Neural function, injury, and stroke subtype predict treatment gains after stroke

• Published in: Annals of neurology Vol. 77; no. 1; pp. 132 - 145
• Main Authors: Burke Quinlan, Erin, Dodakian, Lucy, See, Jill, McKenzie, Alison, Le, Vu, Wojnowicz, Mike, Shahbaba, Babak, Cramer, Steven C.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.01.2015; Wiley Subscription Services, Inc
• Subjects: Adult; Aged; Brain Injuries - etiology; Cognition Disorders - etiology; Exercise Therapy - methods; Female; Hand Strength; Humans; Image Processing, Computer-Assisted; Longitudinal Studies; Magnetic Resonance Imaging; Male; Middle Aged; Mood Disorders - etiology; Predictive Value of Tests; Robotics; Statistics, Nonparametric; Stroke; Stroke - classification; Stroke - complications; Stroke Rehabilitation; Traumatic brain injury
• ISSN: 0364-5134; 1531-8249; 1531-8249
• DOI: 10.1002/ana.24309

Objective This study was undertaken to better understand the high variability in response seen when treating human subjects with restorative therapies poststroke. Preclinical studies suggest that neural function, neural injury, and clinical status each influence treatment gains; therefore, the current study hypothesized that a multivariate approach incorporating these 3 measures would have the greatest predictive value. Methods Patients 3 to 6 months poststroke underwent a battery of assessments before receiving 3 weeks of standardized upper extremity robotic therapy. Candidate predictors included measures of brain injury (including to gray and white matter), neural function (cortical function and cortical connectivity), and clinical status (demographics/medical history, cognitive/mood, and impairment). Results Among all 29 patients, predictors of treatment gains identified measures of brain injury (smaller corticospinal tract [CST] injury), cortical function (greater ipsilesional motor cortex [M1] activation), and cortical connectivity (greater interhemispheric M1–M1 connectivity). Multivariate modeling found that best prediction was achieved using both CST injury and M1–M1 connectivity (r2 = 0.44, p = 0.002), a result confirmed using Lasso regression. A threshold was defined whereby no subject with >63% CST injury achieved clinically significant gains. Results differed according to stroke subtype; gains in patients with lacunar stroke were best predicted by a measure of intrahemispheric connectivity. Interpretation Response to a restorative therapy after stroke is best predicted by a model that includes measures of both neural injury and function. Neuroimaging measures were the best predictors and may have an ascendant role in clinical decision making for poststroke rehabilitation, which remains largely reliant on behavioral assessments. Results differed across stroke subtypes, suggesting the utility of lesion‐specific strategies. ANN NEUROL 2015;77:132–145