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The diameter of the human pupil tracks working memory processing and is associated with activity in the frontoparietal network. At the same time, recent neuroimaging research has linked human pupil fluctuations to activity in the salience network. In this combined functional magnetic resonance imaging (fMRI)/pupillometry study, we recorded the pupil size of healthy human participants while they performed a blockwise organized working memory task (N‐back) inside an MRI scanner in order to monitor the pupil fluctuations associated neural activity during working memory processing. We first confirmed that mean pupil size closely followed working memory load. Combining this with fMRI data, we focused on blood oxygen level dependent (BOLD) correlates of mean pupil size modeled onto the task blocks as a parametric modulation. Interrogating this modulated task regressor, we were able to retrieve the frontoparietal network. Next, to fully exploit the within‐block dynamics, we divided the blocks into 1 s time bins and filled these with corresponding pupil change values (first‐order derivative of pupil size). We found that pupil change within N‐back blocks was positively correlated with BOLD amplitudes in the areas of the salience network (namely bilateral insula, and anterior cingulate cortex). Taken together, fMRI with simultaneous measurement of pupil parameters constitutes a valuable tool to dissect working memory subprocesses related to both working memory load and salience of the presented stimuli., The present report is a combined functional magnetic resonance imaging/pupillometry study investigating neural correlates of pupil fluctuations during working memory processing. We showed that pupil size between task conditions with varying working memory load correlates with the frontoparietal network and pupil change within conditions correlates with the salience network. This combined setup allows for a differentiation between working memory subprocesses related to cognitive load and salience of the presented stimuli.

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = −0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

Fear-related disorders are characterized by hyperexcitability in reflexive circuits and maladaptive associative learning mechanisms. The startle reflex is suited to investigate both processes, either by probing it under baseline conditions or by deriving it in fear conditioning studies. In anxiety research, the amplitude of the fear-potentiated startle has been shown to be influenced by amygdalar circuits and has typically been the readout of interest. In schizophrenia research, prolonged startle peak latency under neutral conditions is an established readout, thought to reflect impaired processing speed. We therefore explored whether startle latency is an informative readout for human anxiety research.We investigated potential similarities and differences of startle peak latency and amplitude derived from a classical fear conditioning task in a sample of 206 participants with varying severity levels of anxiety disorders and healthy control subjects. We first reduced startle response to stable components and regressed individual amygdala gray matter volumes onto the resulting startle measures. We then probed time, stimulus, and group effects of startle latency.We showed that startle latency and startle amplitude were 2 largely uncorrelated measures; startle latency, but not amplitude, showed a sex-specific association with gray matter volume of the amygdala; startle latencies showed a fear-dependent task modulation; and patients with fear-related disorders displayed shorter startle latencies throughout the fear learning task.These data provide support for the notion that probing startle latencies under threat may engage amygdala-modulated threat processing, making them a complementary marker for human anxiety research.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD., The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder.In the largest study of hippocampal subfields in bipolar disorder to date, from 23 sites worldwide, we report widespread reductions in nine of 12 subfields.The lack of differences between lithium users and healthy controls supports a possible protective role of lithium in bipolar disorder.

Background A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathways. Many of the identified (neuro) biological dysfunctions are “transdiagnostic”, meaning that they do not reflect diagnostic boundaries but are shared by different ICD/DSM diagnoses. The compromised biological validity of the current classification system for mental disorders impedes rather than supports the development of treatments that not only target symptoms but also the underlying pathophysiological mechanisms. The Biological Classification of Mental Disorders (BeCOME) study aims to identify biology-based classes of mental disorders that improve the translation of novel biomedical findings into tailored clinical applications. Methods BeCOME intends to include at least 1000 individuals with a broad spectrum of affective, anxiety and stress-related mental disorders as well as 500 individuals unaffected by mental disorders. After a screening visit, all participants undergo in-depth phenotyping procedures and omics assessments on two consecutive days. Several validated paradigms (e.g., fear conditioning, reward anticipation, imaging stress test, social reward learning task) are applied to stimulate a response in a basic system of human functioning (e.g., acute threat response, reward processing, stress response or social reward learning) that plays a key role in the development of affective, anxiety and stress-related mental disorders. The response to this stimulation is then read out across multiple levels. Assessments comprise genetic, molecular, cellular, physiological, neuroimaging, neurocognitive, psychophysiological and psychometric measurements. The multilevel information collected in BeCOME will be used to identify data-driven biologically-informed categories of mental disorders using cluster analytical techniques. Discussion The novelty of BeCOME lies in the dynamic in-depth phenotyping and omics characterization of individuals with mental disorders from the depression and anxiety spectrum of varying severity. We believe that such biology-based subclasses of mental disorders will serve as better treatment targets than purely symptom-based disease entities, and help in tailoring the right treatment to the individual patient suffering from a mental disorder. BeCOME has the potential to contribute to a novel taxonomy of mental disorders that integrates the underlying pathomechanisms into diagnoses. Trial registration Retrospectively registered on June 12, 2019 on ClinicalTrials.gov (TRN: NCT03984084).

