
to discover genetic loci, by aggregating regions based on
their genetic correlation.
Uncovering the genetic basis of brain change
The quest to discover genetic loci that modulate brain
development and aging led to the launch of the ENIGMA-
Plasticity WG
38
, which uses longitudinal brain imaging
data from 36 cohorts worldwide to estimate rates of brain
Fig. 3 Genetic Influences on brain structure: effects of common and rare genetic variants. ENIGMA’s large-scale genetic analyses study the
effects of both common and rare genetic variants on brain measures. aA series of progressively larger genome-wide association studies have
revealed over 45 genetic loci associated with subcortical structure volumes (Hibar et al.
25
, Satizabal et al.
14
) and over 200 genetic loci associated with
cortical thickness and surface area Grasby et al.
13
. The Manhattan plots here (adapted from Hibar et al.
25
, show the genome (on the x-axis) and the
evidence for association (as a logarithm of the p-value, on the y-axis) for each common genetic variant (or SNP) with the volume of each brain
structure shown. bGenetics of Hippocampal Volume. A subsequent genome-wide association study (GWAS) of 33,536 individuals discovered six
independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, two lie within key genes involved in
neuronal migration and microtubule assembly (ASTN2 and MAST4) (Hibar et al.
173
). An interactive browser, ENIGMA-Vis—http://enigma-brain.org/
enigmavis—can be used to navigate ENIGMA’s genomic data. Initially started as a web page to plot ENIGMA summary statistics data for a specific
genomic region, ENIGMA-Vis grew over the years into a portal with tools to query, visualize, and navigate the effects, and relate them to other GWAS.
cIn complementary work on rare variants by the ENIGMA-CNV Working Group, Sønderby and colleagues (2018) examined effects of the 16p11.2
distal CNV that predisposes to psychiatric conditions including autism spectrum disorder and schizophrenia. ENIGMA (including the 16p11.2
European Consortium) and deCODE datasets were combined to discover negative dose-response associations with copy number on intracranial
volume and regional caudate, pallidum and putamen volumes—suggesting a neuropathological pattern that may underlie the neurodevelopmental
syndromes. The agreement across datasets is apparent in the Forest plots for each brain region. [Data adapted, with permission from the authors and
publishers].
Thompson et al. Translational Psychiatry (2020) 10:100 Page 9 of 28

growth or atrophy, and performs GWAS to find genetic
markers that may influence these rates of change. The
ENIGMA-Plasticity WG has established the heritability of
brain changes over time and has shown that distinct
genetic factors influence regional brain volumes and their
rate of change, implying the existence of genetic variants
specifically associated with change
39
. The WG is further
investigating how closely developmental and aging-related
genes overlap, and how they overlap with genetic loci that
are associated with risk for development of psychiatric
and neurological disease throughout life. Overall, the high
rate of discovery driven by ENIGMA is offering initial
glimpses of the overlap among genetic drivers of brain
change throughout life with specific markers of brain
structure and function.
Uncovering the genetic basis of brain functional variation
The ENIGMA Consortium has also carried out genetic
association studies of EEG-derived phenotypes. The first
study
40
of the EEG WG performed the largest GWAS to
date of oscillatory power across a range of frequencies
(delta 1–3.75 Hz, theta 4–7.75 Hz, alpha 8–12.75 Hz, and
beta 13–30 Hz) in 8425 healthy subjects. They identified
several novel genetic variants associated with alpha
oscillatory brain activity that were previously linked to
psychiatric disorders.
Characterizing the association between brain morphology
and disease-risk genes
In an early ENIGMA study, minimal overlap was
detected between schizophrenia-related and brain-related
genetic loci
37
. These questions were revisited with Baye-
sian models
41
and LD-score regression methods
42
which
identified stronger overlap between genetic loci involved
in cortical structure and loci implicated in insomnia,
major depression, Parkinson’s disease, and general cog-
nitive ability or IQ
13
. Despite initial negative results
37
,
ENIGMA’s growing sample size led to more powerful
results, allowing for the recent successes in the discovery
of brain-related genetic variants that also affect risk for
schizophrenia
43,44
, OCD
45
, anxiety disorders
46
, PTSD
46
,
ADHD
47
, anorexia nervosa
48
, Tourette syndrome
49
, and
insomnia
13
.
