Neuroimaging, genetics, and personalized psychiatry: Developments and opportunities from the ENIGMA consortium

Chapter 41

Neuroimaging, genetics, and personalized psychiatry: Developments and opportunities from the ENIGMA consortium Lianne Schmaala,b, Christopher R.K. Chingc, Agnes B. McMahonc, Neda Jahanshadc,d,h and Paul M. Thompsonc,d,e,f,g,h a

Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia, b Centre for Youth Mental Health, The University of

Melbourne, Parkville, VIC, Australia, c Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States, d Department of Neurology, University of Southern California, Los Angeles, CA, United States, e Department of Psychiatry, University of Southern California, Los Angeles, CA, United States, f Department of Radiology, University of Southern California, Los Angeles, CA, United States, g Department of Pediatrics, University of Southern California, Los Angeles, CA, United States, h Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States

1 Introduction Current intervention strategies for treating mental illnesses are not particularly effective. For example, cognitive behavioral therapy and pharmacotherapy—the two most common treatments for depression, which is one of the most common and prevalent psychiatric illnesses worldwide—are both only moderately effective. Only around one-third of patients with depression benefit from the first antidepressant they are prescribed, and more than 40% of patients do not achieve remission, even after two optimally delivered trials of antidepressant medications (Nierenberg et al., 2006). In addition, only around 50% of people with major depressive disorder (MDD) respond to psychotherapy (Cuijpers et al., 2014). The overall limited treatment response may, in part, be explained by the fact that mental illnesses are complex heterogeneous disorders with differing underlying biological mechanisms that require treatments that can target these differing mechanisms. Unfortunately, these targeted treatments are currently not available. Over the past few decades, there have been increased efforts to find markers that can assist in optimizing individualized care for people with mental illness. This “precision medicine” approach aims to personalize prevention and intervention strategies to individual people by taking into account an individual’s characteristics such as their age, sex, genes, personality, blood markers, lifestyle, and environment. Biomarkers have been of particular interest in this respect, as measurable indicators of pathological biological processes associated with mental disorders could help to establish biologically based diagnoses. Biomarkers may also support the detection and development of novel treatments with innovative mechanisms of action, monitor drug effects, and predict who might benefit most from treatments targeting specific biological processes. Biomarkers are defined as objective biological measures that can be diagnostic, that is, index a biological process that discriminates between health and disease, or between different diseases. They may also be prognostic or predictive, that is, reflect a biological process associated with progression of a disease or treatment response (Atkinson et al., 2001). Diagnostic biomarkers may assist in (differential) diagnosis of mental illnesses and provide biological targets for development of new treatments. For example, differentiation between MDD and bipolar disorder (BD) in an early phase is important, as misdiagnosis may result in inadequate pharmacological therapy. However, current diagnostic tools poorly distinguish between the depressed episode of MDD and BD due to comparable symptom profiles (Goodwin et al., 2008). Diagnostic biomarkers may improve psychiatric diagnoses by underpinning them with pathophysiological evidence, thereby aligning psychiatric classification with classification systems used in other areas of medicine (Moffitt et al., 2008). Prognostic biomarkers may be used to predict the onset of a mental disorder, which is of particular importance in child and adolescent psychiatry, as well as the course of mental illness, while predictive biomarkers can be used to predict treatment response. Personalized Psychiatry. https://doi.org/10.1016/B978-0-12-813176-3.00041-9 Copyright © 2020 Elsevier Inc. All rights reserved.

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The majority of studies aimed at identifying clinically useful biomarkers have focused on genetic variants and alterations in brain structure and function associated with mental disorders. Technological advances in genetic sequencing and neuroimaging over the past few decades have fueled the search for biomarkers. In the early 2000s, hope was expressed that these technological advances would help to identify new targets for treatment and lead to a biologically supported psychiatric classification system. More than a decade later, we have to conclude that genetics and neuroimaging have not yet fulfilled their promise in identifying clinically relevant biomarkers for mental disorders (e.g., Fond et al., 2015; Gadad et al., 2018), although much progress has been made in identifying genetic or imaging markers that differentiate patients at the group level. Despite intensive genetic and neuroimaging research in the last decades, we still have a limited understanding of the exact genetic and neurobiological underpinnings of mental disorders. Key issues that have hampered progress include, but are not limited to (e.g., see Kapur, Phillips, & Insel, 2012 for a discussion of other key issues), underpowered studies and a lack of reproducible findings.

