Morgan L Ingemanson1, Justin R Rowe1, Vicky Chan1, Eric T Wolbrecht1, David J Reinkensmeyer1, Steven C Cramer2. 1. From the Departments of Anatomy and Neurobiology (M.L.I., D.J.R., S.C.C.), Biomedical Engineering (J.R.R., D.J.R.), Neurology (V.C. , S.C.C.), Mechanical and Aerospace Engineering (D.J.R.), and Physical Medicine and Rehabilitation (D.J.R. , S.C.C.), University of California at Irvine; and Department of Mechanical Engineering (E.T.W.), University of Idaho, Moscow. 2. From the Departments of Anatomy and Neurobiology (M.L.I., D.J.R., S.C.C.), Biomedical Engineering (J.R.R., D.J.R.), Neurology (V.C. , S.C.C.), Mechanical and Aerospace Engineering (D.J.R.), and Physical Medicine and Rehabilitation (D.J.R. , S.C.C.), University of California at Irvine; and Department of Mechanical Engineering (E.T.W.), University of Idaho, Moscow. scramer@uci.edu.
Abstract
OBJECTIVE: To test the hypothesis that, in the context of robotic therapy designed to enhance proprioceptive feedback via a Hebbian model, integrity of both somatosensory and motor systems would be important in understanding interparticipant differences in treatment-related motor gains. METHODS: In 30 patients with chronic stroke, behavioral performance, neural injury, and neural function were quantified for somatosensory and motor systems. Patients then received a 3-week robot-based therapy targeting finger movements with enhanced proprioceptive feedback. RESULTS: Hand function improved after treatment (Box and Blocks score increase of 2.8 blocks, p = 0.001) but with substantial variability: 9 patients showed improvement exceeding the minimal clinically important difference (6 blocks), while 8 patients (all of whom had >2-SD greater proprioception deficit compared to 25 healthy controls) showed no improvement. In terms of baseline behavioral assessments, a somatosensory measure (finger proprioception assessed robotically) best predicted treatment gains, outperforming all measures of motor behavior. When the neural basis underlying variability in treatment response was examined, somatosensory-related variables were again the strongest predictors. A multivariate model combining total sensory system injury and sensorimotor cortical connectivity (between ipsilesional primary motor and secondary somatosensory cortices) explained 56% of variance in treatment-induced hand functional gains (p = 0.002). CONCLUSIONS: Measures related to the somatosensory network best explained interparticipant differences in treatment-related hand function gains. These results underscore the importance of baseline somatosensory integrity for improving hand function after stroke and provide insights useful for individualizing rehabilitation therapy. CLINICALTRIALSGOV IDENTIFIER: NCT02048826.
OBJECTIVE: To test the hypothesis that, in the context of robotic therapy designed to enhance proprioceptive feedback via a Hebbian model, integrity of both somatosensory and motor systems would be important in understanding interparticipant differences in treatment-related motor gains. METHODS: In 30 patients with chronic stroke, behavioral performance, neural injury, and neural function were quantified for somatosensory and motor systems. Patients then received a 3-week robot-based therapy targeting finger movements with enhanced proprioceptive feedback. RESULTS: Hand function improved after treatment (Box and Blocks score increase of 2.8 blocks, p = 0.001) but with substantial variability: 9 patients showed improvement exceeding the minimal clinically important difference (6 blocks), while 8 patients (all of whom had >2-SD greater proprioception deficit compared to 25 healthy controls) showed no improvement. In terms of baseline behavioral assessments, a somatosensory measure (finger proprioception assessed robotically) best predicted treatment gains, outperforming all measures of motor behavior. When the neural basis underlying variability in treatment response was examined, somatosensory-related variables were again the strongest predictors. A multivariate model combining total sensory system injury and sensorimotor cortical connectivity (between ipsilesional primary motor and secondary somatosensory cortices) explained 56% of variance in treatment-induced hand functional gains (p = 0.002). CONCLUSIONS: Measures related to the somatosensory network best explained interparticipant differences in treatment-related hand function gains. These results underscore the importance of baseline somatosensory integrity for improving hand function after stroke and provide insights useful for individualizing rehabilitation therapy. CLINICALTRIALSGOV IDENTIFIER: NCT02048826.
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