Background Spinal cord injury (SCI) is often associated with long-term impairments related to functional limitations in the sensorimotor system. The use of virtual reality (VR) technology may lead to increased motivation and engagement, besides allowing a wide range of possible tasks/exercises to be implemented in rehabilitation programs. The present review aims to investigate the possible benefits and efficacy of VR-based rehabilitation in individuals with SCI. Methods An electronically systematic search was performed in multiple databases (PubMed, BVS, Web of Science, Cochrane Central, and Scielo) up to May 2019. MESH terms and keywords were combined in a search strategy. Two reviewers independently selected the studies in accordance with eligibility criteria. The PEDro scale was used to score the methodological quality and risk of bias of the selected studies. Results Twenty-five studies (including 482 participants, 47.6 ± 9.5 years, 73% male) were selected and discussed. Overall, the studies used VR devices in different rehabilitation protocols to improve motor function, driving skills, balance, aerobic function, and pain level, as well as psychological and motivational aspects. A large amount of heterogeneity was observed as to the study design, VR protocols, and outcome measures used. Only seven studies (28%) had an excellent/good quality of evidence. However, substantial evidence for significant positive effects associated with VR therapy was found in most of the studies (88%), with no adverse events (88%) being reported. Conclusion Although the current evidence is limited, the findings suggest that VR-based rehabilitation in subjects with SCI may lead to positive effects on aerobic function, balance, pain level, and motor function recovery besides improving psychological/motivational aspects. Further high-quality studies are needed to provide a guideline to clinical practice and to draw robust conclusions about the potential benefits of VR therapy for SCI patients. Protocol details are registered on PROSPERO (registration number: CRD42016052629).

BackgroundRepetitive transcranial magnetic stimulation (rTMS) has been investigated as a new tool in neurological rehabilitation of individuals with spinal cord injury (SCI). However, due to the inconsistent results regarding the effects of rTMS in people with SCI, a randomized controlled double-blind crossover trial is needed to clarify the clinical utility and to assess the effect size of rTMS intervention in this population. Therefore, this paper describes a study protocol designed to investigate whether the use of rTMS can improve the motor and sensory function, as well as reduce spasticity in patients with incomplete SCI.MethodsA double-blind randomized sham-controlled crossover trial will be performed by enrolling 20 individuals with incomplete SCI. Patients who are at least six months post incomplete SCI (aged 18–60 years) will be recruited through referral by medical practitioners or therapists. Individuals will be randomly assigned to either group 1 or group 2 in a 1:1 ratio, with ten individuals in each group. The rTMS protocol will include ten sessions of high-frequency rTMS (5 Hz) over the bilateral lower-limb motor area positioned at the vertex (Cz). Clinical evaluations will be performed at baseline and after rTMS active and sham.DiscussionrTMS has produced positive results in treating individuals with physical impairments; thus, it might be promising in the SCI population. The results of this study may provide new insights to motor rehabilitation thereby contributing towards the better usage of rTMS in the SCI population.Trial registrationClinicalTrials.gov, NCT02899637. Registered on 25 August 2016.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-017-2280-1) contains supplementary material, which is available to authorized users.

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Objective: This study aimed to produce a novel Deep Learning (DL) model for the classification of subjects with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) subjects and Healthy Ageing (HA) subjects using resting-state scalp EEG signals.Approach: The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the Continuous Wavelet Transform (CWT), using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0 to 600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16,197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of 5 hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size.Main Results: The performance was assessed by a 10-fold cross-validation strategy, which produced an average accuracy result of 98.9% ± 0.4% for the three-class classification of AD vs. MCI vs. HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced.Significance: These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.

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