Action Observation for Neurorehabilitation in Apraxia

Neurorehabilitation and brain stimulation studies of post-stroke patients suggest that action-observation effects can lead to rapid improvements in the recovery of motor functions and long-term motor cortical reorganization. Apraxia is a clinically important disorder characterized by marked impairment in representing and performing skillful movements [gestures], which limits many daily activities and impedes independent functioning. Recent clinical research has revealed errors of visuo-motor integration in patients with apraxia. This paper presents a rehabilitative perspective focusing on the possibility of action observation as a therapeutic treatment for patients with apraxia. This perspective also outlines impacts on neurorehabilitation and brain repair following the reinforcement of the perceptual-motor coupling. To date, interventions based primarily on action observation in apraxia have not been undertaken

Observation of Amounts of Movement Practice Provided during Stroke Rehabilitation

Objective To investigate how much movement practice occurred during stroke rehabilitation, and what factors might influence doses of practice provided. Design Observational survey of stroke therapy sessions. Setting Seven inpatient and outpatient rehabilitation sites. Participants We observed a convenience sample of 312 physical and occupational therapy sessions for people with stroke. Interventions Not applicable. Main Outcome Measures We recorded numbers of repetitions in specific movement categories and data on potential modifying factors (patient age, side affected, time since stroke, FIM item scores, years of therapist experience). Descriptive statistics were used to characterize amounts of practice. Correlation and regression analyses were used to determine whether potential factors were related to the amount of practice in the 2 important categories of upper extremity functional movements and gait steps. Results Practice of task-specific, functional upper extremity movements occurred in 51% of the sessions that addressed upper limb rehabilitation, and the average number of repetitions/session was 32 (95% confidence interval [CI]=20–44). Practice of gait occurred in 84% of sessions that addressed lower limb rehabilitation and the average number of gait steps/session was 357 (95% CI=296–418). None of the potential factors listed accounted for significant variance in the amount of practice in either of these 2 categories. Conclusions The amount of practice provided during poststroke rehabilitation is small compared with animal models. It is possible that current doses of task-specific practice during rehabilitation are not adequate to drive the neural reorganization needed to promote function poststroke optimally

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Body weight changes and incidence of cachexia after stroke

Background: Body weight loss is a frequent complication after stroke, and its adverse effect on clinical outcome has been shown in several clinical trials. The purpose of this prospective longitudinal single-centre observational study was to investigate dynamical changes of body composition and body weight after ischemic stroke and an association with functional outcome. Methods: Sixty-seven consecutive patients (age 69 ± 11 years, body mass index 27.0 ± 4.1 kg/m2, 42% female patient, mean ± SD) with acute ischemic stroke with mild to moderate neurological deficit (National Institute of Health Stroke Scale median 4, ranged 0–12) were analysed in the acute phase (4 ± 2 days) and at 12 months (389 ± 26 days) follow-up. Body composition was examined by dual energy X-ray absorptiometry. Cachexia was defined according to the consensus definition by body weight loss ≥5% within 1 year and additional clinical signs. Lean tissue wasting was considered if a ratio of upper and lower limbs lean mass sum to squared height (kg/m2) was ≤5.45 kg/m2 for female patient and ≤7.25 kg/m2 for male patient. Results: According to the body weight changes after 12 months, 42 (63%) patients had weight gain or stable weight, 11 (16%) patients had moderate weight loss, and 14 (21%) patients became cachectic. A relative decline of 19% of fat tissue and 6.5% of lean tissue was observed in cachectic patients, while no changes of lean tissue were observed in non-cachectic patients after 12 months. The modified Rankin Scale was 48% higher (2.1 ± 1.6, P < 0.05), Barthel Index was 22% lower (71 ± 39, P < 0.01), and handgrip strength was 34% lower (21.9 ± 13.0, P < 0.05) in cachectic compared to non-cachectic patients after 12 months. The low physical performance if defined by Barthel Index <60 points was linked to the lean tissue wasting (OR 44.8, P < 0.01), presence of cachexia (OR 20.8, P < 0.01), and low body mass index <25 kg/m2 (OR 11.5, P < 0.05). After adjustment for cofounders, lean tissue wasting remained independently associated with the low physical performance at 12 months follow-up (OR 137.9, P < 0.05). Conclusions: In this cohort study, every fifth patient with ischemic stroke fulfilled the criteria of cachexia within 12 months after index event. The incidence of cachexia was 21%. Cachectic patients showed the lowest functional and physical capacity