Multimodal neural correlates of cognitive control in the Human Connectome Project

Abstract

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.

Multimodal neuroimaging analysis reveals age‐associated common and discrete cognitive control constructs

“…Moreover, several parietal and frontal regions are consistently reported in both GM and ALFF modalities, particularly the precuneus and middle frontal gyrus. Previous studies suggest that age-related alterations of these parietalfrontal regions are linked to executive function and dysfunction (Kievit et al, 2014), which is consistent with our findings of an agerelated association with cognitive control constructs.…”

“…Furthermore, neuroimaging aging studies have probed this issue using only a single brain modality, such as anatomical brain images, functional images, or white-matter tensor-based images (e.g., Reineberg et al, 2015). Some studies incorporate multimodal brain measures but only focus on either the structure or function of the brain (e.g., Lerman-Sinkoff et al, 2017;Smolker et al, 2015). For example, Smolker et al (2015) evaluated multiple neuroanatomical measures, including brain voxels, surface thickness, and white-matter tensor-based measures of brain structures.…”

“…However, they did not use resting-state brain functional imaging, so their interpretation of the functional brain is limited. Lerman-Sinkoff et al (2017) employed a multimodal approach to identify neural correlates of cognitive control function, but their sample was mainly younger adults (22-35 years old), and their use of behavioral tasks was not able to assess the unity/diversity of the cognitive control function framework (Friedman & Miyake, 2017). Therefore, the aim of this study is to obtain multimodal brain neuroimaging data (i.e., gray matter, white matter, and functional data) and to examine neural correlates of cognitive control function based on the unity/diversity framework across the adult lifespan.…”

“…Several multivariate analysis methodologies have been developed to calculate brain network similarities between different imaging modalities, including joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA plus jICA (mCCA + jICA), and parallel ICA (para-ICA) (e.g. Alexander-Bloch, Raznahan, Bullmore, & Giedd, 2013;Calhoun, Adali, Hansen, Larsen, & Pekar, 2003;Calhoun, Adalı, & Pekar, 2004;Calhoun, Adali, Stevens, Kiehl, & Pekar, 2005;Clos, Rottschy, Laird, Fox, & Eickhoff, 2014;Di et al, 2017;Lerman-Sinkoff et al, 2017;Reid et al, 2017;Sui, Adali, Yu, Chen, & Calhoun, 2012;Sui et al, 2011Sui et al, , 2015. In this study, we employed the jICA approach developed by Calhourn (Calhoun et al, 2003;Calhoun, Adalı, & Pekar, 2004;Calhoun, Pekar, & Pearlson, 2004;Calhoun et al, 2005; see also Sui et al, 2011Sui et al, , 2012Sui et al, , 2015 to obtain common networks in relation to cognitive control function among three different imaging modalities: structural MRI (sMRI), resting-state functional MRI (rfMRI), and diffusion tensor imaging (DTI; dMRI).…”

Within and Between-person Correlates of the Temporal Dynamics of Resting EEG Microstates

“…Recent large-sample neuroimaging studies have begun to explore these questions in some detail (Barch et al, 2013). These studies have investigated the contribution of demographic characteristics (Kharabian Masouleh et al, 2019;Smith et al, 2015), personality (Dubois et al, 2018;Nostro et al, 2018), intelligence (Cox et al, 2019), and cognitive function (Lerman-Sinkoff et al, 2017) to patterns of structural and functional connectivity. Much of this work has examined average connectivity patterns of activity derived from whole neuroimaging sessionsso-called "static" functional brain connectivity.…”