| Autor: |
Shenghuan Sun, Justin Torok, Christopher Mezias, Daren Ma, Ashish Raj |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Cell Reports, Vol 42, Iss 10, Pp 113258- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2211-1247 |
| DOI: |
10.1016/j.celrep.2023.113258 |
| Popis: |
Summary: 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
