New brain model suggests how regional chemistry shapes large-scale neural activity
Researchers have developed a computer model of the human cortex that links its microscopic chemistry to its brain-wide patterns of activity, providing new evidence that regional differences in receptor density help shape how activity and information move across the brain.
A new computer model of the human brain's cortex reveals how regional chemical differences shape overall brain activity, according to researchers at the Institute of Biomedical Investigations August Pi i Sunyer (IDIBAPS) in Spain. Published in the Proceedings of the National Academy of Sciences, the study uses The Virtual Brain (TVB) simulation platform to link microscopic receptor density to brain-wide patterns of activity.
Central to the research is the muscarinic acetylcholine receptor, a key target of the neuromodulator acetylcholine. By incorporating detailed maps of receptor density across 68 brain regions onto the brain's structural connections, the model demonstrates that this heterogeneity increases coordination between regions and enhances information flow compared to homogeneous models where all regions behave identically.
The researchers found that this molecular-level variation can meaningfully shape brain-wide activity, producing localized, sleep-like slow waves in some regions while the rest of the cortex remains in an awake-like state. This phenomenon, previously observed during attentional lapses, sleep deprivation, and around brain lesions, highlights how detailed molecular features can influence large-scale brain states.
Lead author Leonardo Dalla Porta notes that the study provides concrete evidence of how microscopic chemistry influences global brain function within a single computational framework. Co-author Maria V. Sanchez-Vives emphasizes that the findings offer insights into how neuromodulators like acetylcholine produce different network dynamics in various cortical regions, depending on receptor concentration.
The research suggests that future brain models could better capture the mechanisms behind state transitions in conditions such as brain lesions or disorders of consciousness by accounting for both structural connectivity and neuromodulator-induced molecular heterogeneity.
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