As the sample size of brain scans in the ENIGMA
Consortium increased beyond 50,000 MRI scans, it
became possible to discover further genetic loci associated
with multiple brain traits implicated in brain disorders. A
recent example is an ENIGMA-CHARGE GWAS of white
matter (WM) hyperintensities, a sign of vascular brain
disease, by Mather et al. (in prep), which found hetero-
geneous effects for variants associated with lesions near
the ventricles versus lesions elsewhere in the brain. An
innovative feature of this analysis was the use of anato-
mical clustering of traits to yield more powerful brain
GWAS results. Anatomical or genetic clustering is yet
another methodological improvement implemented by
ENIGMA, that can be used widely to enhance detection of
genetic associations in multiple brain disorders (see Lor-
enzi, Couvy-Duchesne for other multivariate imaging
GWAS approaches
50,51
).
Uncovering the epigenetic basis of brain morphometric
variation
Inspired by these successes, ENIGMA widened the
scope of its WGs to embrace the study of epigenetic
variations. ENIGMA’s Epigenetics group has already
identified two sites in the genome where methylation
relates to hippocampal volume (N=3337)
52
. Ongoing
studies focus on brain measures sensitive to epigenetic
age, an index of biological as opposed to chronological
aging, in both health and disease.
From common nucleotide variations to rare copy number
variants (CNV)
The ENIGMA-CNV WG was launched to study the
effects of CNVs, relatively rare genetic variants predis-
posing individuals to various neuropsychiatric disorders.
The ENIGMA collaborative approach is ideal for studying
low-frequency variants, as such efforts require large
samples that are usually beyond the scope of a single
study. Their first reports were on the 16p11.2 distal
53
and
15q11.295 CNVs (Fig. 3) and additional studies on other
CNVs are underway.
ENIGMA disorder-based neuroimaging studies
ENIGMA-schizophrenia
The Schizophrenia WG was formed in 2012, and has
since analyzed data from 39 cohorts worldwide and has
identified case–control differences in brain morpho-
metry
1,54,55
and WM microstructure
56,57
, on an unpre-
cedented scale. ENIGMA-Schizophrenia was the first
working group to publish large-scale analyses of disease,
in two seminal papers on case–control differences in
brain morphometry based on the largest samples to date.
Van Erp and ENIGMA colleagues
54
first reported that
patients with SCZ (N=2028 patients) had smaller hip-
pocampus (Cohen’sd=−0.46), amygdala (d=−0.31),
thalamus (d=−0.31), nucleus accumbens (d=−0.25),
total intracranial volumes (d=−0.12), and larger palli-
dum (d=0.21) and lateral ventricle volumes (d=0.37)
compared to healthy controls (N=2540). In a subsequent
study, the team expanded their sample to include 4474
individuals with SCZ and 5098 controls to study cortical
structures
1
. Compared to healthy controls, patients with
SCZ had globally thinner cortices (left/right hemisphere:
d=−0.53/−0.52) and smaller overall cortical surface area
(left/right hemisphere: d=−0.25/−0.25), with greatest
effect sizes in frontal and temporal regions.
Thompson et al. Translational Psychiatry (2020) 10:100 Page 10 of 28

Figures 4and 5present these cortical and subcortical
findings alongside data from several other disorders. It is
notable that these findings from ENIGMA
13,54
were
replicated in a large independent study by the Japanese
COCORO Consortium
58
, and a recent Norwegian study
of 16 cohorts by Alnæs et al.
59
. The convergence of all
three studies, reviewed in Kochunov et al.
60
, represents a
new level of rigor and reproducibility in a field where the
existence of morphometric correlates of schizophrenia
was once hotly debated
61
.
