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Tracy, J.

Publications and source records attributed to Tracy, J..

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Computational Support, Not Primacy, Distinguishes Compensatory Memory Reorganization in Epilepsy

Temporal lobe epilepsy (TLE) is associated with impairment in episodic memory. A substantial sub-group, however, is able to maintain adequate memory despite temporal lobe pathology. What has been missing from prior work in the cognitive reorganization is a direct comparison of TLE patients with intact/compensated status from those who are memory impaired and uncompensated. Little is known about the particular regional activations, functional connectivities (FCs), and/or network reconfigurations that implement changes in the primary computations or the support functions that drive adaptive plasticity and compensated memory. We utilized task fMRI on 54 unilateral TLE patients and 24 matched healthy controls (HC) during performance of a paired-associate memory (PAM) task to address three questions: 1) what regions implement PAM in TLE, and do such regions vary as a function of good versus poor performance, 2) are there unique FCs present during memory encoding that accounts for intact status through the preservation of primary memory computations or the supportive computations that allow for compensated memory responses, and 3) what features during memory encoding are most distinctive: is it the magnitude and location of regional activations, or the presence of enhanced functional connections to key structures such as the hippocampus? Results revealed a unique profile of non-ictal, non-dominant hemisphere regions (e.g., right posterior temporal regions) were most important to intact/compensated status in LTLE, involving both increased regional activity and increased modulatory communication with the hippocampi, all feature that was missing in impaired/uncompensated LTLE. The profile involved areas that are neither contralateral homologues to left hemisphere memory areas, nor regions traditionally considered computationally primary for episodic memory. None of these areas of increased activation or functional connectivity were associated with advantaged memory in HCs. Our emphasis on different performance levels yielded insight into two forms of cognitive reorganization. Computational primacy, where LTLE showed little change relative to HCs, and computational support where Intact/Compensated LTLE patients showed adaptive abnormalities. The analyses isolated the unique regional activations and mediating FCs that implement truly compensatory reorganization in LTLE. The results provided a new perspective on memory deficits by making clear that memory deficits arise not just from knockout of a functional hub, but from the failure to instantiate a complex set of reorganization responses. Such responses provided computational support to ensure successful memory. The findings demonstrated that by keeping track of performance levels, we can increase our understanding of adaptive brain responses and neuroplasticity in epilepsy.

neuroscience↗

White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions

Electrical brain stimulation is currently being investigated as a potential therapy for neurological disease. However, opportunities to optimize and personalize such therapies are challenged by the fact that the beneficial impact (and potential side effects) of focal stimulation on both neighboring and distant regions is not well understood. Here, we use network control theory to build a formal model of brain network function that makes explicit predictions about how stimulation spreads through the brains white matter network and influences large-scale dynamics. We test these predictions using combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data from patients with medically refractory epilepsy undergoing evaluation for resective surgery, and who volunteered to participate in an extensive stimulation regimen. We posit a specific model-based manner in which white matter tracts constrain stimulation, defining its capacity to drive the brain to new states, including states associated with successful memory encoding. In a first validation of our model, we find that the true pattern of white matter tracts can be used to more accurately predict the state transitions induced by direct electrical stimulation than the artificial patterns of a topological or spatial network null model. We then use a targeted optimal control framework to solve for the optimal energy required to drive the brain to a given state. We show that, intuitively, our model predicts larger energy requirements when starting from states that are farther away from a target memory state. We then suggest testable hypotheses about which structural properties will lead to efficient stimulation for improving memory based on energy requirements. We show that the strength and homogeneity of edges between controlled and uncontrolled nodes, as well as the persistent modal controllability of the stimulated region, predict energy requirements. Our work demonstrates that individual white matter architecture plays a vital role in guiding the dynamics of direct electrical stimulation, more generally offering empirical support for the utility of network control theoretic models of brain response to stimulation.

neuroscience↗