Journal
NEUROIMAGE
Volume 163, Issue -, Pages 41-54Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2017.08.081
Keywords
Cognitive control; mCCA plus jICA; Multimodal fusion
Funding
- NIH MSTP training grants [5T32GM007200-38, 5T32GM007200-39]
- Interdisciplinary Training in Cognitive, Computational and Systems Neuroscience [5 T32 NS073547-05]
- McDonnell Center for Systems Neuroscience
- NIH fellowship [F30MH109294]
- Human Connectome Project grant [U54 MH091657]
- NIH [R01EB006841, P20GM103472, 1S10RR022984-01A1, 1S10OD018091-01]
- NSF [1539067]
- Chinese National Science Foundation [81471367]
- National High-Tech Development Plan (863 plan) [2015AA020513]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB02060005]
- 16 NIH Institutes and Centers [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
- Office of Integrative Activities
- Office Of The Director [1539067] Funding Source: National Science Foundation
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Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.
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