Characterizing brain function changes during neuroplasticity within distributed neural systems

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Neuroimaging studies of human brain function are increasingly representing brain activity using the concept of functional connectomes (i.e., a neural network dedicated to a specific function). These distributed functional networks emerge from the brain's extensive underlying structural networks and can be dynamically modified through biological processes that underlie the concept of neuroplasticity (i.e., the brain's innate ability to reorganize neural networks). Since 2004, my NSERC-funded neuroimaging research has focused on characterizing brain activity across distributed neural systems during sensory, perceptual, motor, and cognitive processing - with the goal to improve the ability to non-invasively image brain function and translate this knowledge into advanced evaluation of brain function/dysfunction. Notably, this program has expanded the reach of functional neuroimaging from specialized processing regions in gray matter (GM: the nodes within a network) to functionally active connections in white matter (WM: the connections between nodes). It has tackled the historically controversial challenge of detecting WM activation using functional magnetic resonance imaging (fMRI). As WM connections comprise almost 50% of total neural tissue, the work addressed a major historical blindspot. At the network level, direct measures of active WM connections provide critical information. In parallel, we have begun longitudinal monitoring studies to characterize brain activity changes over time. This work focuses on spatial and temporal changes as a measure of neuroplasticity using fMRI and magneto-/electro- encephalography (M/EEG). These two central research lines have recently been integrated into unified experiments that combine distributed functional networks with longitudinal monitoring of neuroplasticity - the central focus of this application. The current NSERC application will develop deeper investigations into the relationship between functional connectomes and neuroplasticity. The research will focus on functional connectomes involved in motor learning using task-dependent and resting state analyses. My team will examine activation changes over time using a longitudinal study of behavioural motor performance improvements. Common patterns of network level change will be identified first in WM and then in GM. The central hypothesis predicts that common underlying mechanisms govern dynamic network changes consistently between task-based and resting state evaluations. Specifically, based on preliminary results, we predict that neuroplasticity in WM leads to reduced variability in hemodynamic responses - independent of a task-based or resting state functional connectome analysis. Exploratory analyses will also extend this approach to M/EEG to examine correlates in functional connectivity. Knowledge of the underlying patterns for dynamic change can in turn be utilized to improve the evaluation of brain function across various applications.