BACKGROUND Conventional T2 *-weighted functional magnetic resonance imaging (fMRI) is performed with echo-planar imaging (EPI) sequences that create substantial acoustic noise. The loud acoustic noise not only affects the activation of the auditory cortex, but may also interfere with resting state and task fMRI experiments. PURPOSE To demonstrate the feasibility of a novel, quiet, T2 *, whole-brain blood oxygenation level-dependent (BOLD)-fMRI method, termed Looping Star, compared to conventional multislice gradient-echo EPI. STUDY TYPE Prospective. PHANTOM/SUBJECTS Glover stability QA phantom; 10 healthy volunteers. FIELD STRENGTH/SEQUENCE 3.0T: gradient echo (GE)-EPI and T2 * Looping Star fMRI. ASSESSMENT Looping Star fMRI was presented and compared to GE-EPI with a working memory (WM) task and resting state (RS) experiments. Temporal stability and acoustic measurements were obtained for both methods. Functional maps and activation accuracy were compared to evaluate the performance of the novel sequence. STATISTICAL TESTS Mean and standard deviation values were analyzed for temporal stability and acoustic noise tests. Activation maps were assessed with one-sample t-tests and contrast estimates (CE). Paired t-tests and receiver operator characteristic (ROC) were used to compare fMRI sensitivity and performance. RESULTS Looping Star presented a 98% reduction in sound pressure compared with GE-EPI, with stable temporal stability (0.09% percent fluctuation), but reduced temporal signal-to-noise ratio (tSNR) (mean difference = 15.9%). The novel method yielded consistent activations for RS and WM (83.4% and 69.5% relative BOLD sensitivity), which increased with task difficulty (mean CE 2-back = 0.56 vs. 0-back = 0.08, P

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. Resting-state functional magnetic resonance imaging data were obtained from the REST-meta-MDD (N=2338) and PsyMRI (N=1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN) and performance was evaluated using 5-fold cross-validation. Results were visualized using GCN-Explainer, an ablation study and univariate t-testing.Mean classification accuracy was 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes.Whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, pSpatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

BackgroundChronic stress is an important risk factor in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.MethodsUsing an established psycho-social stress task flanked by two resting-state scans, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 unmedicated participants with and without mood and anxiety disorders. To estimate block-wise changes in stress-induced brain activation and FC, we used hierarchical mixed-effects models based on denoised timeseries within a predefined stress network. We predicted inter- and intra-individual differences in stress phases (anticipation vs. acute stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.ResultsWe identified four subnetworks showing distinct changes in FC over time. Subnetwork trajectories predicted the stress phase (accuracy: 71%,ppermR2=.10,ppermR2=.08,pperm=.009), but not the presence or absence of a mood and anxiety disorder.ConclusionsSpatio-temporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response.

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = −0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

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. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.

It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD.

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi‐)genetics. Finally, we highlight points where FreeSurfer‐based hippocampal subfield studies may be optimized., Hippocampal subfield analysis is increasingly performed with the availability of automated magnetic resonance imaging segmentation methods. We give a synopsis of the FreeSurfer hippocampal subfield segmentation algorithm, measurement reliability studies and application domains. We discuss how global size and age effects can be modeled and suggest a standardized hippocampal subfield segmentation quality control procedure for improved pipeline harmonization.

