Date of Award
Fall 4-2024
Embargo Period
4-4-2026
Document Type
Dissertation - MUSC Only
Degree Name
Doctor of Philosophy (PhD)
Department
Neuroscience
College
College of Graduate Studies
First Advisor
Nathan Rowland
Abstract
Restoring motor abilities after a stroke necessitates the involvement of numerous cognitive systems. To discover the mechanistic underpinnings related to successful recovery, we probed elements of motor and cognitive network synchrony in subjects with chronic stroke using transcranial direct current stimulation (tDCS) and fMRI. Following stimulation, Fugl-Meyer Assessments (FMA) were measured along with functional connectivity changes, which were most pronounced in supplementary motor area, inferior frontal gyrus, and temporo-occipital areas. Individual differences in stroke damage and electric field modeling were not related to motor effect of tDCS; instead graph features of the left dorsal attention network most strongly predicted FMA change, even in subjects who improved spontaneously with sham stimulation. Ultimately, attention network structure explained functional connectivity changes in motor, language and visual areas independent of neuromodulation in chronic stroke subjects. Our results demonstrate the fundamental role of network topology as part of a novel integrated approach to advancing stroke recovery.
Recommended Citation
Salazar, Claudia, "Attention Network Dynamics Predict Motor Improvement through Language and Visual Area Synchrony in Chronic Stroke" (2024). MUSC Theses and Dissertations. 853.
https://medica-musc.researchcommons.org/theses/853
Rights
Copyright is held by the author. All rights reserved.