Utility of Transcranial Magnetic Stimulation and Diffusion Tensor maging for Prediction of Upper-Limb Motor Recovery n Acute Ischemic Stroke Patients

Background: The recovery of the upper-limb (UL) motor function after ischemic stroke (IS) remains a major scientific, clinical, and patient concern and it is hard to predict alone from the clinical symptoms. Objective: To determine the accuracy of the prediction of the recovery of UL motor function in patients with acute ischemic middle cerebral artery (MCA) stroke using individual clinical, transcranial magnetic stimulation (TMS) or diffusion tensor imaging (DTI) parameters or their combination. Methods and Material: The first-ever acute ischemic MCA stroke patients within 7 days of the stroke onset who had an obvious UL motor deficit underwent TMS for the presence of motor-evoked potential (MEP) and DTI to evaluate the integrity of corticospinal tracts. Multivariate logistic regression analysis was done to test for the accuracy of the prediction of the recovery of UL motor function. Results: Twenty-nine acute ischemic MCA stroke patients (21 males and 8 females) with a mean age of 51.45 +/- 14.26 years were recruited. Model-I included clinical scales (Fugl-Meyer Assessment [FMA] + Motricity Index [MI]) + TMS (MEP) + DTI (fractional anisotropy [FA]) were found to be the most accurate predictive model, with the overall predictive ability (93.3%; 95% confidence interval [CI]: 0.87-0.99) and sensitivity: 94.9% (95% CI: 0.87-1.0) and specificity: 95.8% (95% CI: 0.89-1.0); respectively. Conclusion: The accuracy of UL motor recovery can be predicted through the clinical battery and their elements as well as TMS (MEP) and DTI (FA) parameters. Further, well-designed prospective studies are needed to confirm our findings.

Automated summary

This study aimed to determine the accuracy of predicting the recovery of upper-limb motor function in patients with acute ischemic middle cerebral artery (MCA) stroke using individual clinical, transcranial magnetic stimulation (TMS), or diffusion tensor imaging (DTI) parameters or their combination. The results showed that a prediction model combining clinical evaluation, TMS, and DTI parameters had high accuracy. These findings have important clinical implications for predicting the recovery of patients.