2

Underpowered studies and the crisis of reproducibility

Historically, most genetic and neuroimaging studies in psychiatry have been conducted with small sample sizes. As a consequence, many studies are underpowered—resulting in a lower probability of finding “true” effects (low-powered studies produce more false negatives than high-powered studies), and an exaggerated estimate of the effect size when a true effect is discovered (Button et al., 2013). If, for example, the true effect has a small effect size—which is often the case for differences between patients and controls in genetic risk or brain measures—only studies with small sample sizes that, by chance, overestimate the magnitude of the effect will pass the threshold for discovery (Ioannidis, 2008). Underpowered studies hamper replication of significant findings, because if the effect sizes of the original study are inflated, power calculations to determine the sample size for a replication study will be too optimistic, and replication studies will tend to show smaller effect sizes that may not reach the threshold for statistical significance (Miller, 2009). Hence, underpowered studies have led to inconsistent and poorly replicated genetic and neuroimaging findings in psychiatry (e.g., M€uller et al., 2017; Sullivan, 2007). Use of larger sample sizes or performing meta-analyses are considered to be important ways to counteract these issues. However, recruiting a large sample is often difficult, because of limited access to patient populations or scanning facilities, and the high costs associated with acquiring large sets of genetic or neuroimaging data. Problems with retrospective meta-analyses are the potential overrepresentation of positive findings in the published literature (the “file drawer” problem), and a lack of harmonization of data processing and statistical analysis methods across the different studies included in the meta-analysis. Worldwide pooling of existing genetic and/or neuroimaging datasets represents a highly effective alternative to tackle issues associated with underpowered studies and poor reproducibility, because it (i) makes optimal use of valuable and costly existing datasets, (ii) collates large datasets relatively cheaply, (iii) controls publication bias, (iv) allows standardization of protocols for data processing and analysis, and (v) combines the expertise of hundreds of professionals in the fields of neuroimaging, psychiatry, and mathematics. The Psychiatric Genomics Consortium (PGC) provides one good example of how worldwide sharing of datasets can accelerate the discovery of the biological bases that contribute to mental illnesses, and can serve as potential new drug targets. Although most complex disorders, including mental illnesses, are moderately to highly heritable (ranging from 30% to 80%; Geschwind & Flint, 2015), effects of individual genetic variants are small, and individually, tend to explain less than 1% of the trait variance, or the risk for mental disorders (e.g., Farrell et al., 2015; Johnson et al., 2017). Traditionally, genetic studies focused on investigating associations between a priori hypothesized single genes and mental disorders—so-called candidate gene studies. However, candidate gene studies have produced very extensive and conflicting results on gene associations for many mental disorders, with high rates of false positive findings and low rates of replication. Therefore, in the past decade, the focus in psychiatric genetic research has shifted to genome-wide association (GWAS) studies, which are hypothesis-free data-driven studies that scan millions of common variants across the whole genome to identify significant associations between individual genetic variations and mental disorders. Very large sample sizes—in the order of tens of thousands of patients and controls—are typically needed for GWAS discovery and replication due to the small effect sizes of individual genetic polymorphisms, and due to the large number of statistical comparisons across genetic markers (usually 500K to 1M) that require correction for multiple comparisons. To address this, consortia such as the Psychiatric Genomics Consortium (PGC) have emerged, that involve data sharing across many genotyped samples worldwide. These efforts have led to significant breakthroughs in the identification of numerous novel genetic risk loci for mental disorders. For example, in 2014, 128 independent genome-wide significant single nucleotide polymorphisms (SNPs) across 108 genomic loci were identified in patients with schizophrenia in a GWAS meta-analysis of data from more than 36,000 patients and 113,000 controls by the PGC (Ripke et al., 2014). Similar

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progress in other psychiatric disorders has been published (Hou et al., 2016; Wray, Sullivan, & Wray, 2018) or is underway (Sullivan et al., 2018). In this chapter, we present and discuss another worldwide data sharing initiative: the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. ENIGMA was initiated to address issues related to small sample sizes in imaging genetics and neuroimaging research. One goal of ENIGMA is to identify genetic influences on brain structure and function, as intermediate phenotypes for mental and neurological disorders. A second goal is to study patterns of brain abnormalities in mental and neurological disorders that are robust and replicable across many different samples worldwide. First, we provide an overview of the history, aims, and organizational structure of ENIGMA, followed by findings on genetic variants of brain measures and structural brain alterations associated with mental disorders. Next, we will discuss progress to date, as well as the potential of future ENIGMA work with regard to identifying clinically useful biomarkers. Finally, we discuss some of the main challenges faced by worldwide data sharing initiatives such as ENIGMA, which involve sociological, ethical, and technical considerations.

3 The enhancing neuroimaging genetics through meta-analysis consortium The ENIGMA Consortium was founded in 2009 with the aim of boosting statistical power to detect genetic influences on brain measures, and identify disease effects on the brain that are replicable across many samples worldwide. As of April 2018, ENIGMA has brought together scientists and datasets from 39 countries working collaboratively to study factors that influence brain structure and function in health and disease, using magnetic resonance imaging (MRI) and other neuroimaging modalities (Fig. 1). ENIGMA has published the largest genetic studies of the brain, in partnership with other consortia (Adams et al., 2016; Hibar et al., 2017; Satizabal et al., 2017), mapping genome-wide effects of more than a million genetic loci in more than 35,000 brain MRI scans. In addition, ENIGMA has published neuroimaging studies that contain two hundred times as many samples as studies that were considered large and well-powered just 5 years ago (N
50), and is now publishing studies with more than 10,000 individuals. The current organizational structure of ENIGMA is shown in Fig. 2. ENIGMA’s initial aim was to perform genome-wide analyses to identify common genetic variants that affect brain structure. Because many successful GWAS studies of complex traits, including psychiatric disorders, required samples of more than 75,000 people, many researchers hoped that brain measures might offer a more efficient way to discover genes involved in psychiatric disorders. Psychiatric disorders are highly heterogeneous, with clinical factors and biological mechanisms likely to differ substantially, even among people diagnosed with the same disorder. Most major mental illnesses are

FIG. 1 Map of ENIGMA members across the world, by working group; an interactive version is available online at http://enigma.usc.edu/about-2/map/.