Brain alterations were also discovered in relation to
clinical features of the disease. In follow-up analyses,
Walton et al. found that positive symptom severity was
negatively related to the thickness of the superior tem-
poral gyrus bilaterally
62
, while the severity of negative
symptoms was negatively related to the cortical thickness
of several prefrontal regions and particularly the left
medial orbitofrontal cortex (MOFC)
63
.
At this point it is worth considering the added value of
other data modalities, such as diffusion MRI, which offers
complementary information on microstructural abnorm-
alities, especially in the WM, that are not detectable on
standard anatomical MRI. ENIGMA’s Diffusion MRI
working group, launched in 2012 with protocols for dif-
fusion tensor imaging (DTI), published a series of papers
on the heritability and reproducibility of DTI measures
derived with a protocol based on tract-based spatial sta-
tistics
64–66
. Over ten of ENIGMA’s working groups have
since used this protocol to rank effect sizes for DTI
metrics across key WM tracts.
Kelly et al. reported on widespread WM abnormalities
in schizophrenia, pooling data from 2359 healthy controls
and 1963 patients with SCZ from 29 independent inter-
national studies
56
. Significant reductions in fractional
anisotropy (FA) in patients with SCZ were widespread
across major WM fasciculi. While effect sizes varied by
tract and included significant reductions in the anterior
corona radiata (d=0.40) and corpus callosum (d=0.39,
specifically its body (d=0.39) and genu (d=0.37)),
effects were observed throughout the brain, with peak
reductions observed for the entire WM skeleton (d=
0.42). Figure 6shows these findings alongside data from
two other disorders for which ENIGMA published large-
scale DTI analyses, MDD
67
, and 22q11DS
17
.
Fig. 4 ENIGMA’s large-scale studies of nine brain disorders. Cortical gray matter thickness abnormalities as Cohen’sd, are mapped for nine
different disorders, for which worldwide data were analyzed with the same harmonized methods. Although the cohorts included in the studies
differed, as did the scanning sites and age ranges studied, some common and distinct patterns are apparent. Cortical maps for major depressive
disorder (MDD), bipolar disorder (BD) and schizophrenia show gradually more extensive profiles of deficits. Across all disorders, the less prevalent
disorders tend to show greater effects in the brain: the relatively subtle pattern of hippocampal-limbic deficits in MDD broadens to include frontal
deficits in bipolar disorder (consistent with frontal lobe dysfunction and impaired self-control). In schizophrenia, deficits widen to include almost the
entire cortex—only the primary visual cortex (specifically the calcarine cortex) failed to show thickness alterations in patients, after meta-analysis.
Autism spectrum disorder (ASD) and the 22q deletion syndrome (22q11DS)—a risk condition for ASD—are associated with hypertrophy in frontal
brain regions, while patients with obsessive-compulsive disorder (OCD) and alcohol use disorder tend to show deficits in frontal brain regions
involved in self-control and inhibition. More refined analyses are now relating symptom domains to these and other brain metrics, within and across
these and other disorders.
Thompson et al. Translational Psychiatry (2020) 10:100 Page 11 of 28

ENIGMA-BD
Formed shortly after the Schizophrenia WG, and fol-
lowing similar protocols, the ENIGMA’s BD WG reported
on cortical thickness and surface area measures using
anatomical MRI data from 1837 adults with BD and 2582
healthy controls, from 28 international groups
68
. BD was
associated with reduced cortical thickness in bilateral
frontal, temporal and parietal regions, and particularly in
the left pars opercularis (d=−0.29), the left fusiform
gyrus (d=−0.29), and left rostral middle frontal cortex
(d=−0.28). Interestingly, lithium use was associated with
thicker cortex in several areas. The WG also examined
case–control differences in subcortical volumes in 1710
patients with BD and 2594 healthy controls; they found
that BD was associated with reductions in the volume of
the hippocampus (d=−0.23) and the thalamus (d=
−0.15), and with enlarged lateral ventricular volume (d=
0.26). A follow-up study, showed that when applied to
regional cortical thickness, surface area, and subcortical
volumes, machine learning methods (based on support
vector machines) differentiated BD participants from
controls with above chance accuracy even in a large and
heterogeneous sample of 3020 participants from 13
ENIGMA cohorts worldwide
23
. Aggregate analyses of
Fig. 5 Subcortical abnormalities in schizophrenia, bipolar
disorder, major depressive disorder, and ADHD. a ENIGMA’s
publications of the three largest neuroimaging papers on
schizophrenia (SCZ), bipolar disorder (BD), and major depressive
disorder (MDD), suggested widespread cross-disorder differences in
effects (van Erp et al.
