Parcellating an Individual Subject's Cortical and Subcortical Brain Structures Using Snowball Sampling of Resting-State Correlations

摘要: We describe methods for parcellating an individual subject's cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify centers areas, subdivisions nuclei, cerebellum. RSFC-Snowballing parcellation is then compared with derived identifying locations where maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Boundary Mapping largely complement one another, but also provide unique information; together, independent entities distinct across many in brain. relatively reliable within a subject scanned multiple days, while area boundaries appear considerable overlap subjects, there cross-subject variability—reinforcing motivation parcellate brains at level individuals. Finally, examination large meta-analysis task-evoked magnetic resonance imaging reveals that defined activity correspondence RSFC-Snowballing. This observation provides important evidence ability broad expanses individual's into functionally meaningful units.