There is no single functional atlas even for a single individual: Functional parcel definitions change with task
The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and cr...
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Published in | NeuroImage (Orlando, Fla.) Vol. 208; p. 116366 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
01.03.2020
Elsevier Limited Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state.
•Functional parcel boundaries reconfigure with cognitive state.•Parcel reconfigurations are robust and reliable across sessions and subjects.•Parcel sizes can significantly predict task condition and task performance.•State-dependent functional atlases have implications for functional connectivity. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 1053-8119 1095-9572 1095-9572 |
DOI: | 10.1016/j.neuroimage.2019.116366 |