Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and even variation within the normal range 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 ENIGMA Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with major depressive disorder, bipolar disorders, posttraumatic stress disorder, and other conditions, along with data from matched controls. In this overview, we explain the algorithm’s principle, 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 modelled, 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, schizophrenia, childhood trauma and posttraumatic stress disorder, inflammatory and systemic disease, alcoholism, stress regulation, neurotoxicity, as well as assessments of heritability, effects of candidate genes, genome-wide association studies and epigenetic effects. Lastly, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.

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. We obtained resting-state functional magnetic resonance imaging data 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. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73–81%). 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. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. 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.

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modelling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates common to individuals with 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 6 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.

BackgroundStress is associated with elevated risk for overweight and obesity, especially in women. Since body mass index (BMI) is correlated with increased inflammation and reduced baseline cortisol, obesity may lead to altered stress responses. However, it is not well understood whether stress-induced changes in brain function scale with BMI and if peripheral inflammation contributes to this.MethodsWe investigated the subjective, autonomous, endocrine, and neural stress response in a transdiagnostic sample (N=192, 120 women, MBMI=23.7±4.0 kg/m2; N=148, 89 women, with cytokines). First, we used regression models to examine effects of BMI on stress reactivity. Second, we predicted BMI based on stress-induced changes in activation and connectivity using cross-validated elastic-nets. Third, to link stress responses with inflammation, we quantified the association of BMI-related cytokines with model predictions.ResultsBMI was associated with higher negative affect after stress and an increased response to stress in the substantia nigra and the bilateral posterior insula (pFWE<.05). Moreover, stress-induced changes in activation of the hippocampus, dACC, and posterior insula predicted BMI in women (pperm<.001), but not in men. BMI was associated with higher baseline cortisol while cytokines were not associated with predicted BMI scores.ConclusionsStress-induced changes in the hippocampus and posterior insula predicted BMI in women, indicating that acute brain responses to stress might be more strongly related to a higher BMI in women compared to men. Altered stress-induced changes were associated with baseline cortisol but independent of cytokines, suggesting that the endocrine system and not inflammation contributes to stress-related changes in BMI.