Open AccessToward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain
Author(s) -
Sripada Chandra,
Angstadt Mike,
Rutherford Saige,
Taxali Aman,
Shedden Kerby
Publication year - 2020
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25007
Subject(s) - cognition , human connectome project , task (project management) , connectome , elementary cognitive task , correlation , psychology , neuroscience , neuroimaging , default mode network , cognitive psychology , working memory , effects of sleep deprivation on cognitive performance , functional connectivity , geometry , management , mathematics , economics
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.