This study examined the relationship of γ-aminobutyric acid (GABA) and glutamate levels from the anterior and posterior cingulates (AC and PC) with cerebral blood flow (CBF) at rest. (1)H magnetic resonance spectroscopy measurements in the AC and PC and pseudo-continuous arterial spin labeling data were acquired from 10 healthy controls. GABA levels from the AC were strongly inversely correlated with global (whole-brain) CBF (r=-0.91, p=0.0015). GABA levels from the PC and glutamate levels from both regions were not significantly correlated with CBF. We hypothesize that GABA-mediated inhibition of AC activation of the locus coeruleus-norepinephrine pathway may influence global CBF.

Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.

Reduced speed of cerebral information processing is a cognitive deficit associated with schizophrenia. Normal information processing speed (PS) requires intact white matter (WM) physiology to support information transfer. In a cohort of 107 subjects (47/60 patients/controls), we demonstrate that PS deficits in schizophrenia patients are explained by reduced WM integrity, which is measured using diffusion tensor imaging, mediated by the mismatch in WM/gray matter blood perfusion, and measured using arterial spin labeling. Our findings are specific to PS, and testing this hypothesis for patient-control differences in working memory produces no explanation. We demonstrate that PS deficits in schizophrenia can be explained by neurophysiological alterations in cerebral WM. Whether the disproportionately low WM integrity in schizophrenia is due to illness or secondary due to this disorder deserves further examination.

New treatment development for psychiatric disorders depends critically upon the development of physiological measures that can accurately translate between preclinical animal models and clinical human studies. Such measures can be used both as stratification biomarkers to define pathophysiologically homogeneous patient populations and as target engagement biomarkers to verify similarity of effects across preclinical and clinical intervention. Traditional "time-domain" event-related potentials (ERP) have been used translationally to date but are limited by the significant differences in timing and distribution across rodent, monkey and human studies. By contrast, neuro-oscillatory responses, analyzed within the "time-frequency" domain, are relatively preserved across species permitting more precise translational comparisons. Moreover, neuro-oscillatory responses are increasingly being mapped to local circuit mechanisms and may be useful for investigating effects of both pharmacological and neuromodulatory interventions on excitatory/inhibitory balance. The present paper provides a roadmap for development of neuro-oscillatory responses as translational biomarkers in neuropsychiatric treatment development.

Patients with schizophrenia have patterns of brain deficits including reduced cortical thickness, subcortical gray matter volumes, and cerebral white matter integrity. We proposed the regional vulnerability index (RVI) to translate the results of Enhancing Neuro Imaging Genetics Meta-Analysis studies to the individual level. We calculated RVIs for cortical, subcortical, and white matter measurements and a multimodality RVI. We evaluated RVI as a measure sensitive to schizophrenia-specific neuroanatomical deficits and symptoms and studied the timeline of deficit formations in: early (≤5 years since diagnosis, N = 45, age = 28.8 ± 8.5); intermediate (6-20 years, N = 30, age 43.3 ± 8.6); and chronic (21+ years, N = 44, age = 52.5 ± 5.2) patients and healthy controls (N = 76, age = 38.6 ± 12.4). All RVIs were significantly elevated in patients compared to controls, with the multimodal RVI showing the largest effect size, followed by cortical, white matter and subcortical RVIs (d = 1.57, 1.23, 1.09, and 0.61, all p < 10^-6 ). Multimodal RVI was significantly correlated with multiple cognitive variables including measures of visual learning, working memory and the total score of the MATRICS consensus cognitive battery, and with negative symptoms. The multimodality and white matter RVIs were significantly elevated in the intermediate and chronic versus early diagnosis group, consistent with ongoing progression. Cortical RVI was stable in the three disease-duration groups, suggesting neurodevelopmental origins of cortical deficits. In summary, neuroanatomical deficits in schizophrenia affect the entire brain; the heterochronicity of their appearance indicates both the neurodevelopmental and progressive nature of this illness. These deficit patterns may be useful for early diagnosis and as quantitative targets for more effective treatment strategies aiming to alter these neuroanatomical deficit patterns.

Regional homogeneity (ReHo) is a measure of local functional brain connectivity that has been reported to be altered in a wide range of neuropsychiatric disorders. Computed from brain resting-state functional MRI time series, ReHo is also sensitive to fluctuations in cerebral blood flow (CBF) that in turn may be influenced by cerebrovascular health. We accessed cerebrovascular health with Framingham cardiovascular risk score (FCVRS). We hypothesize that ReHo signal may be influenced by regional CBF; and that these associations can be summarized as FCVRS→CBF→ReHo. We used three independent samples to test this hypothesis. A test-retest sample of N = 30 healthy volunteers was used for test-retest evaluation of CBF effects on ReHo. Amish Connectome Project (ACP) sample (N = 204, healthy individuals) was used to evaluate association between FCVRS and ReHo and testing if the association diminishes given CBF. The UKBB sample (N = 6,285, healthy participants) was used to replicate the effects of FCVRS on ReHo. We observed strong CBF→ReHo links (p<2.5 × 10−3) using a three-point longitudinal sample. In ACP sample, marginal and partial correlations analyses demonstrated that both CBF and FCVRS were significantly correlated with the whole-brain average (p<10−6) and regional ReHo values, with the strongest correlations observed in frontal, parietal, and temporal areas. Yet, the association between ReHo and FCVRS became insignificant once the effect of CBF was accounted for. In contrast, CBF→ReHo remained significantly linked after adjusting for FCVRS and demographic covariates (p<10−6). Analysis in N = 6,285 replicated the FCVRS→ReHo effect (p = 2.7 × 10−27). In summary, ReHo alterations in health and neuropsychiatric illnesses may be partially driven by region-specific variability in CBF, which is, in turn, influenced by cardiovascular factors.

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.