Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk

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.


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Policy information about availability of computer code Data collection Data analysis Avram J. Holmes Mar 4, 2020 All data analyzed were obtained from publicly available resources (e.g. Allen Human Brain Atlas, UK Biobank).

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All studies must disclose on these points even when the disclosure is negative. Our analyses utilized open-access genetic and neuroimaging consortia data, so no sample size calculations were performed. Rather, we sought to maximize sample size in all cases, removing samples or subjects with questionable data quality.
A total of 13,236 UKB subjects were available and able to be processed through the imaging pipeline. Subjects with mean run-wise frame-toframe head motion greater than 0.20 mm, and inverted resting-state SNR greater than 3 standard deviations above the mean were removed. After filtering for white British subjects with usable genetic data, cryptic relatedness <0.025, and conducting row-wise deletion for the variables age, sex, height, weight, BMI, three head position coordinates (X,Y,Z), combined gray/white matter volume, combined ventricular/ CSF volume, diastolic and systolic blood pressure, run-wise resting state motion, resting state inverse SNR, T1 inverse SNR, and UK Biobank assessment center, 9,713 subjects remained for analyses (percent female=54.33, mean age= 63.67 SD= 7.45, min/max age=45-80). We included the anthropometric measures of height, BMI, weight, and blood pressure given previously demonstrated associations with imaging phenotypes in the UK Biobank.
1) UKBiobank subjects were thresholded based upon SNR (T1 and resting-state) and head motion thresholds, and row-wise deletion was conducted on all analyzed data fields. Last, only genetically unrelated white/non-latino subjects were retained for the final sample (n=9,713). These criteria were not pre-determined, but reflect field standard preprocessing steps.
2) Two macaques in the NIH Blueprint were not excluded due to sparse sampling across the 11 analyzed brain regions (1 primate was sampled in just temporal lobe; 1 primate was only sampled within OFC, DLPFC, and ACC). This exclusion criteria was determined after examining sample counts for each donor in the NIH Blueprint database.
1) We replicated the SST/PVALB negative cortical correlation observed in AHBA data within macaque NIH Blueprint and human Brainspan data.