54
, Hibar et al.
68
). By processing 21,199 people’s
brain MRI scans consistently, we found greater brain structural
abnormalities in SCZ and BD versus MDD, and a very different pattern
in attention-deficit/hyperactivity disorder (ADHD; Hoogman et al.
7
).
Subcortically, all three disorders involve hippocampal volume deficits
—greatest in SCZ, least in MDD, and intermediate in BD. As a slightly
simplified ‘rule of thumb’, the hippocampus, ventricles, thalamus,
amygdala and nucleus accumbens show volume reductions in MDD
that are around half the magnitude of those seen in BD, which in turn
are about half the magnitude of those seen in SCZ. The basal ganglia
are an exception to this rule—perhaps because some antipsychotic
treatments have hypertrophic effects on the basal ganglia, leading to
volume excesses in medicated patients. In ADHD, however, the
amygdala, caudate and putamen, and nucleus accumbens all show
deficits, as does ICV (ventricular data is not included here for ADHD, as
it was not measured in the ADHD study). A web portal, the ENIGMA
Viewer, provides access to these summary statistics from ENIGMA’s
published studies of psychiatric and neurological disorders (http://
enigma-viewer.org/About_the_projects.html). bIndependent work by
the Japanese Consortium, COCORO, found a very similar set of effect
sizes for group differences in subcortical volumes between
schizophrenia patients and matched controls.
Fig. 6 White matter microstructure in schizophrenia, major
depressive disorder, and 22q11.2 deletion syndrome. a White
matter microstructural abnormalities are shown, by tract, based on the
largest-ever diffusion MRI studies of these three disorders. In
schizophrenia (SCZ), fractional anisotropy, a measure of white matter
microstructure, is lower in almost all individual regions, and in the full
skeleton. In major depressive disorder (MDD), a weak pattern of effects
is observed, again with MDD patients showing on average lower FA
across the full white matter skeleton, when compared to controls. In
comparisons between 22q11.2 deletion syndrome (22q11DS) and
matched controls, by contrast, the average FA along the full white
matter skeleton does not show systematic differences; instead, while
some regions do show on average lower FA in affected individuals
compared with controls, several white matter regions show higher FA.
bRelative to appropriately matched groups of healthy controls (HC),
group differences in fractional anisotropy are shown for ENIGMA’s
studies of SCZ, MDD (both in adults), and 22q11.2 deletion syndrome.
[Data adapted, with permission of the authors and publishers, from
Kelly et al.
56
, van Velzen et al.
67
, and Villalón-Reina et al.
17
; a key to the
tract names appears in the original papers; some tracts (i.e. the
hippocampal portion of the cingulum) were omitted from the
22q11DS analysis as they were not consistently in the field of view for
some cohorts of the working group].