Stress is an everyday experience and maladaptive responses play a crucial role in the etiology of affective disorders. Despite its ubiquity, the neural underpinnings of subjective stress experiences have not yet been elucidated, particularly at an individual level. In an important advance, Goldfarb et al.1showed recently that subjective stress and arousal levels in response to threatening stimuli were successfully predicted based on changes in hippocampal connectivity during the task using a machine learning approach. Crucially, stress responses were predicted by interpretable hippocampal connectivity networks, shedding new light on the role of the hippocampus in regulating stress reactivity2. However, the authors induced stress by displaying aversive pictures, while stress research often relies on the extensively validated Trier social stress task (TSST)3. The TSST incorporates crucial factors such as unpredictability of success and the social-evaluative threat of the stressor thereby eliciting cortisol responses more robustly compared to threatening images4. Towards generalization, cross validation within a sample as conducted by Goldfarb et al.1or independent replications are important steps, but the generalizability to different stressors allows to draw broader conclusions about the potential use of hippocampal connectivity to predict subjective stress5. Arguably, translating these findings to clinical applications would require a broad generalization of the results or the prediction algorithm to psychosocial stress. Here, we assessed the predictive performance of Goldfarb et al’s1algorithm for subjective stress in an independent sample using an MR adaption of the TSST6,7. In line with Goldfarb et al.1, we observed robust stress-induced changes in hippocampal connectivity. However, the spatial correlation of the changes in connectivity was low indicating little convergence across alleged stress paradigms. Critically, stress-induced changes of hippocampal connectivity were not robustly predictive of subjective stress across a multiverse of analyses based on connectivity changes. Collectively, this indicates that the generalizability of the reported stress connectivity fingerprint to other stressors is limited at best, suggesting that specific tasks might require tailored algorithms to robustly predict stress above chance levels.

Acute and chronic stress are important factors in the development of mental disorders. Reliable measurement of stress reactivity is therefore pivotal. Critically, experimental induction of stress often involves multiple “hits” and it is an open question whether individual differences in responses to an earlier stressor lead to habituation, sensitization, or simple additive effects on following events. Here, we investigated the effect of the individual cortisol response to intravenous catheter placement (IVP) on subsequent neural, psychological, endocrine, and autonomous stress reactivity. We used an established psychosocial stress paradigm to measure the acute stress response (Stress) and recovery (PostStress) in 65 participants. Higher IVP‐induced cortisol responses were associated with lower pulse rate increases during stress recovery (b = −4.8 bpm, p = .0008) and lower increases in negative affect after the task (b = −4.2, p = .040). While the cortisol response to IVP was not associated with subsequent specific stress‐induced neural activation patterns, the similarity of brain responses Pre‐ and PostStress was higher IVP‐cortisol responders (t[64] = 2.35, p = .022) indicating faster recovery. In conclusion, preparatory stress induced by IVP reduced reactivity in a subsequent stress task by modulating the latency of stress recovery. Thus, an individually stronger preceding release of cortisol may attenuate a second physiological response and perceived stress suggesting that relative changes, not absolute levels are crucial for stress attribution. Our study highlights that considering the entire trajectory of stress induction during an experiment is important to develop reliable individual biomarkers., Stress often involves multiple “hits” and it is an open question whether individual differences in responses to an earlier stressor lead to habituation, sensitization, or additive effects. Here, we show that a cortisol response to the placement of an intravenous catheter attenuated reactivity to a subsequent psychosocial stress task on the neural, endocrine, autonomous, and subjective level.

