White matter disconnection of left multiple demand network is associated with post-lesion deficits in cognitive control
Cognitive control modulates other cognitive functions to achieve internal goals and is important for adaptive behavior. Cognitive control is enabled by the neural computations distributed over cortical and subcortical areas. However, due to technical challenges in recording neural activity from the...
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Published in | Nature communications Vol. 14; no. 1; pp. 1740 - 12 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
29.03.2023
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
ISSN | 2041-1723 2041-1723 |
DOI | 10.1038/s41467-023-37330-1 |
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Summary: | Cognitive control modulates other cognitive functions to achieve internal goals and is important for adaptive behavior. Cognitive control is enabled by the neural computations distributed over cortical and subcortical areas. However, due to technical challenges in recording neural activity from the white matter, little is known about the anatomy of white matter tracts that coordinate the distributed neural computations that support cognitive control. Here, we leverage a large sample of human patients with focal brain lesions (n = 643) and investigate how lesion location and connectivity profiles account for variance in cognitive control performance. We find that lesions in white matter connecting left frontoparietal regions of the multiple demand network reliably predict deficits in cognitive control performance. These findings advance our understanding of the white matter correlates of cognitive control and provide an approach for incorporating network disconnection to predict deficits following lesions.
The anatomy of white matter tracts which coordinate the computations of cognitive control are not well understood. Here, the authors show that lesions in white matter connecting left frontoparietal regions are associated with deficits in cognitive control performance. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-023-37330-1 |