Thompson et al. Translational Psychiatry (2020) 10:100 Page 12 of 28

individual subject data yielded better performance than
meta-analysis of site-level results. Age and exposure to
anticonvulsants were associated with greater odds of
correct classification. Although short of the 80% clinically
relevant threshold, the 65.2% accuracy (0.71 ROC-AUC)
is promising, as the study focused on a difficult to diag-
nose, highly heterogeneous condition and used only
engineered features, not raw brain imaging data. ENIG-
MA’s multi-site design may also offer a more realistic
assessment of “real-world”accuracy, by repeatedly leaving
out different sites’data for cross-validation. Future mul-
tisite brain-imaging machine learning studies will begin to
move towards sharing of more detailed individual subject
data, not only a selection of discrete features or site-level
results derived from a single modality; unsupervised
machine learning techniques may offer potential to better
understand the heterogeneity in the disorder. The
ENIGMA-BD DTI WG conducted both a mega- and
meta-analysis of 3033 subjects (1482 BD and 1551 con-
trols)
69
. Both analyses found lower FA in patients with BD
compared with healthy controls in most brain regions,
with the highest effect sizes in the corpus callosum and
cingulum.
ENIGMA-MDD
Brain morphometric analyses conducted by the
ENIGMA-MDD WG were based on MRI data from 1728
patients with MDD and 7199 controls for subcortical
volumes
70
and from 2148 patients with MDD and 7957
controls for cortical measures
3
. These studies found that
patients with MDD had lower hippocampal volumes (d=
−0.14), an effect driven by patients with recurrent illness
(d=−0.17) and by patients with an adolescent (≤21
years) age of onset (d=−0.20). First-episode patients
showed no subcortical volume differences compared to
controls. Adult patients (>21 years) had reduced cortical
thickness in bilateral orbitofrontal cortex (OFC), anterior
and posterior cingulate cortex, insula, and temporal lobe
regions (d’s: −0.10 to −0.14). In contrast, adolescent
patients showed no differences in cortical thickness but
showed lower total surface area, which seemed to be
especially driven by lower surface area in frontal (medial
OFC and superior frontal gyrus), visual, somatosensory,
and motor areas (d=−0.26 to −0.57). Moreover, these
differences in gray matter morphometry observed in
MDD do not involve abnormal asymmetry, as shown in a
joint study by the Laterality and the MDD WGs involving
2540 MDD individuals and 4230 controls, from 32
datasets
71
.
A follow-up analysis on a subset of these aforemen-
tioned data found that the brain MRIs of adult patients
with MDD (18–75 years old) appeared, on average, 1.08
years older than those of controls (d=0.14)
72
. This ‘brain
age’estimate was based on a machine learning algorithm
trained to predict chronological age from morphometric
data from 2188 controls across 19 cohorts and subse-
quently applied to hold-out data from 2126 healthy con-
trols and 2675 people with MDD. The largest brain aging
effects were observed in antidepressant users (+1.4 years;
d=0.15), currently depressed (+1.5 years; d=0.18), and
remitted patients (+2.2 years; d=0.18), compared to
controls. Within ENIGMA-MDD, Opel et al. also studied
the effects of obesity on structural brain metrics of
patients and controls (N=6420)
73
. Obesity effects were
not different between patients and controls, but there was
a significant obesity by age interaction in relation to
cortical thickness, with thinner cortices in older obese
individuals. Cortical thickness deficits related to obesity
were strongest in the temporal and frontal cortical
regions, and overlapped with patterns observed in several
neuropsychiatric disorders, but exceeded those found in
MDD without regard for BMI—in terms of the effect sizes
and range of structures affected. The magnitude of these
effects suggests a need to better understand the connec-
tions between BMI, brain aging and mental health.
Capitalizing on the statistical power of ENIGMA to
examine the role of risk factors, Frodl
74
and Tozzi
75
examined the association between retrospectively assessed
childhood maltreatment (including emotional, physical and
sexual abuse, or emotional and physical neglect), and brain
morphometry in 3036 and 3872 individuals (aged 13–89)
with and without MDD, respectively. Greater exposure to
childhood maltreatment was associated with lower cortical
thicknessofthebanksofthesuperior temporal sulcus and
supramarginal gyrus, and with lower surface area across the
whole brain and in the middle temporal gyrus. Sex differ-
ences were also observed: in females, greater maltreatment
severity was associated with overall lower gray matter
thickness and smaller caudate volumes, whereas in males,
greater maltreatment severity was associated with lower
thickness of the rostral anterior cingulate cortex.
