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Heritability and Cognitive Relevance of Structural Brain Controllability.

ABSTRACT: Cognition and behavior are thought to emerge from the connections and interactions among brain regions. The precise nature of these relationships remains elusive. Here we use tools provided by network control theory to determine how the structural connectivity profile of brain regions may shape individual variation in cognition. In a cohort of healthy young adults (n?=?1066), we computed two fundamental brain regional control patterns, average and modal controllability, which index the degree of influence of a region over others. We first established that regional brain controllability measures were both reproducible and heritable. Regions with controllability profiles theoretically conducive to facilitating multiple cognitive operations were over-represented in higher-order resting-state networks. Finally, variation in regional controllability accounted for about 50% of interindividual variability in multiple cognitive domains. We conclude that controllability is a biologically plausible property of the structural connectome and provides a mechanistic explanation for how brain structural architecture may influence cognitive functions.

SUBMITTER: Lee WH

PROVIDER: S-EPMC7197079 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Heritability and Cognitive Relevance of Structural Brain Controllability.

Lee Won Hee WH Rodrigue Amanda A Glahn David C DC Bassett Danielle S DS Frangou Sophia S

Cerebral cortex (New York, N.Y. : 1991) 20200501 5

Cognition and behavior are thought to emerge from the connections and interactions among brain regions. The precise nature of these relationships remains elusive. Here we use tools provided by network control theory to determine how the structural connectivity profile of brain regions may shape individual variation in cognition. In a cohort of healthy young adults (n = 1066), we computed two fundamental brain regional control patterns, average and modal controllability, which index the degree of ...[more]

PMID: 31838501

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Project description:ObjectivePredicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity. Network control theory is a powerful tool from the physical and engineering sciences that can provide insights regarding that relationship; it formalizes the study of how the dynamics of a complex system can arise from its underlying structure of interconnected units.ApproachGiven the recent use of network control theory in neuroscience, it is now timely to offer a practical guide to methodological considerations in the controllability of structural brain networks. Here we provide a systematic overview of the framework, examine the impact of modeling choices on frequently studied control metrics, and suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolution diffusion imaging with 730 diffusion directions acquired over approximately 1 h of scanning from ten healthy young adults.Main resultsFollowing a didactic introduction of the theory, we probe how a selection of modeling choices affects four common statistics: average controllability, modal controllability, minimum control energy, and optimal control energy. Next, we extend the current state-of-the-art in two ways: first, by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and second, by defining a complementary metric quantifying the complexity of the energy landscape of a system. We close with specific modeling recommendations and a discussion of methodological constraints.SignificanceOur hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.

Dataset Information

Heritability and Cognitive Relevance of Structural Brain Controllability.

Publications

Heritability and Cognitive Relevance of Structural Brain Controllability.

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