Effects of anodal transcranial direct current stimulation on intracranial compliance in the subacute phase of stroke.

OBJECTIVES: Transcranial direct current stimulation (tDCS) increases cerebral blood flow. This study evaluated the effects of anodal tDCS (A-tDCS) on intracranial compliance (ICC) in patients with subacute stroke using a non-invasive method. METHODS: This was a randomized, proof-of-concept, double-blind, pilot study. Patients with ischemic stroke of the middle cerebral artery (MCA) were divided into the following two groups: 1) A-tDCS in the motor cortex on the affected side for 30 min at 2 mA, and 2) sham tDCS in the motor cortex on the affected side. The primary outcomes were intracranial compliance (P2/P1 ratio and time-to-peak [TTP]) and ICC normalization after the intervention (P2/P1 ratio <1). Secondary outcomes were systolic and diastolic blood pressures, heart rate, and peripheral oxygen saturation. RESULTS: No significant differences were observed in the P2/P1 ratio (P = 0.509) and TTP (P = 0.480) between the groups. However, the A-tDCS group was significantly associated with a normal P2/P1 ratio after intervention (B = 2.583; standard error [SE]: 1.277; P = 0.043; corrected for age and stroke severity). No significant associations were observed between the groups and systolic blood pressure (F = 0.16; P = 0.902), diastolic blood pressure (F = 0.18; P = 0.892), heart rate (F = 0.11; P = 0.950), or peripheral oxygen saturation (F = 0.21; P = 0.750). CONCLUSION: ICC morphology normalization was observed in the A-tDCS group. However, no differences were observed in the P2/P1 ratio, TTP, or hemodynamic variables between the groups. A sample size of 66 patients with ischemic stroke of the MCA can be estimated using the observed effect size and standard α = 5 % and ß = 20 % for future trials. Furthermore, this will aid in conducting the necessary randomized trials targeting these populations.

Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept.

INTRODUCTION: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. METHODS: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. RESULTS: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. CONCLUSION: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation.