In order to understand how the human brain is organized to process and transform information it has become increasingly recognized that a network-level description of its constituent processing elements is needed. To this end, structural and diffusion-based MRI have begun to explain which parts of the human brain are physically connected to each other, and resting state functional connectivity MRI (rs-fcMRI) has revealed large-scale long-term BOLD correlation relationships within that structure. This latter description has typically assumed stability in these relationships over long periods of time, but our understanding of the brain as a complex dynamic system suggests that members of the brain network change their interactions with each other moment-to-moment and in the context of different processing demands. This proposal aims to harness the unique combination of spatial and temporal resolution provided by functional MRI to rigorously demonstrate the dynamic properties of the brain network both at rest and during tasks. The proposal has two aims.
The first aim i s to characterize dynamic fluctuations in connectivity relationships in the unconstraine setting of rest. We plan to rigorously determine whether and where highly variable relationships exist, how these dynamics relate to the known modular network structure, and whether patterns of dynamics exist between known functional communities. We have preliminary evidence that meaningful dynamics exist, but wish to comprehensively describe the spatial organization and temporal properties of these dynamics, to give insight into how they relate to healthy brain function and to provide a reference for identifying altered dynamics in disease.
The second aim i s designed to illuminate the potential biological significance of dynamics in correlation by manipulating them in a well-controlled task setting. Specifically, we wish to test if changing external task demands induces transient changes in network coordination in regions of the brain believed to be involved in orchestrating effective task processing. This would provide strong evidence for an observable role of distributed network coordination in processing information in addition to localized BOLD activity, and would suggest an alternative framework for interpreting processing failures in pathological brains. In funding the Human Connectome Project, the NIMH recognized the importance of an accurate and detailed description of structural and functional connectivity in the healthy human brain as a critical step in understanding disorders of mental health. Description and analysis of dynamics in this connectivity is a natural extension of this mandate and will provide a more sensitive space in which to understand brain function, dysfunction, and changes over development and aging.

Public Health Relevance

The brain is a network, but static measurements of its organization have only begun to tell us how it may function at rest or when performing a task. This project aims to describe the dynamic properties underlying the brain's network connectivity and to identify the brain's capacity for dynamic network coordination. If successful, it will pointto new concepts for understanding brain function in healthy individuals and suggest new approaches for identifying brain dysfunction in disease.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
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Special Emphasis Panel (ZRG1)
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Rosemond, Erica K
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Washington University
Schools of Medicine
Saint Louis
United States
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Gratton, Caterina; Laumann, Timothy O; Nielsen, Ashley N et al. (2018) Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation. Neuron 98:439-452.e5
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Greene, Deanna J; Laumann, Timothy O; Dubis, Joseph W et al. (2014) Developmental changes in the organization of functional connections between the basal ganglia and cerebral cortex. J Neurosci 34:5842-54
Wig, Gagan S; Laumann, Timothy O; Petersen, Steven E (2014) An approach for parcellating human cortical areas using resting-state correlations. Neuroimage 93 Pt 2:276-91

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