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research. ENIGMA MDD work is supported by NIH grants U54 EB020403 (Thompson), R01 MH116147 (Thompson), and R01 MH117601 (Jahanshad & Schmaal). LS was supported by an NHMRC Career Development Fellowship (1140764). AFFDIS cohort: this study was funded by the University Medical Center Goettingen (UMG Startfoerderung) and the research team is supported by German Federal Ministry of Education and Research (Bundesministerium fuer Bildung und Forschung, BMBF: 01 ZX 1507, “PreNeSt - e:Med”). Barcelona cohort: MJP is funded by the Ministerio de Ciencia e Innovación of the Spanish Government and by the Instituto de Salud Carlos III through a ‘Miguel Servet’ research contract (CP16–0020); National Research Plan (Plan Estatal de I + D + I 2016–2019); and co-financed by the European Regional Development Fund (ERDF). BRC DeCC cohort: CHYF is supported by NIHR BRC. Calgary cohort: supported by Canadian Institutes for Health Research, Branch Out Neurological Foundation. Cardiff cohort: supported by the Medical Research Council (grant G 1100629) and the National Center for Mental Health (NCMH), funded by Health Research Wales (HS/14/20). CLING cohort: this study was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via grants to OG (GR1950/5–1 and GR1950/10–1). CODE cohort: Henrik Walter is supported by a grant of the Deutsche Forschungsgemeinschaft (WA 1539/4–1). The CODE cohort was collected from studies funded by Lundbeck and the German Research Foundation (WA 1539/4–1, SCHN 1205/3–1, SCHR443/11–1). DIP-Groningen cohort: this study was supported by the Gratama Foundation, the Netherlands (2012/35 to NG). Edinburgh cohort: The research leading to these results was supported by IMAGEMEND, which received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 602450. This paper reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award 104036/Z/14/Z. FOR2107-Marburg cohort: funded by the German Research Foundation (DFG, grant FOR2107 KR 3822/7–2 to AK; FOR2107 KI 588/14–2 to TK and FOR2107 JA 1890/7–2 to AJ). Houston cohorts: supported in part by NIMH grant R01 085667 and the Dunn Research Foundation. JCS is supported by the Pat Rutherford, Jr. Endowed Chair in Psychiatry. IMH Study cohort: supported by funding from NHG (SIG/15012) and NMRC CISSP (2018). Melbourne cohort: funded by National Health and Medical Research Council of Australia (NHMRC) Project Grants 1064643 (Principal Investigator BJH) and 1024570 (Principal Investigator CGD). Minnesota cohort: the study was funded by the National Institute of Mental Health (K23MH090421; Dr. Cullen) and Biotechnology Research Center (P41 RR008079; Center for Magnetic Resonance Research), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, and the Minnesota Medical Foundation. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute. Münster cohort: funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5–1 and DA1151/5–2 to UD; SFB-TRR58, Projects C09 and Z02 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD). NESDA cohort: The infrastructure for the NESDA study (www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10–000–1002) and is supported by participating universities (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen) and mental health care organizations, see www.nesda.nl. Pharmo cohort: supported by ERA-NET PRIOMEDCHILD FP 6 (EU) grant 11.32050.26. PSYABM-NORMENT: supported by the Research Council of Norway (project number 229135). The South East Norway Health Authority Research Funding (project number 2015052). The Department of Psychology, University of Oslo, Norway. San Francisco cohort: supported by NIH/NCCIH 1R61AT009864–01A1. NIMH R01MH085734. SHIP and SHIP-trend cohorts: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. Stanford cohorts: this work was supported by NIH grant R37 MH101495. The BiDirect Study was supported by grants from the German Federal Ministry of Education and Research (BMBF; grants FKZ-01ER0816 and FKZ-01ER1506). MDS is partially supported by an award funded by the Phyllis and Jerome Lyle Rappaport Foundation. TCH is supported by NIMH grant 5K01MH117442. EJWVS, JL, and TFB are supported by European Research Council grant no. ERC-ADG-2014–671084 INSOMNIA. TFB is supported by a VU University Amsterdam University Research Fellowship 2016–2017. JL is supported by a VU University Amsterdam University Research Fellowship 2017–2018.

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)—three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

Standard T2* weighted functional magnetic resonance imaging (fMRI) performed with echo-planar imaging (EPI) suffers from signal loss in the ventromedial prefrontal cortex (vmPFC) due to macroscopic field inhomogeneity. However, this region is of special interest to affective neuroscience and psychiatry. The Multi-echo EPI (MEPI) approach has several advantages over EPI but its performance against EPI in the vmPFC has not yet been examined in a study with sufficient statistical power using a task specifically eliciting activity in this region. We used a fear conditioning task with MEPI to compare the performance of MEPI and EPI in vmPFC and control regions in 32 healthy young subjects. We analyzed activity associated with short (12ms), standard (29ms) and long (46ms) echo times, and a voxel-wise combination of these three echo times. Behavioral data revealed successful differentiation of the conditioned versus safety stimulus; activity in the vmPFC was shown by the contrast "safety stimulus > conditioned stimulus" as in previous research and proved significantly stronger with the combined MEPI than standard single-echo EPI. Then, we aimed to demonstrate that the additional cluster extent (ventral extension) detected in the vmPFC with MEPI reflects activation in a relevant cluster (i.e., not just non-neuronal noise). To do this, we used resting state data from the same subjects to show that the time-course of this region was both connected to bilateral amygdala and the default mode network. Overall, we demonstrate that MEPI (by means of the weighted sum combination approach) outperforms standard EPI in vmPFC; MEPI performs always at least as good as the best echo time for a given brain region but provides all necessary echo times for an optimal BOLD sensitivity for the whole brain. This is relevant for affective neuroscience and psychiatry given the critical role of the vmPFC in emotion regulation.

