The Predictive Value of Motor-Evoked Potentials and the Silent Period on Patient Outcome after Acute Cerebral Infarction

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life.

The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.

The Effects of Modified Constraint-Induced Movement Therapy in Acute Subcortical Cerebral Infarction

Background: Constraint-induced movement therapy (CIMT) promotes upper extremity recovery post stroke, however, it is difficult to implement clinically due to its high resource demand and safety of the restraint. Therefore, we propose that modified CIMT (mCIMT) be used to treat individuals with acute subcortical infarction. Objective: To evaluate the therapeutic effects of mCIMT in patients with acute subcortical infarction, and investigate the possible mechanisms underlying the effect. Methods: The role of mCIMT was investigated in 26 individuals experiencing subcortical infarction in the preceding 14 days. Patients were randomly assigned to either mCIMT or standard therapy. mCIMT group was treated daily for 3 h over 10 consecutive working days, using a mitt on the unaffected arm for up to 30% of waking hours. The control group was treated with an equal dose of occupational therapy and physical therapy. During the 3-month follow-up, the motor functions of the affected limb were assessed by the Wolf Motor Function Test (WMFT) and Motor Activity Log (MAL). Altered cortical excitability was assessed via transcranial magnetic stimulation (TMS). Results: Treatment significantly improved the movement in the mCIMT group compared with the control group. The mean WMF score was significantly higher in the mCIMT group compared with the control group. Further, the appearance of motor-evoked potentials (MEPs) were significantly higher in the mCIMT group compared with the baseline data. A significant change in ipsilesional silent period (SP) occurred in the mCIMT group compared with the control group. However, we found no difference between two groups in motor function or electrophysiological parameters after 3 months of follow-up. Conclusions: mCIMT resulted in significant functional changes in timed movement immediately following treatment in patients with acute subcortical infarction. Further, early mCIMT improved ipsilesional cortical excitability. However, no long-term effects were seen.