In addition to these investigations of gray matter in
MDD, a large-scale analysis of WM microstructure with
DTI has also been completed, comparing 1305 adults and
adolescents with MDD to 1602 healthy controls from
20 samples worldwide
67
. In adults with MDD, widespread
lower FA values were found in 16 out of 25 WM tracts of
interest (d’s=0.12–0.26), with the largest differences in
the corpus callosum and corona radiata. Widespread
increased radial diffusivity (RD) was also observed (d’s=
0.12–0.18) and was driven by patients with recurrent
MDD and an adult-onset of depression.
ENIGMA-PGC Post-Traumatic Stress Disorder
In partnership with the PGC, ENIGMA launched a WG
on PTSD that has analyzed neuroimaging and clinical
data from 1868 individuals (including 794 patients with
PTSD) from 16 cohorts. In this first ENIGMA-PTSD
Thompson et al. Translational Psychiatry (2020) 10:100 Page 13 of 28

study, Logue and colleagues found that patients with
current PTSD had smaller hippocampal volumes (d=
−0.17) compared to trauma-exposed controls
4
. Child-
hood trauma predicted smaller hippocampal volume (d=
−0.17) independent of diagnosis. In a subsequent study,
the WG found that cortical thickness in 3378 individuals
(including 1309 patients with PTSD) was lower in PTSD
in the orbitofrontal cortex, cingulate cortex, precuneus,
insula, and lateral parietal cortices. In addition, a DTI
meta-analysis of 3057 individuals (including 1405 patients
with PTSD) from 25 cohorts found alterations in WM
organization in the tapetum, a structure that connects the
left and right hippocampus
76
. Structural covariance net-
work analysis applied to data from 3505 individuals
(including 1344 patients with PTSD), which examined
correlated patterns of cortical thickness and surface area,
found that PTSD is associated with network centrality
features of the insula and visual association areas
77
.To
extend these findings, ongoing studies are assessing cor-
tical structure
78,79
and hippocampal subfields in PTSD
and MDD
80–83
, to better understand the pattern and
regional specificity of hippocampal deficits in the two
disorders, and whether these patterns coincide.
ENIGMA-Addictions/SUD
The ENIGMA-Addictions/SUDs WG has 33 partici-
pating sites, contributing MRI data from 12,347 indivi-
duals of whom 2277 are adult patients with SUD relating
to one of five substances (alcohol, nicotine, cocaine,
methamphetamine, or cannabis)
5,84,85
. In these data,
Mackey
5
observed lower cortical thickness/subcortical
volume in cases relative to controls in regions that play
key roles in evaluating reward (MOFC, amygdala), task
monitoring (superior frontal cortex), attention (superior
parietal cortex, posterior cingulate) and perception/reg-
ulation of internal body states (insula). While the most
pervasive case–control differences appeared to be related
to alcohol dependence, some effects were observed for
substance dependence generally (e.g., the insula and
MOFC). A support vector machine trained on cortical
thickness and subcortical volume successfully classified
set-aside test sets for both alcohol (ROC-AUC: 0.74–0.78;
p< 0.0001) and nicotine dependence (ROC-AUC:
0.60–0.64; p< 0.0001), relative to non-dependent con-
trols
5
. A separate meta-analysis also compared the effect
size of addiction-related brain impairment to that of other
psychiatric disorders: effect sizes of alcohol-related brain
differences in subcortical brain regions were equivalent to
those reported for schizophrenia
86
.
