UCSF researchers Harry (Shenghuan) Sun, Justin Torok, Daren Ma, and Ashish Raj, PhD, with Christopher Mezias of Cold Spring Harbor Laboratory, published “Spatial cell-type enrichment predicts mouse brain connectivity” in Cell Reports. Co-first author Sun is a PhD candidate; co-first author Torok and Ma are specialists in Raj’s Brain Networks Laboratory at UCSF. The study found that machine learning methods can accurately predict whole-brain connectivity from neural cell types, and in particular oligodendrocytes, which produce and maintain myelin around neuronal axons. Using feature importance analysis, the researchers also identified key contributors to this connectivity prediction by cell types within the mouse brain.
How the structural connectome – the web of connections within the brain – is related to the distributions of neural cell types is an important but…
