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Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio-metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning "BrainAge" index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD- (N = 964), SMI-/CMD+ (N = 3,765), SMI-/CMD- (N = 8,083). SMI (F = 40.47, p = 2.06 × 10

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.

Cardiovascular risk factors such as dyslipidemia and hypertension increase the risk for white matter pathology and cognitive decline. We hypothesize that white matter levels of N-acetylaspartate (NAA), a chemical involved in the metabolic pathway for myelin lipid synthesis, could serve as a biomarker that tracks the influence of cardiovascular risk factors on white matter prior to emergence of clinical changes. To test this, we measured levels of NAA across white matter and gray matter in the brain using echo planar spectroscopic imaging (EPSI) in 163 individuals and examined the relationship of regional NAA levels and cardiovascular risk factors as indexed by the Framingham Cardiovascular Risk Score (FCVRS). NAA was strongly and negatively correlated with FCVRS across the brain, but, after accounting for age and sex, the association was found primarily in white matter regions, with additional effects found in the thalamus, hippocampus, and cingulate gyrus. FCVRS was also negatively correlated with creatine levels, again primarily in white matter. The results suggest that cardiovascular risks are related to neurochemistry with a predominantly white matter pattern and some subcortical and cortical gray matter involvement. NAA mapping of the brain may provide early surveillance for the potential subclinical impact of cardiovascular and metabolic risk factors on the brain.

Occupational exposure to hypobaria (low atmospheric pressure) is a risk factor for reduced white matter integrity, increased white matter hyperintensive burden, and decline in cognitive function. We tested the hypothesis that a discrete hypobaric exposure will have a transient impact on cerebral physiology. Cerebral blood flow, fractional anisotropy of water diffusion in cerebral white matter, white matter hyperintensity volume, and concentrations of neurochemicals were measured at baseline and 24 hr and 72 hr postexposure in N = 64 healthy aircrew undergoing standard US Air Force altitude chamber training and compared to N = 60 controls not exposed to hypobaria. We observed that hypobaric exposure led to a significant rise in white matter cerebral blood flow (CBF) 24 hr postexposure that remained elevated, albeit not significantly, at 72 hr. No significant changes were observed in structural measurements or gray matter CBF. Subjects with higher baseline concentrations of neurochemicals associated with neuroprotection and maintenance of normal white matter physiology (glutathione, N‐acetylaspartate, glutamate/glutamine) showed proportionally less white matter CBF changes. Our findings suggest that discrete hypobaric exposure may provide a model to study white matter injury associated with occupational hypobaric exposure.

Background We are developing the miniature pig (Sus scrofa domestica), an in-vivo translational, gyrencephalic model for brain development, as an alternative to laboratory rodents/non-human primates. We analyzed longitudinal changes in adolescent pigs using proton magnetic resonance spectroscopy (1H-MRS) and examined the relationship with white matter (WM) integrity derived from diffusion weighted imaging (DWI). New method Twelve female Sinclair™ pigs underwent three imaging/spectroscopy sessions every 23.95 ± 3.73 days beginning at three months of age using a clinical 3 T scanner. 1H-MRS data were collected using 1.2 × 1.0 × 3.0 cm voxels placed in left and right hemisphere WM using a Point Resolved Spectroscopy sequence (TR = 2000 ms, TE = 30 ms). Concentrations of N-acetylaspartate, myo-inositol (MI), glutamate + glutamine, choline, creatine, and macromolecules (MM) 09 and 14 were averaged from both hemispheres. DWI data were collected using 15 shells of b-values (b = 0–3500 s/mm2) with 32 directions/shell and fit using the WM Tract Integrity model to calculate fractional anisotropy (FA), kurtosis anisotropy (KA) and permeability-diffusivity index. Results MI and MM09 significantly declined with age. Increased FA and KA significantly correlated with decline in MI and MM09. Correlations lost significance once corrected for age. Comparison with existing methods MRI scanners/protocols can be used to collect 1H-MRS and DWI data in pigs. Pigs have a larger, more complex, gyrencephalic brain than laboratory rodents but are less complex than non-human primates, thus satisfying the “replacement” principle of animal research. Conclusions Longitudinal effects in MRS measurements were similar to those reported in adolescent humans. MRS changes correlated with diffusion measurements indicating ongoing WM myelination/maturation.

BACKGROUND Neuroscience research in brain development and disorders can benefit from an in vivo animal model that portrays normal white matter (WM) development trajectories and has a sufficiently large cerebrum for imaging with human MRI scanners and protocols. NEW METHOD Twelve three-month-old Sinclair™ miniature pigs (Sus scrofa domestica) were longitudinally evaluated during adolescent development using advanced diffusion weighted imaging (DWI) focused on cerebral WM. Animals had three MRI scans every 23.95 ± 3.73 days using a 3-T scanner. The DWI imaging protocol closely modeled advanced human structural protocols and consisted of fifteen b-shells (b = 0-3500 s/mm2) with 32-directions/shell. DWI data were analyzed using diffusion kurtosis and bi-exponential modeling that provided measurements that included fractional anisotropy (FA), radial kurtosis, kurtosis anisotropy (KA), axial kurtosis, tortuosity, and permeability-diffusivity index (PDI). RESULTS Significant longitudinal effects of brain development were observed for whole-brain average FA, KA, and PDI (all p

A novel mega-analytical approach that reduced methodological variance was evaluated using a multisite diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N = 192 (119 patients/73 controls) data sets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx, and Siemens Trio. DTI protocol included five b = 0 and 60 diffusion-sensitized gradient directions (b = 1,000 s/mm2 ). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the enhancing neuro imaging genetics meta-analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ∼3-4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values-from Cohen's d = .53 to .57-and improved the agreement between the regional pattern of FA differences observed in this study versus ENIGMA from r = .54 to .70. Application of MP-PCA-denoising further improved the agreement to r = .81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.

BACKGROUND: Abnormal cerebral levels of myo-inositol, a molecule involved in membrane phospholipid synthesis and regulation of intracellular processes, may be a biomarker for depression, including experience of depression within schizophrenia spectrum disorders. However, studies to date have resulted in mixed findings, in part due to limitations in comprehensive assessment of myo-inositol levels in different regions of the brain. METHODS: We aimed to determine the regional pattern of myo-inositol changes in cortical gray matter in depression by using echo-planar spectroscopic imaging (EPSI) at 3T in a sample of 193 individuals from the Amish and Mennonite communities, of whom 59 had a current or past history of unipolar depression. The relationship of myo-inositol levels to depression was examined from a categorical approach (using diagnosis of unipolar depressive disorder) as well as a dimensional approach (measuring symptoms of depression using the Maryland Trait and State Depression scales). Regression models covarying for age, sex, and accounting for the high correlation of brain metabolites between regions were used to examine these relationships, with false discovery rate used to control for multiple comparisons. RESULTS: Myo-inositol levels were lower in individuals with a history of depression compared to controls in 4 regions of the frontal and parietal cortex. Using a dimensional approach, myo-inositol levels were inversely correlated with trait depression (frequency of symptoms experienced over course of life) in 19 regions, primarily in frontal, limbic, and subcortical regions; however, myo-inositol was not strongly related to state depressive symptoms. Furthermore, lower myo-inositol was related to greater experience of stressful life events in 14 regions, largely overlapping with the regions associated with trait depression. DISCUSSION: These results indicate that decreased myo-inositol levels specifically in frontal regions may be a biomarker for depression. The finding of a relationship between stressful life events and myo-inositol levels may have implications for further study of the trans-diagnostic mechanisms of stress related psychopathology.