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MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Major depressive disorder (MDD) is highly prevalent and associated with considerable morbidity, yet its pathophysiology remains only partially understood. While numerous studies have investigated the neurobiological correlates of MDD, most have used only a single neuroimaging modality. In particular, diffusion tensor imaging (DTI) studies have failed to yield uniform results. In this context, examining key tracts and using information from multiple neuroimaging modalities may better characterize potential abnormalities in the MDD brain. This study analyzed data from 30 participants with MDD and 26 healthy participants who underwent DTI, magnetic resonance spectroscopy (MRS), resting-state functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). Tracts connecting the subgenual anterior cingulate cortex (sgACC) and the left and right amygdala, as well as connections to the left and right hippocampus and thalamus, were examined as target areas. Reduced fractional anisotropy (FA) was observed in the studied tracts. Significant differences in the correlation between medial prefrontal glutamate concentrations and FA were also observed between MDD and healthy participants along tracts connecting the sgACC and right amygdala; healthy participants exhibited a strong correlation but MDD participants showed no such relationship. In the same tract, a correlation was observed between FA and subsequent antidepressant response to ketamine infusion in MDD participants. Exploratory models also suggested group differences in the relationship between DTI, fMRI, and MEG measures. This study is the first to combine MRS, DTI, fMRI, and MEG data to obtain multimodal indices of MDD and antidepressant response and may lay the foundation for similar future analyses., (Published 2019. This article is a U.S. Government work and is in the public domain in the USA.)

Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779-791, 2017. © 2016 Wiley Periodicals, Inc., (Published 2016. This article is a U.S. Government work and is in the public domain in the USA.)

Multiple techniques exist for the automated segmentation of magnetic resonance images (MRIs). The validity of these techniques can be assessed by evaluating test-retest reliability, interscanner reliability, and consistency with manual segmentation. We evaluate these measures for the FSL/FIRST subcortical segmentation tool. We retrospectively analyzed 190 MRI scans from 87 subjects with mood or anxiety disorders and healthy volunteers scanned multiple times on different platforms (N = 56) and/or the same platform (N = 45, groups overlap), and 146 scans from subjects who underwent both high-resolution and whole brain imaging in a single session, for comparison with manual segmentation of the hippocampus. The thalamus, caudate, putamen, hippocampus, and pallidum were reliably segmented in different sessions on the same scanner (Intraclass correlation coefficient (ICC) > 0.83 scanners and diagnostic groups pooled). In these regions, the range of between platform reliabilities were lower (0.527 < ICC < 0.953), although values below 0.7 were due to systematic differences between platforms or low reliability in the hippocampus between eight- and single-channel coil platforms. Accumbens and amygdala segmentations were generally unreliable within and between scanning platforms. ICC values for hippocampal volumes between automated and manual segmentations were acceptable (ICC > 0.7, groups pooled), and both methods detected significant differences between genders. In addition, FIRST segmentations were consistent with manual segmentations (in a subset of images; N = 20) in the left caudate and bilateral putamen. This retrospective analysis assesses realistic performance of the algorithm in conditions like those found in multisite trials or meta-analyses. In addition, the inclusion of psychiatric patients establishes reliability in subjects exhibiting volumetric abnormalities, validating patient studies., (Copyright © 2012 Wiley Periodicals, Inc., a Wiley company.)