Format

Send to

Choose Destination
Neuroimage. 2019 Sep 28:116233. doi: 10.1016/j.neuroimage.2019.116233. [Epub ahead of print]

Individualized functional networks reconfigure with cognitive state.

Author information

1
Department of Electrical Engineering, Yale University, New Haven, CT, 06511, USA; Yale Institute for Network Science (YINS), Yale University, New Haven, CT, 06511, USA. Electronic address: mehraveh.salehi@yale.edu.
2
Department of Electrical Engineering, Yale University, New Haven, CT, 06511, USA; Yale Institute for Network Science (YINS), Yale University, New Haven, CT, 06511, USA.
3
Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06520, USA.
4
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA.
5
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA; Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06520, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06520, USA.

Abstract

There is extensive evidence that functional organization of the human brain varies dynamically as the brain switches between task demands, or cognitive states. This functional organization also varies across subjects, even when engaged in similar tasks. To date, the functional network organization of the brain has been considered static. In this work, we use fMRI data obtained across multiple cognitive states (task-evoked and rest conditions) and across multiple subjects, to measure state- and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but that many nodes change their network membership as a function of cognitive state. Such reconfigurations are highly robust and reliable to the extent that they can be used to predict cognitive state with up to 97% accuracy. Our findings suggest that if functional networks are to be defined via functional clustering of nodes, then it is essential to consider that such definitions may be fluid and cognitive-state dependent.

KEYWORDS:

Connectivity; Functional; Functional parcellation; Individual variability; Task variability; fMRI

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center