Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk.
Kevin M AndersonMeghan A CollinsRowena ChinTian GeMonica D RosenbergAvram J HomesPublished in: Nature communications (2020)
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.
Keyphrases
- resting state
- genome wide
- functional connectivity
- bipolar disorder
- white matter
- endothelial cells
- magnetic resonance imaging
- copy number
- gene expression
- single cell
- stem cells
- poor prognosis
- high resolution
- computed tomography
- contrast enhanced
- healthcare
- transcription factor
- mental health
- magnetic resonance
- oxidative stress
- photodynamic therapy
- long non coding rna
- blood brain barrier
- mass spectrometry
- big data
- brain injury
- induced pluripotent stem cells
- diffusion weighted imaging
- binding protein
- bioinformatics analysis