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Cross disorders I

Scz

Addiction

MDD

Bipolar

PTSD

HIV

Relatives

Early onset psychosis

Suicidal thoughts and behaviors

Schizotypy

Rare genetic disorders Lysosomal storage 22q DS diseases

Sleep

Cancer and chemotherapy

Anxiety disorder Panic disorder

Social anxiety

Generalized AD

Cross disorders II OCD

Neurological disorders

Eating disorders

Brain injury

ASPD and CD

Autism

Ataxia

Epilepsy FTD

Stroke recovery Tourette’s syndrome

ADHD

Irritability

Parkinson’s disease

dMRI

Voxelwise analyses

EEG

Shape

rsfMRI

Hippocampal subfields

Sulci

tbfMRI

Connectomics, methods, and tech

ENIGMA core

support Infrastructure development

Genomics

Healthy variation aging

ORIGINs

GWAS

CNVs

Lifespan

Transgendered persons

Epigenetics

Evolution

Laterality

Plasticity

FIG. 2 ENIGMA is organized as a set of working groups that perform or support international studies on specific topics. The Clinical Working groups study specific diseases of the brain, including psychiatric and neurological disorders, ranging from affective disorders to substance use disorders, anxiety, PTSD, and even infectious diseases such as HIV/AIDS, and monogenetic disorders such as 22q Deletion Syndrome. In parallel, Technical working groups develop harmonized analytic approaches for genomic data (e.g., GWAS, copy number variants, and epigenetic analyses) and various kinds of neuroimaging data (including MRI, diffusion-weighted imaging, resting state functional MRI, and EEG).

thought to have a complex genetic architecture with polygenic influences and gene-by-environment interaction effects. These underlying complexities make it very challenging to identify genetic variants that are robustly associated with the disease, as their effects may also depend on complex interactions between multiple genes of mostly small to modest effect. In line with previous observations in other medical conditions such as heart disease (Cohen, Boerwinkle, Mosley, & Hobbs, 2006) and diabetes (Walters et al., 2010), it was assumed that genetic association is stronger at the level of biological

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substrates related to the psychiatric disorder than a psychiatric diagnosis itself. Therefore, genetic risk factors for psychiatric disorders may be more easily detected by examining intermediate phenotypes such as brain measures obtained from MRI (Gottesman & Gould, 2003). Intermediate phenotypes, or endophenotypes, may have a simpler genetic architecture than a clinical diagnosis (Geschwind & Flint, 2015), and brain measures may also offer a more precise, objective, and reproducible phenotype than a clinical diagnostic scale (Potkin et al., 2009). Thus, genome-wide screening of brain measures was considered to be a relatively efficient approach to identify otherwise weak or unobservable genetic effects on complex phenotypes of interest (e.g., psychiatric disorder). As collecting a sufficient amount of brain imaging data for GWAS analysis may be difficult or too costly for any one individual site, the need for a global community effort in imaging genetics became clear, and the ENIGMA consortium was initiated to combine existing genomic and imaging data around the globe. Building on ENIGMA’s initial successes in imaging genetics, which are further described below, disease “working groups” were initiated to study patterns of brain abnormalities in major psychiatric, neurodevelopmental, neurological, and neurogenetic disorders. In this chapter, we specifically focus on the psychiatric working groups. Since the initiation of the first ENIGMA psychiatric working groups in 2012 that focused on MDD, bipolar disorder, schizophrenia, and attention deficit hyperactivity disorder (ADHD), more than 20 additional psychiatric working groups were formed. For a complete overview of the different psychiatric working groups, see Table 1. These working groups typically analyze imaging data from 5000 to 10,000 people; eight of the mental disorder working groups have recently published the largest imaging studies to date of the disorders they study (schizophrenia, bipolar disorder, MDD, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), ADHD, autism spectrum disorders, substance use disorder, see Table 1). Projects conducted within and across the ENIGMA psychiatric working groups are designed to: (1) identify imaging markers of specific mental disorders that are consistent across many samples worldwide; (2) investigate how factors including age, sex, and disease characteristics (e.g., stage, severity and duration of mental illness, age of onset, and medication use) moderate these brain alterations; and (3) identify common and unique patterns of brain alterations across different mental disorders. The ENIGMA psychiatric working groups are supported by ENIGMA Methods working groups. These working groups are dedicated to developing and large-scale testing of imaging and genomics methods. For example, some of these ENIGMA Methods working groups develop standardized protocols to harmonize processing and quality assurance of imaging data, including structural MRI, diffusion-weighted MRI, resting state functional MRI and electroencephalography (EEG), across all samples in order to reduce statistical heterogeneity and researcher degrees of freedom, and to ensure or evaluate reproducibility. Brain measures derived from the ENIGMA protocols have shown good reliability and heritability (Acheson et al., 2017; Adhikari, Jahanshad, Reynolds, Cox, & Nichols, 2018; Jahanshad et al., 2013; Kochunov et al., 2015). ENIGMA is dedicated to “open science” and, therefore, ENIGMA protocols are publicly available on the ENIGMA website, and have been widely used in hundreds of ongoing and published projects. ENIGMA has successfully harmonized imaging measures across existing datasets from around the world to achieve unprecedented power in detecting brain alterations associated with several mental disorders. These efforts have also driven the largest, and most successful, replicated findings of specific genetic variants that associate with brain structure. The findings of these studies will be discussed next.

