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Background Multiplayer video games promoting exercise-based rehabilitation may facilitate motor learning, by increasing motivation through social interaction. However, a major design challenge is to enable meaningful inter-subject interaction, whilst allowing for significant skill differences between players. We present a novel motor-training paradigm that allows real-time collaboration and performance enhancement, across a wide range of inter-subject skill mismatches, including disabled vs. able-bodied partnerships. Methods A virtual task consisting of a dynamic ball on a beam, is controlled at each end using independent digital force-sensing handgrips. Interaction is mediated through simulated physical coupling and locally-redundant control. Game performance was measured in 16 healthy-healthy and 16 patient-expert dyads, where patients were hemiparetic stroke survivors using their impaired arm. Dual-player was compared to single-player performance, in terms of score, target tracking, stability, effort and smoothness; and questionnaires probing user-experience and engagement. Results Performance of less-able subjects (as ranked from single-player ability) was enhanced by dual-player mode, by an amount proportionate to the partnership’s mismatch. The more abled partners’ performances decreased by a similar amount. Such zero-sum interactions were observed for both healthy-healthy and patient-expert interactions. Dual-player was preferred by the majority of players independent of baseline ability and subject group; healthy subjects also felt more challenged, and patients more skilled. Conclusion This is the first demonstration of implicit skill balancing in a truly collaborative virtual training task leading to heightened engagement, across both healthy subjects and stroke patients. Electronic supplementary material The online version of this article (doi:10.1186/s12984-017-0319-x) contains supplementary material, which is available to authorized users.

An important challenge for technology-assisted self-led rehabilitation is how to automate appropriate schedules of exercise that are responsive to patients’ needs, and optimal for learning. While random scheduling has been found to be superior for long-term learning relative to fixed scheduling (Contextual Interference), this method is limited by not adequately accounting for task difficulty, or skill acquisition during training. One method that combines contextual interference with adaptation of the challenge to the skill-level of the player is Challenge Point Framework (CPF) theory. In this pilot study we test whether self-led motor training based upon CPF scheduling achieves faster learning than deterministic, fixed scheduling. Training was implemented in a mobile gaming device adapted for arm disability, allowing for grip and wrist exercises. We tested 11 healthy volunteers and 12 hemiplegic stroke patients in a single-blinded no crossover controlled randomized trial. Results suggest that patients training with CPF-based adaption performed better than those training with fixed conditions. This was not seen for healthy volunteers whose performance was close to ceiling. Further data collection is required to determine the significance of the results.

Significance Simple voluntary movements (e.g., reaching or gripping) deteriorate with distraction, suggesting that the attention-control system—which suppresses distraction—influences motor control. Here, we tested the causal dependency of simple movements on attention control, and its neuroanatomical basis, in healthy elderly and patients with focal brain lesions. Not only did we find that attention control correlates with motor performance, correcting for lesion size, fatigue, etc., but we found a revealing pattern of dissociations: Severe motor impairment could occur with normal attention control whereas impaired attention control never occurred with disproportionately milder motor impairment—suggesting that attention control is required for normal motor performance. One implication is that a component of stroke paralysis arises from poor attentional control, which could itself be a therapeutic target., Attention control (or executive control) is a higher cognitive function involved in response selection and inhibition, through close interactions with the motor system. Here, we tested whether influences of attention control are also seen on lower level motor functions of dexterity and strength—by examining relationships between attention control and motor performance in healthy-aged and hemiparetic-stroke subjects (n = 93 and 167, respectively). Subjects undertook simple-tracking, precision-hold, and maximum force-generation tasks, with each hand. Performance across all tasks correlated strongly with attention control (measured as distractor resistance), independently of factors such as baseline performance, hand use, lesion size, mood, fatigue, or whether distraction was tested during motor or nonmotor cognitive tasks. Critically, asymmetric dissociations occurred in all tasks, in that severe motor impairment coexisted with normal (or impaired) attention control whereas normal motor performance was never associated with impaired attention control (below a task-dependent threshold). This implies that dexterity and force generation require intact attention control. Subsequently, we examined how motor and attention-control performance mapped to lesion location and cerebral functional connectivity. One component of motor performance (common to both arms), as well as attention control, correlated with the anatomical and functional integrity of a cingulo-opercular “salience” network. Independently of this, motor performance difference between arms correlated negatively with the integrity of the primary sensorimotor network and corticospinal tract. These results suggest that the salience network, and its attention-control function, are necessary for virtually all volitional motor acts while its damage contributes significantly to the cardinal motor deficits of stroke.