ENIGMA-Obsessive-Compulsive Disorder
The ENIGMA’s OCD WG grew out of a previously
established consortium (the OCD Brain Imaging Con-
sortium, or OBIC)
87
, and has published the largest studies
to date of brain structure in adult and pediatric OCD,
using both meta- and mega-analytic approaches
6,88
. The
first study analyzed MRI scans from 1830 patients diag-
nosed with OCD and 1759 controls across 35 cohorts
from 26 sites worldwide
88
. Unmedicated pediatric OCD
patients demonstrated larger thalamic volumes, while the
pallidum was enlarged in adult OCD patients with disease
onset at childhood. Adult OCD patients also had sig-
nificantly smaller hippocampal volumes (d=−0.13), with
stronger effects in medicated patients with adult-onset
OCD compared to healthy controls (d=−0.29). A cor-
tical study included data from 1905 patients diagnosed
with OCD and 1760 healthy controls across 38 cohorts
from 27 sites worldwide. In adult patients diagnosed with
OCD versus controls, significantly smaller surface area of
the transverse temporal cortex (d=−0.16) and a thinner
inferior parietal cortex (d=−0.14) were found. Medi-
cated adult patients with OCD also showed thinner cor-
tices throughout the brain (Cohen’sdeffect sizes varied
between −0.10 and −0.26). Pediatric patients with OCD
showed significantly thinner inferior and superior parietal
cortices (d’s=−0.24 to −0.31), but none of the regions
analyzed showed significant differences in cortical surface
area. However, medicated pediatric patients with OCD
had smaller surface area in frontal regions (d’s=−0.27 to
−0.33), that may indicate a delayed cortical maturation.
The absence of cortical surface area abnormalities in adult
patients with a childhood onset of OCD could indicate a
normalization of these abnormalities—a hypothesis that is
now being explored with longitudinal data collection.
To assess whether the anatomical differences could be
used to create a neuroimaging biomarker for OCD, a
machine learning analysis of the cortical and subcortical
data was performed with 2304 OCD patients and 2068
controls. Classification performance across ten different
machine and deep learning approaches was poor. With
site-stratified cross-validation, the ROC-AUC ranged
between 0.57 and 0.62. The performance dropped to
chance level when leave-one-site-out cross-validation was
used, with classification performance between 0.51 and
0.54. This indicates that these anatomical brain features
do not provide a biomarker for OCD. But when patients
were stratified according to whether they had used med-
ication, classification performance improved remarkably.
Medicated OCD patients and controls could then be
distinguished with 0.73, unmedicated OCD and controls
with 0.61, and medicated and unmedicated OCD patients
with 0.86 ROC-AUC. These multivariate results therefore
mirror the univariate results, and highlight that medica-
tion use is associated with large differences in brain
anatomy
89
.
The OCD WG, in conjunction with the Laterality WG,
studied brain asymmetry in OCD using 16 pediatric
datasets (501 patients with OCD and 439 healthy
Thompson et al. Translational Psychiatry (2020) 10:100 Page 14 of 28

controls), and 30 adult datasets (1777 patients and 1654
controls)
90
. In the pediatric datasets, the largest
case–control differences were observed for volume
asymmetry of the thalamus (more leftward in patients
compared to controls; d=0.19) and the pallidum (less
leftward in patients compared to controls; d=−0.21). No
asymmetry differences were found in the adult datasets.
These findings may reflect altered neurodevelopmental
processes in OCD, affecting cortico-striato-thalamo-
cortical circuitry, which is involved in a wide range of
cognitive, motivational and emotional processes.
ENIGMA-Attention-Deficit/Hyperactivity Disorder
ENIGMA’s ADHD WG has analyzed data from up to
2264 participants with ADHD and 1934 controls from up
to 36 sites (age range: 4–63 years; 66% males)
91
. Volumes
of the nucleus accumbens (d=−0.15), amygdala (d=
−0.19), caudate (d=−0.11), hippocampus (d=−0.11),
putamen (d=−0.14), and ICV (d=−0.10) were smaller
in cases relative to controls. Effect sizes were highest in
children. No statistically significant univariate case/con-
trol differences were detected in adults. Volume differ-
ences were found to have similar effect sizes in those
treated with psychostimulant medication and those naïve
to psychostimulants. Bioinformatics analyses suggested
that the selective subcortical brain region vulnerability
was associated with differential expression of oxidative
stress, neurodevelopment and autophagy pathways
92
.