4 ENIGMA imaging genomics and genome-wide association studies By pooling brain MRI scans and genome-wide genetic data from thousands of individuals, ENIGMA performed a coordinated analysis with the CHARGE Consortium to discover the first genetic loci associated with the volume of the hippocampus—the brain’s key center for learning and memory (Stein et al., 2012). Over recent years, ENIGMA and CHARGE have worked to expand this analysis—the total sample size now includes more than 25,000 individual samples. By adding more data, the consortia have replicated earlier findings and identified new genetic risk factors for smaller hippocampal volumes (Hibar et al., 2015; Hibar et al., 2017). These genomic risk factors may provide some insight into the biological underpinnings and mechanisms underlying diseases that affect the hippocampus, and the structure and function of the brain, as evidenced by a growing number of collaborative ENIGMA GWAS studies, including of all subcortical volumes (Hibar et al., 2015; Satizabal et al., 2017), cortical surface area and thickness (Grasby et al., 2018); and global brain function as determined by EEG power (Smit et al., 2018). Beyond GWAS, the ENIGMA-CNV working group also began to perform the largest imaging studies of rare genetic variants (Sonderby et al., 2018). Although CNVs are implicated in autism, epilepsy, and other disorders, their rareness makes it hard to perform a concerted study of their effects. In one study, the 16p11.2 distal CNV affected intracranial volume, and the putamen and pallidum specifically, a pattern replicated in data from the DeCODE Genetics consortium.

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TABLE 1 Overview ENIGMA GWAS and psychiatric disorder working groups, including current sample sizes (July 2018), number of sites, and publications per working group Group

Project

N of samples

N of sites

Genome-wide association

Cortical

51,238

Subcortical

Reference

Journal

58

Grasby et al. (2018)

BioRxiv

30,717

50

Hibar et al. (2015)

Nature

Cortical

10,105

20

Schmaal et al. (2017)

Molecular Psychiatry

Subcortical

8927

15

Schmaal et al. (2016)

Molecular Psychiatry

DTI

2907

18

van Velzen et al. (2019)

Molecular Psychiatry

Childhood adversity

3106

12

Frodl et al. (2017)

Journal of Psychiatric Research

Suicidality

3097

20

Renteria et al. (2017)

Translational Psychiatry

Cortical

3665

27

Boedhoe et al. (2018)

American Journal of Psychiatry

Subcortical

3589

35

Boedhoe et al. (2017)

American Journal of Psychiatry

Cortical

9572

39

van Erp et al. (2018)

Biological Psychiatry

Subcortical

4568

15

van Erp et al. (2016)

Molecular Psychiatry

DTI

4322

29

Kelly et al. (2018)

Molecular Psychiatry

Positive symptoms

1987

17

Walton et al. (2017)

Acta Psychiatry

Negative symptoms

1985

17

Walton et al. (2017)

Psychological Medicine

Autism spectrum disorder

Cortical and subcortical

3222

49

Van Rooij et al. (2018)

American Journal of Psychiatry

Attention deficit hyperactivity disorder

Cortical

4200

36

Hoogman et al. (2019)

American Journal of Psychiatry

Subcortical

3242

23

Hoogman et al. (2017)

The Lancet Psychiatry

Cortical

6503

28

Hibar et al. (2018)

Molecular Psychiatry

Subcortical

4304

20

Hibar et al. (2016)

Molecular Psychiatry

Posttraumatic stress disorder

Subcortical

1868

16

Logue et al. (2018)

Biological Psychiatry

Substance use disorders

Cortical and subcortical

3420

23

Mackey et al. (2018)

American Journal of Psychiatry

Anxiety disorders

Cortical and subcortical

4210

69

Major depressive disorder

Obsessive compulsive disorder

Schizophrenia

Bipolar disorder

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Additional, more recent discoveries were made for the effect of the 1p21.1 distal CNV in intracranial volume, cortical surface area, and caudate volume—deletion (n ¼ 16), and duplication carriers (n ¼ 9) of the 1q21.1 CNV were compared with noncarriers (n ¼ 19,678) from the same scanner sites. This CNV is associated with delayed development, schizophrenia, congenital heart defects, and obesity, and the brain phenotypes may at least partially explain the various neurodevelopmental abnormalities observed in 1q21.1 distal carriers. Although ENIGMA’s recent GWAS have discovered more than 100 genome-wide significant variants associated with brain structural measures derived from MRI, the fact that tens of thousands of scans were needed to discover these variants suggests that initial hopes were unfounded that brain imaging GWAS would be much more efficient than psychiatric GWAS in discovering genetic markers associated with traits of interest. Recently, however, Holland and colleagues (Holland et al., 2018) performed a comprehensive analysis of many neuroimaging and psychiatric traits, modeling the sample sizes needed to discover common variants that account for various fractions of the genetic variance for each of the traits assessed. They concluded that some traits, such as HDL cholesterol levels in the blood, do indeed have a simpler genetic architecture than traits such as schizophrenia or MDD. As a result, smaller samples are sufficient to discover genetic loci that account for a given fraction of the genetic variance. Initial evidence suggests that some neuroimaging measures may indeed have a simpler genetic architecture than cognitive metrics such as intellectual attainment, with more of the genetic variance accounted for by a smaller number of genetic loci. Of all the quantitative traits evaluated, educational attainment had the highest polygenicity—slightly larger than that for schizophrenia; the SNP “discoverability” was lower for MDD than for bipolar disorder or schizophrenia, lending insight into the vast sample sizes needed in GWAS for some psychiatric disorders.