Brain volumetric measurements in multiple sclerosis (MS) reflect not only disease-specific processes but also other sources of variability. The latter has to be considered especially in multicenter and longitudinal studies. Here, we compare data generated by three different 3-Tesla magnetic resonance scanners (Philips Achieva; Siemens Verio; GE Signa MR750). We scanned two patients diagnosed with relapsing remitting MS six times per scanner within three weeks (T1w and FLAIR, 3D). We assessed T2-hyperintense lesions by an automated lesion segmentation tool and determined volumes of grey matter (GM), white matter (WM) and whole brain (GM+WM) from the lesion-filled T1-weighted images using voxel-based morphometry (SPM8/VBM8) and SIENAX (FSL). We measured cortical thickness using FreeSurfer from both, lesion-filled and original T1-weighted images. We quantified brain volume changes with SIENA. In both patients, we found significant differences in total lesion volume, global brain tissue volumes and cortical thickness measures between the scanners. Morphometric measures varied remarkably between repeated scans at each scanner, independent of the brain imaging software tool used. We conclude that for cross-sectional multicenter studies, the effect of different scanners has to be taken into account. For longitudinal monocentric studies, the expected effect size should exceed the size of false positive findings observed in this study. Assuming a physiological loss of brain volume of about 0.3% per year in healthy adult subjects (Good et al., 2001), which may double in MS (De Stefano et al., 2010; De Stefano et al., 2015), with current tools reliable estimation of brain atrophy in individual patients is only possible over periods of several years.

Altres ajuts: The ENIGMA-Major Depressive Disorder working group gratefully acknowledges support from the NIH Big Data to Knowledge (BD2K) award (U54 EB020403 to PMT) and NIH grant R01 MH116147 (PMT). LS is supported by an NHMRC MRFF Career Development Fellowship (APP1140764). We wish to acknowledge the patients and control subjects that have particiaped int the study. We thank Rosa Schirmer, Elke Schreiter, Reinhold Borschke and Ines Eidner for image acquisition and data preparation, and Anna Oliynyk for quality checks. We thank Dorothee P. Auer and F. Holsboer for initiation of the RUD study. We wish to acknowledge the patients and control subjects that have particiaped int the study. We thank Rosa Schirmer, Elke Schreiter, Reinhold Borschke and Ines Eidner for image acquisition and data preparation, and Anna Oliynyk for quality checks. We thank Dorothee P. Auer and F. Holsboer for initiation of the RUD study. NESDA: The infrastructure for the NESDA study (www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10-000-1002) and is supported by participating universities (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen) and mental health care organizations, see www.nesda.nl. M-JvT was supported by a VENI grant (NWO grant number 016.156.077). UCSF: This work was supported by the Brain and Behavior Research Foundation (formerly NARSAD) to TTY; the National Institute of Mental Health (R01MH085734 to TTY; K01MH117442 to TCH) and by the American Foundation for Suicide Prevention (PDF-1-064-13) to TCH. Stanford: This work was supported by NIMH Grants R01MH59259 and R37101495 to IHG. MS is partially supported by an award funded by the Phyllis and Jerome Lyle Rappaport Foundation. Muenster: This work was funded by the German Research Foundation (SFB-TRR58, Projects C09 and Z02 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD). Marburg: This work was funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5-1 and DA1151/5-2 to UD; KI 588/ 14-1, KI 588/14-2 to TK; KR 3822/7-1, KR 3822/7-2 to AK; JA 1890/ 7-1, JA 1890/7-2 to AJ). IMH-MDD: This work was supported by the National Healthcare Group Research Grant (SIG/15012) awarded to KS. Barcelona: This study was funded by two grants of the Fondo de Investigación Sanitaria from the Instituto de Salud Carlos III, by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM). The author is funded through 'Miguel Servet' research contract (CP16-0020), co-financed by the European Regional Development Fund (ERDF) (2016-2019). QTIM: We thank the twins and singleton siblings who gave generously of their time to participate in the QTIM study. We also thank the many research assistants, radiographers, and IT support staff for data acquisition and DNA sample preparation. This study was funded by White matter disturbances in major depressive disorder: a coordinated analysis across 20 international. . . 1521 the National Institute of Child Health & Human Development (RO1 HD050735); National Institute of Biomedical Imaging and Bioengineering (Award 1U54EB020403-01, Subaward 56929223); National Health and Medical Research Council, Australia (Project Grants 496682, 1009064). NIH ENIGMA-BD2K U54 EB020403 (Thompson); R01 MH117601 (Jahanshad/Schmaal). Magdeburg: M.L. and M.W. are funded by SFB 779. Bipolar Family Study: This study has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013). This paper reflects only the author's views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award (104036/Z/14/Z). Minnesota Adolescent Depression Study: The study was funded by the National Institute of Mental Health (K23MH090421), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, the Minnesota Medical Foundation, and the Biotechnology Research Center (P41 RR008079 to the Center for Magnetic Resonance Research), University of Minnesota, and the Deborah E. Powell Center for Women's Health Seed Grant, University of Minnesota. Dublin: This study was supported by Science Foundation Ireland through a Stokes Professorhip grant to TF. MPIP: The MPIP Sample comprises patients included in the Recurrent Unipolar Depression (RUD) Case-Control study at the clinic of the Max Planck Institute of Psychiatry, Munich, German. The RUD study was supported by GlaxoSmithKline. Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