The ENIGMA-ADHD WG was the first WG in
ENIGMA to perform a detailed investigation of the case-
control effects on the cerebellum. Differential age trajec-
tories were identified for children with ADHD when
compared with typically developing children for the cor-
pus medullare
93
.
In an analysis of the cerebral cortex, lower surface area
values were found, on average, in children with ADHD,
mainly in frontal, cingulate, and temporal regions; the
largest effect was for total surface area (d=−0.21).
Fusiform gyrus and temporal pole cortical thickness were
also lower in children with ADHD. All effects were most
pronounced in early childhood. Neither surface area nor
thickness differences were found in the adolescent or
adult groups
7
, but machine learning analyses supported
the hypothesis that the case–control differences observed
in childhood could be detected in adulthood
94
. Impor-
tantly, many of the same surface area features were
associated with subclinical ADHD symptoms in children
from the general population that do not have a clinical
psychiatric diagnosis. Several of the observed brain
alterations fulfilled many of the criteria of ‘endopheno-
types’(An endophenotype is a trait, such as brain struc-
ture or function, related to the biological process of a
disorder; to qualify as an endophenotype, the trait, should
be heritable, co-segregate with an illness, yet be present
even when the disease is not, and be found in non-affected
family members at a higher rate than in the general
population
95,96
), as they were also seen in unaffected
siblings of people with ADHD in a subsample analysis of
the cortical features. The stronger effects in children may
reflect a developmental delay, perhaps due in part to
genetic risk factors, given recent findings of overlap
between the genetic contributions to ADHD and to sub-
cortical volumes
13,47
.
ENIGMA-Autism Spectrum Disorders
The ENIGMA-ASD WG published the largest neuroi-
maging study of autism analyzing data from 1571 partici-
pants with ASD and 1651 controls, from 49 sites worldwide
(ages 2–64 years)
8
. Unlike most of the disorders discussed
so far, the direction of effects seen in ASD varied by brain
region, and did so across the age span analyzed. ASD was
associated with larger lateral ventricle and intracranial
volumes, greater frontal cortical thickness and lower tem-
poral cortical thickness (d=−0.21 to 0.20). Participants
with ASD also had, on average, lower subcortical volumes
for the pallidum, putamen, amygdala, and nucleus accum-
bens. Post hoc fractional polynomial analyses showed a
sharp increase in volumes in the same regions in childhood,
peaking in adolescence and decreasing again in adulthood.
Overall, patients with ASD showed altered morphometry in
the cognitive and affective associated-regions of the stria-
tum, frontal cortex, and temporal cortex.
The ASD group worked together with the Laterality
group to produce the largest ever study of brain asym-
metry in ASD, involving 1774 patients and 1809 controls,
from 54 datasets
97
. Generally, subtle but widespread
reductions of cortical thickness asymmetries were present
in patients with ASD compared to controls, as well as
volume asymmetry of the putamen, and surface area
asymmetry of the MOFC (the strongest effect had
Cohen’sd=−0.16). Altered lateralized neurodevelop-
ment may, therefore, be a feature of ASD, affecting
widespread cortical regions with diverse functions.
Neurogenetic disorders, CNV, and rare
neurodevelopmental conditions
Several neurodevelopmental disorders arise due to the
abnormal duplication or deletion of segments of the gen-
ome. ENIGMA has dedicated WGs studying 22q11DS,
Gaucher’s disease, and Hepatic Glycogen storage dis-
ease
98,99
, along with a CNV WG meta-analyzing imaging
data from carriers of several other CNVs
53,100
. Here, we
focus on the work of the two most established groups, that
examine carriers of 22q11.2 deletions and other CNVs.
ENIGMA-22q11.2 Deletion Syndrome
22q11DS is associated with a 20-fold increased risk for
psychosis, and an elevated risk for developmental
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