5 ENIGMA psychiatric neuroimaging studies With the largest combined imaging samples of individuals with mental disorders, ENIGMA’s psychiatric working groups are well positioned to identify patterns of brain alterations associated with mental disorders, and to test their replicability and reliability across many different samples worldwide. The first ENIGMA studies of mental disorders were conducted using a meta-analysis approach. Following this approach, investigators participating in ENIGMA psychiatric working groups ran harmonized ENIGMA processing, quality assurance (QA), and statistical analysis protocols on their data locally, and sent back the QA output and analysis results to a central analysis site where the data was combined with data from other sites for a meta-analysis. In this way, large sample sizes can be acquired without the sometimes burdensome requirements of large-scale data transfers or issues related to sharing of individual raw imaging data. More recently, a mega-analysis approach that aggregates de-identified, individual-level data, has also been adopted by many ENIGMA psychiatric working groups. ENIGMA meta- and mega-analyses of subcortical volume alterations have shown robust evidence for a smaller hippocampus across various mental disorders, including schizophrenia (SZ; van Erp et al., 2016), bipolar disorder (BD; Hibar et al., 2016), MDD (Schmaal et al., 2016), OCD (Boedhoe et al., 2017), PTSD (Logue et al., 2018), substance and alcohol use disorders (Mackey et al., 2018), and ADHD (Hoogman et al., 2017), but not autism spectrum disorder (ASD; Van Rooij et al., 2018). The hippocampus plays a key role in the formation of new memories, as well as learning and emotion processing (Desmedt, Marighetto, Richter-Levin, & Calandreau, 2015; Frodl et al., 2006; Hickie et al., 2005). The hippocampus is particularly prone to effects of chronic stress associated with a range of mental disorders. Chronic stress induces elevated glucocorticoid levels due to chronic hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis, and the hippocampus has a particularly high expression of glucocorticoid receptors (Sapolsky, Krey, & McEwen, 1984). Chronic elevated levels of glucocorticoids may promote atrophy of the hippocampus via remodeling and downregulation of growth factors, including brain-derived neurotrophic factor (Campbell & MacQueen, 2003). Additional volume reductions were less consistent across mental disorders, with smaller volumes of the amygdala and nucleus accumbens observed in SZ, ASD, ADHD, and substance use disorders; of the thalamus in SZ, BD, and alcohol use disorder; of the putamen in ASD, ADHD, and alcohol use disorder; and smaller volumes of the pallidum in ASD and alcohol use disorder, but larger pallidum volumes in SZ and adults with OCD (Boedhoe et al., 2017; Hibar et al., 2016; Hoogman et al., 2017; Mackey et al., 2018; van Erp et al., 2016; Van Rooij et al., 2018). In addition, the structure of frontal, temporal, and parietal brain regions seems to be affected in multiple mental disorders, including regions such as the dorsolateral prefrontal cortex, orbitofrontal cortex, cingulate cortex, insula, inferior parietal cortex and temporal gyri, as shown by studies produced by the ENIGMA SZ, BD, MDD, Addiction, and OCD working groups (Boedhoe et al., 2018; Hibar et al., 2018; Mackey et al., 2018; Schmaal et al., 2017; van Erp et al., 2018). These regions have been implicated in a wide range of cognitive functions, including emotion processing and regulation (dorsolateral prefrontal cortex, cingulate cortex, temporal gyri; Kret & Ploeger, 2015), social cognition (dorsolateral

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prefrontal cortex, temporal gyri; Ferna´ndez, Mollinedo-Gajate, & Pen˜agarikano, 2018), motivation and reward (orbitofrontal cortex, anterior cingulate cortex; Haber, 2016), interoception (insula, anterior cingulate cortex; Seth & Friston, 2016), and decision-making (dorsolateral prefrontal cortex, cingulate cortex, orbitofrontal cortex, inferior parietal cortex; Starcke & Brand, 2012), that may contribute to the broad spectrum of emotional, cognitive, and behavioral disturbances observed in these mental disorders. Interestingly, ASD was associated with lower cortical gray matter thickness in temporal brain regions, in line with findings in SZ, BD, MDD, and substance use disorders, but with greater cortical thickness in frontal regions (Van Rooij et al., 2018). In general, the largest and most widespread effects of structural brain alterations were found in SZ (maximum Cohen’s d effect size 0.53), followed by BD and OCD (maximum Cohen’s d effect size 0.30), ASD and ADHD (maximum Cohen’s d effect size 0.20), and MDD, substance use disorders, and PTSD (maximum Cohen’s d effect size 0.14–0.17) compared with healthy controls. Some of the published findings of ENIGMA psychiatric working groups are summarized in Fig. 3. The ENIGMA psychiatric working groups have recently started to analyze measures from other neuroimaging modalities than structural MRI, for example, diffusion MRI, which allows investigation of structural white matter connections between different brain regions, and measures of white matter microstructure. The first results from the ENIGMA SZ working group show that schizophrenia is not only associated with widespread alterations in the structure of brain regions, but also with widespread alterations in structural connections between these brain regions (Kelly et al., 2018). Other working groups are finalizing their diffusion MRI analyses at the time of writing this chapter. Structural imaging protocols and tools have thus far been the mainstay of ENIGMA, driving more than 20 research papers to date. Although our previous findings have provided important insights into structural brain abnormalities in mental disorders, it remains unclear how alterations observed in individual regions interact and contribute to disturbances in brain functioning. Therefore, an

FIG. 3 Global studies of brain disease. ENIGMA recently published the largest neuroimaging studies of six psychiatric disorders, including schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), alcohol use disorder, autism spectrum disorder, and obsessive compulsive disorder, investigating the differences in cortical thickness in individuals with psychiatric disorders compared with healthy controls. The color bar represents Cohen’s d effect sizes.

