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Introduction: Emerging brain mapping methods measure function of individual brain circuits and have the potential to predict a patient’s gains and needs in the context of stroke rehabilitation. We recently described a motor-parietal circuit underlying visuomotor tracking and defined an EEG coherence measure (reflecting connectivity) that predicts visuomotor learning. Here we test the hypothesis that this EEG metric predicts visuomotor learning after stroke.Methods: After baseline dense-array resting EEG, patients with chronic hemiparetic stroke were provided with a home-based gaming system. During 9 half-hour training sessions, patients played games in which the stroke-affected arm tracked objects moving on the tabletop. Games were implemented using augmented reality, which we have found has advantages for motor training and in which virtual objects are projected into the real world and modified during game play.Results: Subjects (n=12) had affected arm Box&Blocks score of 15±12 and were 35±26 mo post-stroke. Visuomotor tracking improved significantly: on a standardized visuomotor test using the gaming system, scores increased from 60.5±11.5% to 74.0±3.2% (p=0.003). Gains were specific, as other behaviors were unchanged. Individual gains in visuomotor tracking score were predicted by the EEG connectivity metric from our prior study, coherence between leads overlying ipsilesional primary motor cortex (M1i) and ipsilesional lateral parietal region in the high beta (20-30 Hz) range, with higher connectivity predicting greater visuomotor tracking gains (r=0.61, p=0.037). This too was specific, as connectivity between M1i and other brain areas did not predict gains. Secondary analysis found that baseline visuomotor tracking scores correlated with several EEG connectivity measures, all inversely and all between M1i and contralesional regions.Conclusions: We found that (1) training that targets a specific brain circuit improves behavioral output of that circuit, and (2) an EEG measure of brain connectivity within that circuit predicts these behavioral gains--both with specificity. This approach may be useful for many neural circuits and their respective rehabilitation-related behaviors.