Determining whether a person with stroke has reached their full potential for recovery is difficult. While techniques such as transcranial magnetic stimulation (TMS) and MRI have some prognostic value, their role in rehabilitation is undefined. This study used TMS and MRI to determine which factors predict functional potential, defined as an individual's capacity for further functional improvement at least 6 months following stroke. We studied 21 chronic stroke patients with upper limb impairment. The functional integrity of the corticospinal tracts (CSTs) was assessed using TMS and functional MRI. The presence or absence of motor-evoked responses (MEPs) to TMS in the affected upper limb, and the lateralization of cortical activity during affected hand use were determined. The structural integrity of the CST was assessed using MRI, and diffusion tensor imaging was used to measure the asymmetry in fractional anisotropy (FA) of the internal capsules. A multiple linear regression analysis was performed, to predict both clinical score at inception and change in clinical score for 17 patients who completed a 30 day programme of motor practice with the affected upper limb. The main findings were that in patients with MEPs, meaningful gains were still possible 3 years after stroke, although the capacity for improvement declined with time. In patients without MEPs, functional potential declines with increasing CST disruption, with no meaningful gains possible if FA asymmetry exceeds a value of 0.25. This study is the first to demonstrate the complementary nature of TMS and MRI techniques in predicting functional potential in chronic stroke patients. An algorithm is proposed for the selection of individualized rehabilitation strategies, based on the prediction of functional potential. These strategies could include neuromodulation using a range of emerging techniques, to prime the motor system for a plastic response to rehabilitation.

Stroke is a leading cause of adult disability and the recovery of motor function is important for independence in activities of daily living. Predicting motor recovery after stroke in individual patients is difficult. Accurate prognosis would enable realistic rehabilitation goal-setting and more efficient allocation of resources. The aim of this study was to test and refine an algorithm for predicting the potential for recovery of upper limb function after stroke. Forty participants were prospectively enrolled within 3 days of ischaemic stroke. First, shoulder abduction and finger extension strength were graded 72 h after stroke onset to compute a shoulder abduction and finger extension score. Secondly, transcranial magnetic stimulation was used to assess the functional integrity of descending motor pathways to the affected upper limb. Third, diffusion-weighted magnetic resonance imaging was used to assess the structural integrity of the posterior limbs of the internal capsules. Finally, these measures were combined in the PREP algorithm for predicting an individual's potential for upper limb recovery at 12 weeks, measured with the Action Research Arm Test. A cluster analysis was used to independently group patients according to Action Research Arm Test score at 12 weeks, for comparison with predictions from the PREP algorithm. There was excellent correspondence between the cluster analysis of Action Research Arm Test score at 12 weeks and predictions made with the PREP algorithm. The algorithm had positive predictive power of 88%, negative predictive power of 83%, specificity of 88% and sensitivity of 73%. This study provides preliminary data in support of the PREP algorithm for the prognosis of upper limb recovery in individual patients. PREP may enable tailored planning of rehabilitation and more accurate stratification of patients in clinical trials.

These findings indicate that upper limb impairment resolves by 70% of the maximum possible, regardless of initial impairment, but only for patients with intact corticomotor function. Impairment resolution seems to reflect spontaneous neurobiological processes that involve the ipsilesional corticomotor pathway. A better understanding of these mechanisms could lead to interventions that increase resolution of impairment above 70%.

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ObjectiveRecovery of motor function is important for regaining independence after stroke, but difficult to predict for individual patients. Our aim was to develop an efficient, accurate, and accessible algorithm for use in clinical settings. Clinical, neurophysiological, and neuroimaging biomarkers of corticospinal integrity obtained within days of stroke were combined to predict likely upper limb motor outcomes 3 months after stroke.MethodsData from 207 patients recruited within 3 days of stroke [103 females (50%), median age 72 (range 18–98) years] were included in a Classification and Regression Tree analysis to predict upper limb function 3 months poststroke.ResultsThe analysis produced an algorithm that sequentially combined a measure of upper limb impairment; age; the presence or absence of upper limb motor evoked potentials elicited with transcranial magnetic stimulation; and stroke lesion load obtained from MRI or stroke severity assessed with the NIHSS score. The algorithm makes correct predictions for 75% of patients. A key biomarker obtained with transcranial magnetic stimulation is required for one third of patients. This biomarker combined with NIHSS score can be used in place of more costly magnetic resonance imaging, with no loss of prediction accuracy.InterpretationThe new algorithm is more accurate, efficient, and accessible than its predecessors, which may support its use in clinical practice. While further work is needed to potentially incorporate sensory and cognitive factors, the algorithm can be used within days of stroke to provide accurate predictions of upper limb functional outcomes at 3 months after stroke. www.presto.auckland.ac.nz