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important next step is to extend our previous work by investigating the impact of various mental disorders on functional brain circuitries. To date, ENIGMA’s work has provided more definitive answers to questions of the extent of structural brain abnormalities in mental disorders by addressing issues of poor replication, unreliable results, and an overestimation of effect sizes in previously underpowered studies. These findings of structural brain alterations associated with mental disorders from the ENIGMA consortium can (i) help to prioritize brain measures for future analyses aimed at unraveling underlying cellular, molecular, and genetic mechanisms of brain abnormalities in mental disorders, (ii) identify novel treatment targets, and (iii) generate new hypotheses about the impact of mental disorders on the integrity of the brain (or vice versa) that can be tested in future, more targeted studies (e.g., using a longitudinal design).

6 Toward personalized psychiatry The work conducted by the ENIGMA psychiatric working groups described herein were derived from comparisons between people with and without a mental illness, as determined by current symptom-based classification schemes, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) or ICD. However, these DSM or ICD-based categories of mental disorders have been criticized in recent years, as they have shown low diagnostic validity and lack of specificity (e.g., Kapur et al., 2012; Kotov et al., 2017; McGorry, Nelson, Goldstone, & Yung, 2010; Stein, Lund, & Nesse, 2013). The main criticism is that current classification schemes assume that mental disorders are discrete entities. However, many mental disorders are instead complex, highly heterogeneous disorders, and distinct pathophysiological mechanisms may cause a similar presentation of symptoms in different people, which would require different treatments. Thus, if people are merely classified on the basis of the presence of an DSM or ICD diagnosis, distinct pathophysiological mechanisms underlying different subgroups of patients may remain undetected. Consequently, different groups of people with the same diagnosis of a mental disorder, but with distinct underlying mechanisms, cannot be distinguished, and, hence, cannot be stratified to different interventions. Not surprisingly, reducing the diagnostic heterogeneity of mental disorders is widely recognized as the next frontier in progressing research into underlying pathophysiological mechanisms and treatment allocation for people suffering from mental disorders. To address the issue of the heterogeneity of mental disorders, the ENIGMA studies have investigated whether structural brain alterations are specifically linked to distinct subgroups of individuals characterized by different stages of brain development and different clinical characteristics, such as age of onset or disease stage. For example, distinct patterns of cortical structural brain alterations were observed in adolescents compared with adults with MDD. Gray matter thickness in the orbitofrontal cortex, anterior and posterior cingulate, insula, and temporal lobes was lower in adult MDD patients than controls. In contrast, adolescents with MDD showed no detectable abnormalities in cortical thickness, at least at the group level, but did show global reductions in cortical surface area (Schmaal et al., 2017). Similarly, children with OCD had a larger thalamus and thinner inferior and superior parietal cortices, whereas adults with OCD were characterized by a smaller hippocampus, a larger pallidum, lower surface area of the transverse temporal cortex, and a thinner inferior parietal cortex (Boedhoe et al., 2017, 2018). Distinct patterns, depending on age, were also observed in people with ADHD, with a smaller accumbens, amygdala, caudate, hippocampus, putamen, and intracranial volume in children, a smaller hippocampus in adolescents, and no subcortical structural brain abnormalities in adults (Hoogman et al., 2017). In ASD, the largest differences in cortical thickness occurred around adolescence (Van Rooij et al., 2018), whereas in BD and SZ, the largest differences in cortical thickness were found with increasing age (Hibar et al., 2018; van Erp et al., 2018). With regard to sex, males and females with MDD, SZ, ADHD, OCD, and ASD did not show different patterns of structural brain alterations (Boedhoe et al., 2017, 2018; Hoogman et al., 2017; Schmaal et al., 2016, 2017; van Erp et al., 2016, 2018; Van Rooij et al., 2018). However, a larger thalamus was a specific characteristic of adult females with BD (Hibar et al., 2016), and less thinning of frontal and temporal cortices of adolescent females with BD (Hibar et al., 2018). In addition, hippocampal volume reductions were more pronounced in females compared with males with PTSD (Logue et al., 2018). The heterogeneity observed in structural brain alterations associated with mental disorders can further be explained by differences in disease characteristics. More pronounced structural brain abnormalities were observed in subgroups of people with more than one episode, and an earlier age of onset of depression (Schmaal et al., 2016), a longer duration of illness in schizophrenia and bipolar disorder (Hibar et al., 2018; van Erp et al., 2016, 2018), and with higher symptom severity in ASD (Van Rooij et al., 2018) and PTSD (Logue et al., 2018). Other sources of heterogeneity may be less disorder-specific, and affect the extent of brain alterations in a similar way across various mental disorders. For example, childhood adversity is a risk factor for various mental disorders (Kessler et al., 2010). Although the effects of childhood adversity on brain structure have been examined separately in people with depression (Frodl et al., 2017) and in PTSD (Logue et al., 2018) within the ENIGMA MDD and PTSD working