BackgroundPTSD and depression commonly co-occur and have been associated with smaller hippocampal volumes compared to healthy and trauma-exposed controls. However, the hippocampus is heterogeneous, with subregions that may be uniquely affected in individuals with PTSD and depression.MethodsWe used random effects regressions and a harmonized neuroimaging protocol based on FreeSurfer (v6.0) to identify sub-structural hippocampal markers of current PTSD (C-PTSD), depression, and the interaction of these conditions across 31 cohorts worldwide (N=3,115;Mage=38.9±13.9 years). Secondary analyses tested these associations by sex and after modeling the simultaneous effects of remitted PTSD, childhood trauma, mild traumatic brain injury, and alcohol use disorder.ResultsA significant negative main effect of depression (n=800, vs. no depression, n=1456) was observed in the hippocampal tail (ß=−0.13) and CA1 (ß=−0.09) after adjusting for covariates and multiple testing (adjusted p’s (q)=0.028). A main effect of C-PTSD (n=1042 vs. control, n=1359) was not significant, but an interaction between C-PTSD and depression was significant in the CA1 (ß=−0.24, q=0.044). Pairwise comparisons revealed significantly smaller CA1 volumes in individuals with C-PTSD+Depression than controls (ß=−0.12, q=0.012), C-PTSD-only (ß=−0.17, q=0.001), and Depression-only (ß=−0.18, q=0.023). Follow-up analyses revealed sex effects in the hippocampal tail of depressed females, and an interaction effect of C-PTSD and depression in the fimbria of males.ConclusionsCollectively our results suggest that depression is a stronger predictor of hippocampal volumetry than PTSD, particularly in the CA1, and provide compelling evidence of more pronounced hippocampal phenotypes in comorbid PTSD and depression compared to either condition alone.

Background: The course of multiple sclerosis (MS) shows substantial inter-individual variability. The underlying determinants of disease severity likely involve genetic and environmental factors. Objective: The aim of this study was to assess the impact of APOE and HLA polymorphisms as well as smoking and body mass index (BMI) in the very early MS course. Methods: Untreated patients ( n = 263) with a recent diagnosis of relapsing-remitting (RR) MS or clinically isolated syndrome underwent standardized magnetic resonance imaging (MRI). Genotyping was performed for single-nucleotide polymorphisms (SNPs) rs3135388 tagging the HLA-DRB1*15:01 haplotype and rs7412 (Ɛ2) and rs429358 (Ɛ4) in APOE. Linear regression analyses were applied based on the three SNPs, smoking and BMI as exposures and MRI surrogate markers for disease severity as outcomes. Results: Current smoking was associated with reduced gray matter fraction, lower brain parenchymal fraction and increased cerebrospinal fluid fraction in comparison to non-smoking, whereas no effect was observed on white matter fraction. BMI and the SNPs in HLA and APOE were not associated with structural MRI parameters. Conclusions: Smoking may have an unfavorable effect on the gray matter fraction as a potential measure of MS severity already in early MS. These findings may impact patients’ counseling upon initial diagnosis of MS.

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3,337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc) –three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. CpG sites associated with hippocampus volume were significantly enriched within cancer-related genes and within regulatory elements containing the transcriptionally repressive histone H3K27 tri-methylation mark that is vital for stem cell fate specification. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.