Passive rehabilitation devices, providing motivation and feedback, potentially offer an automated and low-cost therapy method, and can be used as simple human–machine interfaces. Here, we ask whether there is any advantage for a hand-training device to be elastic, as opposed to rigid, in terms of performance and preference. To address this question, we have developed a highly sensitive and portable digital handgrip, promoting independent and repetitive rehabilitation of grasp function based around a novel elastic force and position sensing structure. A usability study was performed on 66 healthy subjects to assess the effect of elastic versus rigid handgrip control during various visuomotor tracking tasks. The results indicate that, for tasks relying either on feedforward or on feedback control, novice users perform significantly better with the elastic handgrip, compared with the rigid equivalent (11% relative improvement, 9–14% mean range; p

Background and Purpose— Visit-to-visit variability in blood pressure (vBP) is associated with ischemic stroke. We sought to determine whether such variability has genetic causes and whether genetic variants associated with BP variability are also associated with ischemic stroke. Methods— A Genome Wide Association Study (GWAS) for loci influencing BP variability was undertaken in 3802 individuals from the Anglo-Scandinavian Cardiac Outcome Trial (ASCOT) study, in which long-term visit-to-visit and within-visit BP measures were available. Because BP variability is strongly associated with ischemic stroke, we genotyped the sentinel single nucleotide polymorphism in an independent ischemic stroke population comprising 8624 cases and 12 722 controls and in 3900 additional (Scandinavian) participants from the ASCOT study to replicate our findings. Results— The ASCOT discovery GWAS identified a cluster of 17 correlated single nucleotide polymorphisms within the NLGN1 gene (3q26.31) associated with BP variability. The strongest association was with rs976683 ( P =1.4×10 −8 ). Conditional analysis of rs976683 provided no evidence of additional independent associations at the locus. Analysis of rs976683 in patients with ischemic stroke found no association for overall stroke (odds ratio, 1.02; 95% CI, 0.97–1.07; P =0.52) or its subtypes: cardioembolic (odds ratio, 1.07; 95% CI, 0.97–1.16; P =0.17), large vessel disease (odds ratio, 0.98; 95% CI, 0.89–1.07; P =0.60), and small vessel disease (odds ratio, 1.07; 95% CI, 0.97–1.17; P =0.19). No evidence for association was found between rs976683 and BP variability in the additional (Scandinavian) ASCOT participants ( P =0.18). Conclusions— We identified a cluster of single nucleotide polymorphisms at the NLGN1 locus showing significant association with BP variability. Follow-up analyses did not support an association with risk of ischemic stroke and its subtypes.

Objective: To determine whether behavioral dissociations and interactions occur between the attentional functions—alerting, orienting, and conflict resolution—depending upon stroke location and to determine the approximate proportion of patients who can be classified into 1 of these 3 anatomical networks. Methods: We recruited 110 anatomically unselected acute stroke patients and 62 age-matched controls. Subjects underwent the attention network test (ANT), which provides a measure of each attention type. Their performance was related to lesion anatomy on MRI using a voxel-lesion mapping approach. Results: Patients as a whole performed poorer than controls, but there were no group differences in the size of attentional effects. Specific deficits in 1 of the 3 ANT-tested functions were found in the following lesion locations: alerting deficiency with bilateral anteromedial thalamus and upper brainstem (17% of patients); orienting impairment with right pulvinar and right temporoparietal cortex (15%); conflict resolution with bilateral prefrontal and premotor areas (23%). Lesions to right frontoparietal regions also modified interactions among the 3 types of attention. Conclusions: More than half of all stroke patients can be expected to have a lesion location classifiable into 1 of the 3 principal attention networks. Our results have potential implications for therapy personalization in focal brain diseases including stroke.

We present a collaborative training game, based on a novel task where the participants are virtually but dynamically coupled and require collective actions for successful task completion. This can be considered a new type of interpersonal interaction which both increases player motivation during training (compared to single-player participation) and also intrinsically balances the skill levels of the two partners without the need for an additional procedure. This is achieved by a temporary averaging, during collaboration, of the individual performance’s which leads to a more balanced playing field and challenge point being set for both partners.

Repetitive retraining of grip function is critical for stroke patients to regain functional abilities. Therefore, we have developed ‘GripAble’, a low-cost but sensitive hand-grip promoting independent and repetitive rehabilitation of grasp function based around a novel flexible structure. An initial usability study was performed on (N = 34) healthy subjects to compare user interaction between this flexible structure and an equivalent completely rigid version during a predictive feed-forward visual tracking task. Results indicate that at similar or higher tracking performance level, there was a highly significant user preference for the flexible hand-grip with over 62% of subjects preferring it compared to 26% for the rigid device.

Cerebral small vessel disease SVD is a common cause of ageing-associated physical and cognitive impairment. Identifying SVD is important for both clinical and research purposes but is usually dependent on radiologists' evaluation of brain scans. Computer tomography CT is the most widely used brain imaging technique but for SVD it shows a low signal-to-noise ratio, and consequently poor inter-rater reliability. We therefore propose a novel framework based on multiple instance learning MIL to distinguish between absent/mild SVD and moderate/severe SVD. Intensity patches are extracted from regions with high probability of containing lesions. These are then used as instances in MIL for the identification of SVD. A large baseline CT dataset, consisting of 590 CT scans, was used for evaluation. We achieved approximately 75% accuracy in classifying two different types of SVD, which is high for this challenging problem. Our results outperform those obtained by either standard machine learning methods or current clinical practice.

