Intrasubject Reconfiguration of the Cortical Gradients' Topological Properties With Cognitive States.
5 affiliations
- 1Boys Town National Research Hospital, Institute for Human Neuroscience, Boys Town, Nebraska, USA.
- 2Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado, USA.
- 3Department of Psychology, University of Arizona, Tucson, Arizona, USA.
- 4Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, Nebraska, USA.
- 5Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, Nebraska, USA.
Abstract
Cortical gradients have been shown to be an effective technique to study the functional organization of the brain by capturing patterns of functional similarities. Until now, most studies have focused on analyzing cortical gradients derived from resting-state fMRI. With recent work highlighting the changes that functional brain networks undergo during various cognitive loads, there is a need to investigate whether cortical gradients are also sensitive to different cognitive demands, and to what degree. In this context, we collected a sample of 138 healthy individuals aged 12-25, each of whom completed a resting-state fMRI scan as well as three task fMRI scans (social prospection, spatial working memory, and verbal working memory). All data were analyzed to examine the task dependence of cortical gradients' topological properties. By employing the measures of network gradient dispersions as well as networks centroid distances, we revealed that alterations specific to each task occurred in topological properties of gradients involving both transmodal and unimodal networks, such as the executive control network varying between each task condition and the resting-state condition. This work highlights the promise of applying cortical gradients to task-based fMRI data, revealing the condition-sensitive nature of certain cortical gradients' topological properties.
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BibTeX
@article{pavelka2026intrasubject,
title = {{Intrasubject Reconfiguration of the Cortical Gradients' Topological Properties With Cognitive States.}},
author = {Derek J Pavelka and Attakias T Mertens and Katrina Myers and Callum Goldsmith and Joanna Arch and Jessica R Andrews-Hanna and Gaelle E Doucet},
journal = {Human brain mapping},
year = {2026},
month = nov,
doi = {10.1002/hbm.70686},
url = {https://europepmc.org/article/MED/42998005},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Intrasubject Reconfiguration of the Cortical Gradients' Topological Properties With Cognitive States.
AU - Derek J Pavelka
AU - Attakias T Mertens
AU - Katrina Myers
AU - Callum Goldsmith
AU - Joanna Arch
AU - Jessica R Andrews-Hanna
AU - Gaelle E Doucet
PY - 2026
DA - 2026/11/01
JO - Human brain mapping
DO - 10.1002/hbm.70686
UR - https://europepmc.org/article/MED/42998005
AB - Cortical gradients have been shown to be an effective technique to study the functional organization of the brain by capturing patterns of functional similarities. Until now, most studies have focused on analyzing cortical gradients derived from resting-state fMRI. With recent work highlighting the changes that functional brain networks undergo during various cognitive loads, there is a need to investigate whether cortical gradients are also sensitive to different cognitive demands, and to what degree. In this context, we collected a sample of 138 healthy individuals aged 12-25, each of whom completed a resting-state fMRI scan as well as three task fMRI scans (social prospection, spatial working memory, and verbal working memory). All data were analyzed to examine the task dependence of cortical gradients' topological properties. By employing the measures of network gradient dispersions as well as networks centroid distances, we revealed that alterations specific to each task occurred in topological properties of gradients involving both transmodal and unimodal networks, such as the executive control network varying between each task condition and the resting-state condition. This work highlights the promise of applying cortical gradients to task-based fMRI data, revealing the condition-sensitive nature of certain cortical gradients' topological properties.
SN - 1065-9471
SN - 1097-0193
ER -