Computer-aided prediction of extent of motor recovery following constraint-induced movement therapy in chronic stroke

• Published in: Behavioural brain research Vol. 329; pp. 191 - 199
• Main Authors: George, Sarah Hulbert, Rafiei, Mohammad Hossein, Gauthier, Lynne, Borstad, Alexandra, Buford, John A., Adeli, Hojjat
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30-06-2017
• Subjects: Adult; Aged; Aged, 80 and over; Chronic Disease; Chronic stroke; Computational neuroscience; Constraint induced movement therapy; Diagnosis, Computer-Assisted - methods; Enhanced probabilistic neural networks; Female; Humans; Male; Middle Aged; Motion Therapy, Continuous Passive - methods; Motor Activity - physiology; Motor function; Recovery of Function - physiology; Somatosensory function; Stroke - physiopathology; Stroke Rehabilitation; Young Adult; Constraint induced movement therapy; Motor function; Enhanced probabilistic neural networks; Computational neuroscience; Somatosensory function; Chronic stroke
• ISSN: 0166-4328; 1872-7549
• DOI: 10.1016/j.bbr.2017.03.012

Constraint-induced movement therapy (CI therapy) is a well-researched intervention for treatment of upper limb function. Overall, CI therapy yields clinically meaningful improvements in speed of task completion and greatly increases use of the more affected upper extremity for daily activities. However, individual improvements vary widely. It has been suggested that intrinsic feedback from somatosensation may influence motor recovery from CI therapy. To test this hypothesis, an enhanced probabilistic neural network (EPNN) prognostic computational model was developed to identify which baseline characteristics predict extent of motor recovery, as measured by the Wolf Motor Function Test (WMFT). Individual characteristics examined were: proprioceptive function via the brief kinesthesia test, tactile sensation via the Semmes-Weinstein touch monofilaments, motor performance captured via the 15 timed items of the Wolf Motor Function Test, stroke affected side. A highly accurate predictive classification was achieved (100% accuracy of EPNN based on available data), but facets of motor functioning alone were sufficient to predict outcome. Somatosensation, as quantified here, did not play a large role in determining the effectiveness of CI therapy.