Background: Repetition of motor imagery improves the motor function of patients with stroke. However, patients who develop severe upper-limb paralysis after chronic stroke often have an impaired ability to induce motor imagery. We have developed a method to passively induce kinesthetic perception using visual stimulation (kinesthetic illusion induced by visual stimulation [KINVIS]). Objective: This pilot study further investigated the effectiveness of KINVIS in improving the induction of kinesthetic motor imagery in patients with severe upper-limb paralysis after stroke. Methods: Twenty participants (11 with right hemiplegia and 9 with left hemiplegia; mean time from onset [±standard deviation], 67.0±57.2 months) with severe upper-limb paralysis who could not extend their paretic fingers were included in this study. The ability to induce motor imagery was evaluated using the event-related desynchronization (ERD) recorded during motor imagery before and after the application of KINVIS for 20 min. The alpha- and beta-band ERDs around the premotor, primary sensorimotor, and posterior parietal cortices of the affected and unaffected hemispheres were evaluated during kinesthetic motor imagery of finger extension and before and after the intervention. Results: Beta-band ERD recorded from the affected hemisphere around the sensorimotor area showed a significant increase after the intervention, while the other ERDs remained unchanged. Conclusions: In patients with chronic stroke who were unable to extend their paretic fingers for a prolonged period of time, the application of KINVIS, which evokes kinesthetic perception, improved their ability to induce motor imagery. Our findings suggest that although KINVIS is a passive intervention, its short-term application can induce changes related to the motor output system.

Rehabilitation methods for executive dysfunction were focused on cognitive rehabilitation in patients with stroke and traumatic brain injury. However, no reviews have focused on the various rehabilitation methods and assessment of executive function in patients with only stroke and included various study designs. This study aimed to identify various interventions and assessments in patients with stroke and executive dysfunction via a scoping review. We searched for articles using the PubMed, Web of Science, and CINAHL databases. Two reviewers independently screened the articles based on the inclusion and exclusion criteria using the title, abstract, and full text. We subsequently determined the study design, sample size, time since stroke, intervention, and assessment. We extracted 1131 articles, of which 27 articles were selected. The study designs were randomized controlled trials (81.5%), pilot studies (11.1%), and feasibility studies (7.4%), with a total of 599 participants. Interventions varied from cognitive training (22.2%), virtual reality (22.2%), noninvasive brain stimulation (14.8%), and dual-task training (11.1%), with consistent results. The assessments used were the Trail Making Test Part B (70.4%), Stroop Color and Word Test (44.4%), Digit Symbol Test, Frontal Assessment Battery, and Tower of London test (11.1%). In conclusion, this scoping review provided various interventions and assessments in patients with stroke with executive dysfunction.

Previous studies have shown an association between executive dysfunction and walking ability. However, it remains unclear whether the degree of executive dysfunction is associated with differences in walking ability in patients with stroke. The aim of this study was to investigate whether there are differences in walking ability according to executive dysfunction in patients with stroke. A total of 51 patients with stroke were enrolled in this study. Executive function was measured using the Trail Making Test (TMT) Part B, and walking ability was assessed using the 10 m walk test and the Timed Up and Go Test (TUGT). Cluster analysis was performed using the TMT Part B and compared within each cluster. TMT Part B was categorized into three groups (cluster 1: n = 20, cluster 2: n = 24, and cluster 3: n = 7). Cluster 1 was significantly better than clusters 2 and 3, and cluster 2 was significantly better than cluster 3. The 10 m walk time and TUGT of cluster 1 were significantly better than those of cluster 3. However, the 10 m walk time and TUGT of clusters 1 and 2 did not differ significantly. In conclusion, these findings may indicate differences in walking ability according to executive dysfunction.