Background Major Depressive Disorder (MDD) has been associated reliably with ruminative responding; this kind of responding is composed of both maladaptive and adaptive components. Levels of activity in the default-mode network (DMN) relative to the task-positive network (TPN), as well as activity in structures that influence DMN and TPN functioning, may represent important neural substrates of maladaptive and adaptive rumination in MDD. Methods We used a unique metric to estimate DMN dominance over TPN from blood-oxygen-level dependent data collected during eyes-closed rest in 17 currently depressed and 17 never-disordered adults. We calculated correlations between this metric of DMN dominance over TPN and the depressive, brooding, and reflective subscales of the Ruminative Responses Scale, correcting for associations between these measures both with one another and with severity of depression. Finally, we estimated and compared across groups right fronto-insular cortex (RFIC) response during initiations of ascent in DMN and in TPN activity. Results In the MDD participants, increasing levels of DMN dominance were associated with higher levels of maladaptive, depressive rumination and lower levels of adaptive, reflective rumination. Moreover, our RFIC state-change analysis showed increased RFIC activation in the MDD participants at the onset of increases in TPN activity; conversely, healthy control participants exhibited increased RFIC response at the onset of increases in DMN activity. Conclusions These findings support a formulation in which the DMN undergirds representation of negative, self-referential information in depression, and the RFIC, when prompted by increased levels of DMN activity, initiates an adaptive engagement of the TPN.

Structural and functional underconnectivity have been reported for multiple brain regions, functional systems, and white matter tracts in individuals with autism spectrum disorders (ASD). Although recent developments in complex network analysis have established that the brain is a modular network exhibiting small-world properties, network level organization has not been carefully examined in ASD. Here we used resting-state functional MRI (n = 42 ASD, n = 37 typically developing; TD) to show that children and adolescents with ASD display reduced short and long-range connectivity within functional systems (i.e., reduced functional integration) and stronger connectivity between functional systems (i.e., reduced functional segregation), particularly in default and higher-order visual regions. Using graph theoretical methods, we show that pairwise group differences in functional connectivity are reflected in network level reductions in modularity and clustering (local efficiency), but shorter characteristic path lengths (higher global efficiency). Structural networks, generated from diffusion tensor MRI derived fiber tracts (n = 51 ASD, n = 43 TD), displayed lower levels of white matter integrity yet higher numbers of fibers. TD and ASD individuals exhibited similar levels of correlation between raw measures of structural and functional connectivity (n = 35 ASD, n = 35 TD). However, a principal component analysis combining structural and functional network properties revealed that the balance of local and global efficiency between structural and functional networks was reduced in ASD, positively correlated with age, and inversely correlated with ASD symptom severity. Overall, our findings suggest that modeling the brain as a complex network will be highly informative in unraveling the biological basis of ASD and other neuropsychiatric disorders.

Normal aging and Alzheimer’s disease cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease (AD). We cover studies that employ the three primary methods to analyze functional connectivity – seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.

Rumination, or recursive self-focused thinking, has important implications for understanding the development and maintenance of depressive episodes. Rumination is associated with the worsening of negative mood states, greater affective responding to negative material, and increased access to negative memories. The present study was designed to use fMRI to examine neural aspects of rumination in depressed and healthy control individuals. We used a rumination induction task to assess differences in patterns of neural activation during ruminative self-focus as compared with a concrete distraction condition and with a novel abstract distraction condition in 14 participants who were diagnosed with major depressive disorder and 14 healthy control participants. Depressed participants exhibited increased activation in the orbitofrontal cortex, subgenual anterior cingulate, and dorsolateral prefrontal cortex as compared with healthy controls during rumination versus concrete distraction. Neural activity during rumination versus abstract distraction was greater for depressed than for control participants in the amygdala, rostral anterior cingulate/medial prefrontal cortex, dorsolateral prefrontal cortex, posterior cingulate, and parahippocampus. These findings indicate that ruminative self-focus is associated with enhanced recruitment of limbic and medial and dorsolateral prefrontal regions in depression.

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, attentiondeficit/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.

BACKGROUND Many studies report smaller hippocampal and amygdala volumes in posttraumatic stress disorder (PTSD), but findings have not always been consistent. Here, we present the results of a large-scale neuroimaging consortium study on PTSD conducted by the Psychiatric Genomics Consortium (PGC)–Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) PTSD Working Group. METHODS We analyzed neuroimaging and clinical data from 1868 subjects (794 PTSD patients) contributed by 16 cohorts, representing the largest neuroimaging study of PTSD to date. We assessed the volumes of eight subcortical structures (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, and lateral ventricle). We used a standardized image-analysis and quality-control pipeline established by the ENIGMA consortium. RESULTS In a meta-analysis of all samples, we found significantly smaller hippocampi in subjects with current PTSD compared with trauma-exposed control subjects (Cohen’s d = −0.17, p = .00054), and smaller amygdalae (d = −0.11, p = .025), although the amygdala finding did not survive a significance level that was Bonferroni corrected for multiple subcortical region comparisons (p < .0063). CONCLUSIONS Our study is not subject to the biases of meta-analyses of published data, and it represents an important milestone in an ongoing collaborative effort to examine the neurobiological underpinnings of PTSD and the brain’s response to trauma.

Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain’s anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 Tesla. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant’s co-registered anatomical scans. The degree of fiber connections between all pairs of cortical regions, or nodes, were found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70×70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.

Resting-state MRI (rs-fMRI) is a powerful procedure for studying whole brain neural connectivity. In this study we provide the first empirical evidence of the longitudinal reliability of rs-fMRI in children. We compared rest-retest measurements across spatial, temporal, and frequency domains for each of six cognitive and sensorimotor intrinsic connectivity networks (ICNs) both within and between scan sessions. Using Kendall's W, concordance of spatial maps ranged from .60 to .86 across networks, for various derived measures. The Pearson correlation coefficient for temporal coherence between networks across all Time one -Time two (T1/T2) z-converted measures was . 66 (p<.001). There were no differences between T1/T2 measurements in low-frequency power of the ICNs. For the visual network, within-session T1 correlated with the T2 low-frequency power, across participants. These measures from resting-state data in children were consistent across multiple domains (spatial, temporal, and frequency). Resting-state connectivity is therefore a reliable method for assessing large-scale brain networks in children.