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groups, efforts are currently underway to examine the transdiagnostic effects of childhood adversity. In addition, suicidal thoughts and behaviors represent another transdiagnostic disorder that may be associated with a specific, but similar, pattern of brain alterations across disorders (Renteria et al., 2017). The ENIGMA Suicidal Thoughts and Behaviors working group—the first transdiagnostic working group within ENIGMA—was recently established to investigate shared and unique brain suicidality related brain abnormalities across different mental disorders (http://enigma.ini.usc.edu/ ongoing/enigma-stb/). Suicidal behaviors are also highly etiologically heterogeneous and multifactorial in nature. Therefore, the ENIGMA Suicidal Thoughts and Behaviors working group aims to identify biopsychosocial subtypes of suicidal ideation and suicide attempt, thereby identifying distinct trajectories of suicidal behaviors in groups of people that differ in, for example, age, sex, and a constellation of various other neuroanatomical, psychosocial, cognitive, and clinical characteristics. The preceding findings from the ENIGMA psychiatric working groups indicate that structural alterations in mental disorders depend on specific characteristics, such as stage of brain development, childhood trauma, and clinical characteristics, such as stage of the disorder. These findings provide valuable insights into the biological heterogeneity of mental disorders. Machine learning methods are becoming increasingly more popular for evaluating the diagnostic or predictive value of biomarkers in previously unseen individuals (e.g., “new” patients). ENIGMA offers a unique framework to evaluate brain imaging measures as potential diagnostic biomarkers for various mental disorders, because the inclusion of many samples worldwide reflects the broad range of clinical heterogeneity in mental disorders, which is essential for creating more generalizable clinical decision support tools. In addition, the inclusion of many datasets allows the evaluation of how well a predictive model developed in a subset of datasets generalizes to new patients (other datasets). ENIGMA psychiatric working groups have evaluated patterns of structural brain alterations as a potential diagnostic biomarker of MDD (Zhu et al., 2017) or bipolar disorder (Nunes et al., 2018) in individual patients. Identifying a diagnostic biomarker for depression could be particularly relevant, as there is only minimal agreement among psychiatrists on who does and does not have major depressive disorder. For example, the field trials for the DSM-5 demonstrated an intraclass kappa of 0.28, which means that highly trained specialist psychiatrists under study conditions were only able to agree that a patient has depression between 4% and 15% of the time (Regier et al., 2013). The findings of the ENIGMA MDD and ENIGMA BD working groups showed that diagnostic status (having a mental disorder vs. not having a mental disorder) could be predicted in individual patients with accuracies ranging between 60% and 70% (Nunes et al., 2018; Zhu et al., 2017), which is not yet sufficient for these models to become clinically useful. More work is needed to improve the diagnostic classification accuracies, perhaps by including additional (biological) predictors to the model, including data from other imaging modalities, as well as genetic or plasma markers, or even mobile sensor data.

7

Challenges of large-scale data-sharing

Worldwide data-sharing initiatives such as ENIGMA are not without their challenges. Some of these challenges encompass ethical and computational issues with regard to data sharing, as well as science and data sharing policies that may vary from one research institute to another, from country to country, or even from continent to continent, and may change over time (e.g., the recent implementation of the General Data Protection Regulation [GDPR] in the European Union). This may restrict some researchers from sharing raw neuroimaging data, although sharing de-identified, individual-level data may still be feasible. Another challenge that ENIGMA is facing is the lack of harmonization in data collection. When combining already collected data across worldwide samples, data collection protocols are not prospectively harmonized. Clinical assessments, therefore, differ across studies, which limits the analysis of sources of heterogeneity, and perhaps subsequently, the identification of biomarkers. In addition, neuroimaging data were collected using different MRI scanners and different sequences, which may introduce noise and further complicate the search for robust biomarkers. By pooling data across many samples worldwide, ENIGMA psychiatric working groups include combined samples that are highly heterogeneous. In the case of MDD, for example, the working group performs studies encompassing a range of depressive phenotypes—from very mild to severe, and a broad range of previous treatments received. We argue that it is precisely for this reason that this work furnishes an important transition toward real-world clinical populations, including patients recruited from community and primary care settings, where most people with mental illness reside. By combining a large number of datasets that were collected across the world, we have had the opportunity to better estimate the effect sizes of structural brain alterations in people with mental disorders. As it turns out, many of these structural brain alterations show lower effect sizes than previously assumed based on previously published studies. However, as discussed earlier in this chapter, many of the high effect sizes observed in prior studies may have been driven by small sample sizes and the “file

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drawer” problem. Therefore, our ENIGMA findings more likely reflect the true effect sizes of structural brain alterations in a representative population of people with mental disorders with a broad range of illness severity. Nonetheless, it has become clear that because of these small effect sizes, we are actively searching for novel imaging measures, including fMRI measures, or a combination of measures that outperform the current structural MRI measures, because effect sizes between a Cohen’s d of 1.5 and 3 are likely to be required for a biomarker to be clinically useful, depending on the nature of the application (Castellanos, Di Martino, Craddock, Mehta, & Milham, 2013). Future ENIGMA studies should further investigate whether combining these neuroimaging modalities with other neuroimaging and/or clinical, psychosocial, and other biological data modalities could result in clinically useful diagnostic or predictive tools.

