Inhibitory interneurons orchestrate information flow across the cortex and are implicated in psychiatric illness. Although interneuron classes have unique functional properties and spatial distributions, the influence of interneuron subtypes on brain function, cortical specialization, and illness risk remains elusive. Here, we demonstrate stereotyped negative correlation of somatostatin and parvalbumin transcripts within human and non-human primates. Cortical distributions of somatostatin and parvalbumin cell gene markers are strongly coupled to regional differences in functional MRI variability. In the general population (n = 9,713), parvalbumin-linked genes account for an enriched proportion of heritable variance in in-vivo functional MRI signal amplitude. Single-marker and polygenic cell deconvolution establish that this relationship is spatially dependent, following the topography of parvalbumin expression in post-mortem brain tissue. Finally, schizophrenia genetic risk is enriched among interneuron-linked genes and predicts cortical signal amplitude in parvalbumin-biased regions. These data indicate that the molecular-genetic basis of brain function is shaped by interneuron-related transcripts and may capture individual differences in schizophrenia risk.

The results indicated that anosognosia is common in both AD and MCI patients and associated with cognitive dysfunction and apathy in AD. The findings of this study warrant further research to delineate the mechanisms of anosognosia as it poses a challenge to treatment outcomes.

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Within a transcultural setting, the p-AD8 demonstrated good discriminative validity and can be used to gain a preliminary understanding of an individual's cognitive status.

Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging (MRI) data. However, these traditional techniques lack sensitivity and predictive value at the individual level. Machine-learning approaches have emerged as potential diagnostic and prognostic tools. We used an anatomically and spatially regularized support vector machine (SVM) framework to categorize schizophrenia and healthy individuals based on whole-brain gray matter densities estimated using voxel-based morphometry from structural MRI scans. The regularized SVM model yielded recognition accuracy of 86.6% in the training set of 127 individuals and validation accuracy of 83.5% in an independent set of 85 individuals. A sequential region-of-interest (ROI) selection step was adopted for feature selection, improving recognition accuracy to 92.0% in the training set and 89.4% in the validation set. The combined model achieved 96.6% sensitivity and 74.1% specificity. Seven ROIs were identified as the optimal discriminatory subset: the occipital fusiform gyrus, middle frontal gyrus, pars opercularis of the inferior frontal gyrus, anterior superior temporal gyrus, superior frontal gyrus, left thalamus and left lateral ventricle. These findings demonstrate the utility of spatial and anatomical priors in SVM for neuroimaging analyses in conjunction with sequential ROI selection in the recognition of schizophrenia.