Sun, Shenghuan, Torok, Justin, Mezias, Christopher, Ma, Daren, Raj, Ashish (October 2023) Spatial cell-type enrichment predicts mouse brain connectivity. Cell Reports, 42 (10). p. 113258. ISSN 2211-1247
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Abstract
A fundamental neuroscience topic is the link between the brain's molecular, cellular, and cytoarchitectonic properties and structural connectivity. Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the matrix inversion and subset selection algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell-type densities, demonstrating surprisingly strong prediction accuracy of cell types in general, and particular non-neuronal cells such as oligodendrocytes. We found evidence of a strong distance dependency in the cell connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia were salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.
Item Type: | Paper |
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Subjects: | bioinformatics bioinformatics > computational biology > algorithms organism description > animal organs, tissues, organelles, cell types and functions > organs types and functions > brain bioinformatics > computational biology organism description > animal > mammal organism description > animal > mammal > rodent > mouse organs, tissues, organelles, cell types and functions > organs types and functions organism description > animal > mammal > rodent |
CSHL Authors: | |
Communities: | CSHL labs > Mitra lab |
SWORD Depositor: | CSHL Elements |
Depositing User: | CSHL Elements |
Date: | 18 October 2023 |
Date Deposited: | 01 Nov 2023 16:05 |
Last Modified: | 11 Jan 2024 14:24 |
Related URLs: | |
URI: | https://repository.cshl.edu/id/eprint/41290 |
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