8 Conclusions 8.1 Future directions Findings from the ENIGMA psychiatric working groups indicate that mental disorders are associated with structural alterations in a highly dynamic way, depending on specific characteristics, such as stage of brain development and stage of the disorder. One way to better characterize the impact of different stages of brain development on brain alterations in mental disorders is by defining “normal” variability in brain structure in healthy populations. This can be achieved by mapping the normative association between age and brain structure across the entire lifespan in people without mental or neurological illnesses. These normative charts, or brain charts, may then be used to help in identifying individuals with mental disorders that may deviate from this normative pattern. This can be compared with the use of growth charts to map child development in terms of height and weight as a function of age, where deviations from a normal growth trajectory manifest as outliers within the normative range at each age. The ENIGMA Lifespan working group is currently developing these normative charts using cross-sectional data from healthy volunteers from many samples worldwide. These models could be further improved by including data on longitudinal brain changes in healthy individuals from samples included in the ENIGMA Plasticity working group. The normative charts can subsequently be used by the ENIGMA psychiatric working groups to help in identifying patients who deviate from appropriate normative charts. Importantly, the measures derived from the normative charts are modeled as deviations in individual patients (i.e., individual differences instead of group averages), which is a critical step toward personalized psychiatry. Much current neuroimaging research still focuses on characterizing group differences using the mean and standard deviation of derived measures. Crucially, this approach assumes homogeneity among members of (psychiatric) groups, with the different members resembling the “average.” Group-based findings obtained using this approach are not sufficiently informative about the individual standing in front of a clinician. To identify diagnostic, prognostic, or predictive biomarkers that can be translated into clinical practice, it is necessary to shift the design of experiments from testing for clinical differences among groups to individual-level prediction of relevant outcomes, such as diagnostic status or treatment response. In addition, the ENIGMA studies investigating the value of brain measures as diagnostic biomarkers for single disorders are an important first step, but perhaps a clinically more useful question is whether these brain measures can be used as biomarkers for differential diagnosis, or for predicting future outcomes, such as treatment response or course trajectory. Although ENIGMA may not provide the ideal platform to test the latter, because of a lack of longitudinal data and a lack in harmonization of treatment protocols in the few treatment samples included in ENIGMA, machine learning efforts are underway to evaluate structural brain measures as biomarkers for differentiating among different disorders such as depression, bipolar disorder, and schizophrenia. Still, it may prove too difficult to identify diagnostic biomarkers for broad categories such as DSM or ICD diagnoses that likely include subgroups of people with different patterns of underlying pathology. One way to address this is to identify biologically more meaningful subgroups based on different patterns of brain alterations. However, a key challenge remains as to how to detect relevant biomarkers that underlie biologically meaningful classifications of mental illness without the use of a priori defined labels such as DSM diagnoses. A potential solution for identifying neurobiologically relevant subtypes of mental disorders without using a priori defined clinical labels is to apply data-driven methods to brain measures. Such data-driven methods, also referred to as unsupervised machine learning techniques, have been successfully used to stratify mental disorders based on brain imaging measures. For example, using a hierarchical clustering approach, a recent study identified neurobiological subtypes of adult patients with a MDD diagnosis that were defined by distinct patterns of dysfunctional brain connectivity (Drysdale et al., 2016). Each neurobiological subtype was associated with a distinct clinical symptom profile. Importantly, these neurobiological subtypes predicted treatment response to repetitive transcranial magnetic stimulation more effectively than symptoms alone (Drysdale et al., 2016). Various efforts are underway within the ENIGMA psychiatric working groups to identify neurobiologically relevant subtypes of mental disorders based on structural or diffusion-weighted MRI measures.

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8.2 Conclusions Current intervention strategies for treating mental illness are not always effective, which can partly be explained by the fact that mental illnesses are complex, pathophysiologically heterogeneous disorders. Identifying diagnostic, predictive, and prognostic biomarkers of mental disorders is widely recognized as the next frontier in progressing research into underlying pathophysiological mechanisms and treatment allocation of people suffering from mental illness. Group-level analyses in mental disorders, especially those in genetics and neuroimaging, have encountered issues in statistical power and reproducibility. In order to overcome these issues, the ENIGMA consortium aims to consolidate efforts across many researchers worldwide, and increase sample sizes. Over the past 10 years since its initiation, ENIGMA has grown rapidly, and ENIGMA working groups have published the largest samples sizes in neuroimaging studies for nine major brain disorders (Table 1), which provided more definitive answers to questions of the extent of structural brain abnormalities in mental disorders. Currently, various efforts are underway within ENIGMA to address the heterogeneity in mental disorders, by identifying more pathophysiologically homogeneous subgroups of patients. Moreover, because of the inclusion of many samples worldwide, reflecting the broad range of clinical heterogeneity in mental disorders, ENIGMA offers a unique framework to evaluate brain imaging measures as potential diagnostic biomarkers for various mental disorders. Some of the current ENIGMA work focuses on individual-level—as opposed to group-level—prediction of relevant outcomes, which is a critical step toward personalized psychiatry.

Acknowledgment The ENIGMA studies reported here were supported by a range of public and private agencies worldwide, and by a grant from the NIH (U54 EB020403).

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Wray, N., Sullivan, P. F., & The Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681. Zhu, D., Li, Q., Riedel, B. C., Jahanshad, N., Hibar, D. P., Veer, I. M., … Thompson, P. M. (2017). Large-scale classification of major depressive disorder via distributed Lasso. Proceedings of SPIE, 10160. https://doi.org/10.1117/12.2256935.

Further reading Walton, E., Hibar, D. P., van Erp, T. G. M., Potkin, S. G., Roiz-Santian˜ez, R., Crespo-Facorro, B., … Ehlrich, S. (2018). Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium. Psychological Medicine, 48(1), 82–94.