ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2024
Development of Effective Connectome from Infancy to Adolescence.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 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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Abstract
Delineating the normative developmental profile of functional connectome is important for both standardized assessment of individual growth and early detection of diseases. However, functional connectome has been mostly studied using functional connectivity (FC), where undirected connectivity strengths are estimated from statistical correlation of resting-state functional MRI (rs-fMRI) signals. To address this limitation, we applied regression dynamic causal modeling (rDCM) to delineate the developmental trajectories of effective connectivity (EC), the directed causal influence among neuronal populations, in whole-brain networks from infancy to adolescence (0-22 years old) based on high-quality rs-fMRI data from Baby Connectome Project (BCP) and Human Connectome Project Development (HCP-D). Analysis with linear mixed model demonstrates significant age effect on the mean nodal EC which is best fit by a "U" shaped quadratic curve with minimal EC at around 2 years old. Further analysis indicates that five brain regions including the left and right cuneus, left precuneus, left supramarginal gyrus and right inferior temporal gyrus have the most significant age effect on nodal EC (
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