Toward a "treadmill test" for cognition: Improved prediction of general cognitive ability from the task activated brain.
Adult
Aptitude
/ physiology
Cognition
/ physiology
Connectome
Default Mode Network
/ diagnostic imaging
Executive Function
/ physiology
Functional Neuroimaging
/ methods
Humans
Intelligence
/ physiology
Magnetic Resonance Imaging
Memory, Short-Term
/ physiology
Models, Theoretical
Nerve Net
/ diagnostic imaging
Neuropsychological Tests
Psychomotor Performance
/ physiology
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
15 08 2020
15 08 2020
Historique:
received:
14
12
2019
revised:
06
03
2020
accepted:
03
04
2020
pubmed:
5
5
2020
medline:
15
12
2021
entrez:
5
5
2020
Statut:
ppublish
Résumé
General cognitive ability (GCA) refers to a trait-like ability that contributes to performance across diverse cognitive tasks. Identifying brain-based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole-brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N-back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2-back versus 0-back contrast achieved a 0.50 correlation with GCA scores in 10-fold cross-validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation-a brain activation pattern associated with executive processing and higher cognitive demand-are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain-based prediction of GCA.
Identifiants
pubmed: 32364670
doi: 10.1002/hbm.25007
pmc: PMC7375130
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3186-3197Subventions
Organisme : NIMH NIH HHS
ID : R01 MH107741
Pays : United States
Organisme : Dana Foundation David Mahoney Neuroimaging Program
Informations de copyright
© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.
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