ArticleImaging neuroscience (Cambridge, Mass.)2024
Edge-centric network control on the human brain structural network.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Network control theory models how gray matter regions transition between cognitive statesthrough associated white matter connections, where controllability quantifies the contributionof each region to driving these state transitions. Current applications predominantly adoptnode-centric views and overlook the potential contribution of brain network connections. Tobridge this gap, we use edge-centric network control theory (E-NCT) to assess the role of brainconnectivity (i.e., edges) in governing brain dynamic processes. We applied this framework todiffusion MRI data from individuals in the Human Connectome Project. We first validate edgecontrollability through comparisons against null models, node controllability, and structuraland functional connectomes. Notably, edge controllability predicted individual differences inphenotypic information. Using E-NCT, we estimate the brain's energy consumption foractivating specific networks. Our results reveal that the activation of a complex, whole-brainnetwork predicting executive function (EF) is more energy efficient than the correspondingcanonical network pairs. Overall, E-NCT provides an edge-centric perspective on thebrain's network control mechanism. It captures control energy patterns andbrain-behavior phenotypes with a more comprehensive understanding of brain dynamics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.