Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke

 Predicting motor recovery is TOTALLY FUCKING USELESS! Deliver motor recovery! I'd fire all of you.

Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke

Assia Jaillard

2017, NeuroImage: Clinical

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Firdaus Fabrice Hannanu a,b , Thomas A. Zeffiro b,c , Laurent Lamalle a,d,e,f , Olivier Heck g,h , Félix Renard i , Antoine Thuriot i,k , Alexandre Krainik a,d,e,f,g,h , Marc Hommel b,i,j , Olivier Detante b,h,k , Assia Jaillard a,b,i, ⁎, on behalf of the ISIS-HERMES Study Group K. Garambois 1 , M. Barbieux-Guillot 2 , I. Favre-Wiki 2 , S. Grand 3 , J.F. Le Bas 4 , A. Moisan 5 , M.J. Richard 6 , F. De Fraipont 6 , J. Gere 7 , S. Marcel 7 , W. Vadot 8 , G. Rodier 8 , D. Perennou 9 , A. Chrispin 9 , P. Davoine 9 , B. Naegele 2 , P. Antoine 2 , I. Tropres 10 , F. Renard 11

abstract  

 

article info Article history: Received 9 November 2016 Received in revised form 9 January 2017 Accepted 22 January 2017 Available online 26 January 2017 While motor recovery following mild stroke has been extensively studied with neuroimaging, mechanisms of recovery after moderate to severe strokes of the types that are often the focus for novel restorative therapies remain obscure. We used fMRI to: 1) characterize reorganization occurring after moderate to severe subacute stroke, 2) identify brain regions associated with motor recovery and 3) to test whether brain activity associated with pas- sive movement measured in the subacute period could predict motor outcome six months later. Because many patients with large strokes involving sensorimotor regions cannot engage in voluntary movement, we used passive flexion-extension of the paretic wrist to compare 21 patients with subacute ischemic stroke to 24 healthy controls one month after stroke. Clinical motor outcome was assessed with Fugl-Meyer motor scores (motor-FMS) six months later. Multiple regression, with predictors including baseline (one-month) motor-FMS and sensorimotor network regional activity (ROI) measures, was used to determine optimal variable selection for motor outcome prediction. Sensorimotor network ROIs were derived from a meta-analysis of arm voluntary movement tasks. Bootstrapping with 1000 replications was used for internal model validation. During passive movement, both control and patient groups exhibited activity increases in multiple bilateral sensorimotor network regions, including the primary motor (MI), premotor and supplementary motor areas (SMA).