TY - JOUR
T1 - Multimodal neural correlates of cognitive control in the Human Connectome Project
AU - Lerman-Sinkoff, Dov B.
AU - Sui, Jing
AU - Rachakonda, Srinivas
AU - Kandala, Sridhar
AU - Calhoun, Vince D.
AU - Barch, Deanna M.
N1 - Funding Information:
DLS was supported by NIH MSTP training grants 5T32GM007200-38 , 5T32GM007200-39 ; Interdisciplinary Training in Cognitive, Computational and Systems Neuroscience ( 5 T32 NS073547-05 ) and the McDonnell Center for Systems Neuroscience ; and NIH fellowship F30MH109294 . DMB was supported by the Human Connectome Project grant U54 MH091657 . SR and VDC were supported by NIH grants R01EB006841 & P20GM103472 and NSF grant 1539067 . JS was supported by the Chinese National Science Foundation grant No. 81471367 , the National High-Tech Development Plan (863 plan) No. 2015AA020513 and the Strategic Priority Research Program of the Chinese Academy of Sciences ( XDB02060005 ). Computations were performed using the facilities of the Washington University Center for High Performance Computing, which were partially funded by NIH grants 1S10RR022984-01A1 and 1S10OD018091-01 . Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The content of this report is solely the responsibility of the authors and does not necessarily represent the views of the funding agencies.
Publisher Copyright:
© 2017 Elsevier Inc.
PY - 2017/12
Y1 - 2017/12
N2 - 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.
AB - 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.
KW - Cognitive control
KW - Multimodal fusion
KW - mCCA + jICA
UR - http://www.scopus.com/inward/record.url?scp=85029472647&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029472647&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2017.08.081
DO - 10.1016/j.neuroimage.2017.08.081
M3 - Article
C2 - 28867339
AN - SCOPUS:85029472647
SN - 1053-8119
VL - 163
SP - 41
EP - 54
JO - NeuroImage
JF - NeuroImage
ER -