Multimodal neural correlates of cognitive control in the Human Connectome Project

&NA; 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. HighlightsIdentified independent components (ICs) that link across multiple types of imaging.Two ICs significantly correlated with cognitive control performance.Results were replicated using predictive and independent analyses in two cohorts.ICs included regions thought to be related to stable and dynamic cognitive control.

[1]  V. Calhoun,et al.  Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[2]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[3]  Michael C. Frank,et al.  Estimating the reproducibility of psychological science , 2015, Science.

[4]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[5]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[6]  V. Calhoun,et al.  The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.

[7]  Atle Bjørnerud,et al.  Error-related negativity is mediated by fractional anisotropy in the posterior cingulate gyrus--a study combining diffusion tensor imaging and electrophysiology in healthy adults. , 2009, Cerebral cortex.

[8]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[9]  Mark W. Woolrich,et al.  Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure , 2012, NeuroImage.

[10]  M. Baker 1,500 scientists lift the lid on reproducibility , 2016, Nature.

[11]  Aapo Hyvärinen,et al.  Validating the independent components of neuroimaging time series via clustering and visualization , 2004, NeuroImage.

[12]  Richard J Hodes,et al.  The NIH Toolbox , 2013, Neurology.

[13]  R. Gur,et al.  Development of Abbreviated Nine-Item Forms of the Raven’s Standard Progressive Matrices Test , 2012, Assessment.

[14]  Oluwasanmi Koyejo,et al.  Toward open sharing of task-based fMRI data: the OpenfMRI project , 2013, Front. Neuroinform..

[15]  N. Fox,et al.  NIH Toolbox for Assessment of Neurological and Behavioral Function , 2013, Neurology.

[16]  S. Lilienfeld,et al.  A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function. , 2000, Clinical psychology review.

[17]  Luke J. Chang,et al.  Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. , 2013, Cerebral cortex.

[18]  Vince D. Calhoun,et al.  Effective connectivity analysis of fMRI and MEG data collected under identical paradigms , 2011, Comput. Biol. Medicine.

[19]  V. Calhoun,et al.  An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders , 2015, Front. Genet..

[20]  Hannah R. Snyder Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: a meta-analysis and review. , 2013, Psychological bulletin.

[21]  N. Raz,et al.  Prefrontal cortex and executive functions in healthy adults: A meta-analysis of structural neuroimaging studies , 2014, Neuroscience & Biobehavioral Reviews.

[22]  Kalina Christoff,et al.  Localizing the rostrolateral prefrontal cortex at the individual level , 2007, NeuroImage.

[23]  Justin L. Vincent,et al.  Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.

[24]  B. Postle,et al.  The cognitive neuroscience of working memory. , 2007, Annual review of psychology.

[25]  Vince D. Calhoun,et al.  Canonical Correlation Analysis for Data Fusion and Group Inferences , 2010, IEEE Signal Processing Magazine.

[26]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[27]  Vince D. Calhoun,et al.  Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia , 2008, IEEE Journal of Selected Topics in Signal Processing.

[28]  Vince D. Calhoun,et al.  A review of multivariate methods for multimodal fusion of brain imaging data , 2012, Journal of Neuroscience Methods.

[29]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[30]  Kimberly L. Ray,et al.  Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions , 2012, Cognitive, affective & behavioral neuroscience.

[31]  V. Calhoun,et al.  In Search of Multimodal Neuroimaging Biomarkers of Cognitive Deficits in Schizophrenia , 2015, Biological Psychiatry.

[32]  Aapo Hyvärinen,et al.  Icasso: software for investigating the reliability of ICA estimates by clustering and visualization , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

[33]  Michael W. Cole,et al.  Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration , 2016, The Journal of Neuroscience.

[34]  Hao He,et al.  Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia , 2013, NeuroImage.

[35]  M. Minzenberg,et al.  Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. , 2009, Archives of general psychiatry.

[36]  Jen-Chuen Hsieh,et al.  Regional cortical thinning in patients with major depressive disorder: A surface-based morphometry study , 2012, Psychiatry Research: Neuroimaging.

[37]  Damien A. Fair,et al.  Defining functional areas in individual human brains using resting functional connectivity MRI , 2008, NeuroImage.

[38]  M. Botvinick,et al.  Motivation and cognitive control: from behavior to neural mechanism. , 2015, Annual review of psychology.

[39]  Michael W. Cole,et al.  Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence , 2012, The Journal of Neuroscience.

[40]  Vince D. Calhoun,et al.  Joint Blind Source Separation by Multiset Canonical Correlation Analysis , 2009, IEEE Transactions on Signal Processing.

[41]  Hao He,et al.  Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[42]  Mark W. Woolrich,et al.  Linked independent component analysis for multimodal data fusion , 2011, NeuroImage.

[43]  D. Barch,et al.  Introduction to the special issue on reliability and replication in cognitive and affective neuroscience research , 2013, Cognitive, affective & behavioral neuroscience.

[44]  P. Fox,et al.  Genetic control over the resting brain , 2010, Proceedings of the National Academy of Sciences.

[45]  Rex E. Jung,et al.  Does function follow form?: Methods to fuse structural and functional brain images show decreased linkage in schizophrenia , 2010, NeuroImage.

[46]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[47]  Vince D. Calhoun,et al.  A review of multivariate methods in brain imaging data fusion , 2010, Medical Imaging.

[48]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[49]  Timothy O. Laumann,et al.  Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project , 2016, Brain Connect..

[50]  Jessica A. Turner,et al.  COINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data , 2015, NeuroImage.

[51]  Vince D. Calhoun,et al.  Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model , 2011, NeuroImage.

[52]  Vince D. Calhoun,et al.  Chronnectomic patterns and neural flexibility underlie executive function , 2017, NeuroImage.

[53]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[54]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[55]  Irene E. Nagel,et al.  Cortical thickness is linked to executive functioning in adulthood and aging , 2012, Human brain mapping.

[56]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[57]  John G. Csernansky,et al.  Structure–function relationship of working memory activity with hippocampal and prefrontal cortex volumes , 2012, Brain Structure and Function.

[58]  Kristina M. Visscher,et al.  A Core System for the Implementation of Task Sets , 2006, Neuron.

[59]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

[60]  J. Pekar,et al.  Method for multimodal analysis of independent source differences in schizophrenia: Combining gray matter structural and auditory oddball functional data , 2006, Human brain mapping.

[61]  F. Meinecke,et al.  Analysis of Multimodal Neuroimaging Data , 2011, IEEE Reviews in Biomedical Engineering.

[62]  B. Pennington,et al.  Validity of the Executive Function Theory of Attention-Deficit/Hyperactivity Disorder: A Meta-Analytic Review , 2005, Biological Psychiatry.