Background and Purpose— Elevated total plasma homocysteine (tHcy) levels are known to be associated with increased risk of ischemic stroke (IS). Given that both tHcy and IS are heritable traits, we investigated a potential genetic relationship between homocysteine levels and stroke risk by assessing 18 polymorphisms previously associated with tHcy levels for their association with IS and its subtypes. Methods— Previous meta-analysis results from an international stroke collaborative network, METASTROKE, were used to assess association of the 18 tHcy-associated single-nucleotide polymorphisms (SNPs) in 12 389 IS cases and 62 004 controls. We also investigated the associations in regions located within 50 kb from the 18 tHcy-related SNPs and the association of a genetic risk score, including the 18 SNPs. Results— One SNP located in the RASIP1 gene and a cluster of 3 SNPs located at and near SLC17A3 were significantly associated with IS ( P MUT was significantly associated with small-vessel disease ( P =0.0022), whereas 1 SNP located in MTHFR was significantly associated with large-vessel disease ( P =0.00019). A genetic risk score, including the 18 SNPs, did not show significant association with IS or its subtypes. Conclusions— This study found several potential associations with IS and its subtypes: an association of an MUT variant with small-vessel disease, an MTHFR variant with large-vessel disease, and associations of RASIP1 and SLC17A3 variants with overall IS.

A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis — a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH). Certain imaging features based upon early computerized tomography (CT), in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH). The sample was split into training (n = 106) and test sets (n = 10), repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM), along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort). Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC), of the SVM (0.744) compared favourably with that of prognostic scores (original and adapted versions: 0.626–0.720; p, Highlights • Machine learning of acute stroke CTs may predict thrombolysis-associated haemorrhage. • CT machine learning also circumvents high variability of radiologist interpretations. • Favourable performance of CT machine learning reported here warrants further research.

Item does not contain fulltext Background and purpose: Strokes caused by lesions to certain brain areas are associated with poor outcome, which is important both prognostically and to understand the neural basis for recovery. However, lesion anatomy associations with outcome may occur because of effects on baseline severity rather than because of effects on recovery per se. Here, all common stroke locations were surveyed to determine the strongest lesion anatomy associations separately for baseline functional severity and proportional recovery. Since most recovery occurs early, the focus here is on functional changes over the first week. Method: Global functional scores (National Institutes of Health Stroke Scale) at baseline and proportional recovery over 1 week were derived from the records of 550 ischaemic stroke patients and related to magnetic resonance imaging lesion location using voxel-lesion mapping. The effects of lesions extending over more than one location were also considered. Cross-validation estimated the percentage of recovery rate variance (r(2)) accountable by lesion location information. Results: High baseline severity was associated with lesions to the left capsule, striatum and thalamocortical white matter, whereas high recovery rate was associated with lesions to more superficial left fronto-temporal areas. Low recovery rates were associated with lesions to bilateral parietal, right insula, medial frontal, capsule and brainstem. Inclusion of these regions into a multivariate model of proportional recovery rate increased r(2) from 8% to 45%. Conclusion: The strongest stroke lesion location associations with 1-week recovery were identified, and it was shown that anatomical information accounts for a sizeable proportion of early recovery variability.

Objective To determine whether machine learning of acute stroke computerised tomography (CT) images helps predict Symptomatic Intracranial Haemorrhage (SICH), following treatment with intravenous thrombolysis (tPA). Methods Clinical records and CT-scans of 116 acute ischaemic stroke patients treated with intravenous tPA were collected retrospectively, comprising 100 patients who subsequently improved, and 16 who developed SICH. The sample was split into training (n=106) and test sets (n=10), the latter of which contained one SICH-to-be subject every time, repeated with different combinations 1760 times. Raw CT-scans were spatially normalised and input into a Support Vector Machine (SVM), adjusted for clinical severity. Performance of the SVM was compared with validated prognostication tools (SEDAN and HAT scores), by assessing area under receiver operating characteristic curves (AUC), and success on 1/10 forced-choice tests. Results AUC of the SVM (0.72) compared favourably with that of SEDAN and HAT scores (0.63–0.71). The SVM also identified 9/16 SICHs on forced-choice tests, as opposed to 1/4 using prognostic scores. Conclusions This proof-of-concept study shows that machine learning methods applied to acute stroke CT-scans potentially offers automation, and improved performance in SICH prediction following thrombolysis. Larger-scale cohorts, and incorporation of CT perfusion/angiography data, should be tested with such methods.