Increased cognitive load in immersive virtual reality during visuomotor adaptation is associated with decreased long-term retention and context transfer.

BACKGROUND: Complex motor tasks in immersive virtual reality using a head-mounted display (HMD-VR) have been shown to increase cognitive load and decrease motor performance compared to conventional computer screens (CS). Separately, visuomotor adaptation in HMD-VR has been shown to recruit more explicit, cognitive strategies, resulting in decreased implicit mechanisms thought to contribute to motor memory formation. However, it is unclear whether visuomotor adaptation in HMD-VR increases cognitive load and whether cognitive load is related to explicit mechanisms and long-term motor memory formation. METHODS: We randomized 36 healthy participants into three equal groups. All groups completed an established visuomotor adaptation task measuring explicit and implicit mechanisms, combined with a dual-task probe measuring cognitive load. Then, all groups returned after 24-h to measure retention of the overall adaptation. One group completed both training and retention tasks in CS (measuring long-term retention in a CS environment), one group completed both training and retention tasks in HMD-VR (measuring long-term retention in an HMD-VR environment), and one group completed the training task in HMD-VR and the retention task in CS (measuring context transfer from an HMD-VR environment). A Generalized Linear Mixed-Effect Model (GLMM) was used to compare cognitive load between CS and HMD-VR during visuomotor adaptation, t-tests were used to compare overall adaptation and explicit and implicit mechanisms between CS and HMD-VR training environments, and ANOVAs were used to compare group differences in long-term retention and context transfer. RESULTS: Cognitive load was found to be greater in HMD-VR than in CS. This increased cognitive load was related to decreased use of explicit, cognitive mechanisms early in adaptation. Moreover, increased cognitive load was also related to decreased long-term motor memory formation. Finally, training in HMD-VR resulted in decreased long-term retention and context transfer. CONCLUSIONS: Our findings show that cognitive load increases in HMD-VR and relates to explicit learning and long-term motor memory formation during motor learning. Future studies should examine what factors cause increased cognitive load in HMD-VR motor learning and whether this impacts HMD-VR training and long-term retention in clinical populations.

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Transfer of motor skill between virtual reality viewed using a head-mounted display and conventional screen environments.

BACKGROUND: Virtual reality viewed using a head-mounted display (HMD-VR) has the potential to be a useful tool for motor learning and rehabilitation. However, when developing tools for these purposes, it is important to design applications that will effectively transfer to the real world. Therefore, it is essential to understand whether motor skills transfer between HMD-VR and conventional screen-based environments and what factors predict transfer. METHODS: We randomized 70 healthy participants into two groups. Both groups trained on a well-established measure of motor skill acquisition, the Sequential Visual Isometric Pinch Task (SVIPT), either in HMD-VR or in a conventional environment (i.e., computer screen). We then tested whether the motor skills transferred from HMD-VR to the computer screen, and vice versa. After the completion of the experiment, participants responded to questions relating to their presence in their respective training environment, age, gender, video game use, and previous HMD-VR experience. Using multivariate and univariate linear regression, we then examined whether any personal factors from the questionnaires predicted individual differences in motor skill transfer between environments. RESULTS: Our results suggest that motor skill acquisition of this task occurs at the same rate in both HMD-VR and conventional screen environments. However, the motor skills acquired in HMD-VR did not transfer to the screen environment. While this decrease in motor skill performance when moving to the screen environment was not significantly predicted by self-reported factors, there were trends for correlations with presence and previous HMD-VR experience. Conversely, motor skills acquired in a conventional screen environment not only transferred but improved in HMD-VR, and this increase in motor skill performance could be predicted by self-reported factors of presence, gender, age and video game use. CONCLUSIONS: These findings suggest that personal factors may predict who is likely to have better transfer of motor skill to and from HMD-VR. Future work should examine whether these and other predictors (i.e., additional personal factors such as immersive tendencies and task-specific factors such as fidelity or feedback) also apply to motor skill transfer from HMD-VR to more dynamic physical environments.

Embodiment Is Related to Better Performance on a Brain-Computer Interface in Immersive Virtual Reality: A Pilot Study.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) for motor rehabilitation aim to "close the loop" between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays considered more immersive (e.g., head-mounted display virtual reality (HMD-VR)) to displays considered less immersive (e.g., computer screens). However, it is not clear whether more immersive displays improve neurofeedback performance and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot study, we compared neurofeedback performance in HMD-VR versus a computer screen in 12 healthy individuals and examined whether individual differences on two measures (i.e., presence, embodiment) were related to neurofeedback performance in either environment. We found that, while participants' performance on the BCI was similar between display conditions, the participants' reported levels of embodiment were significantly different. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to a computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in HMD-VR. Overall, these preliminary results suggest that embodiment may relate to better performance on EEG-based BCIs and that HMD-VR may increase embodiment compared to computer screens.

Stroke atlas of the brain: Voxel-wise density-based clustering of infarct lesions topographic distribution.

OBJECTIVE: The supply territories of main cerebral arteries are predominantly identified based on distribution of infarct lesions in patients with large arterial occlusion; whereas, there is no consensus atlas regarding the supply territories of smaller end-arteries. In this study, we applied a data-driven approach to construct a stroke atlas of the brain using hierarchical density clustering in large number of infarct lesions, assuming that voxels/regions supplied by a common end-artery tend to infarct together. METHODS: A total of 793 infarct lesions on MRI scans of 458 patients were segmented and coregistered to MNI-152 standard brain space. Applying a voxel-wise data-driven hierarchical density clustering algorithm, we identified those voxels that were most likely to be part of same infarct lesions in our dataset. A step-wise clustering scheme was applied, where the clustering threshold was gradually decreased to form the first 20 mother (>50 cm3) or main (1-50 cm3) clusters in addition to any possible number of tiny clusters (<1 cm3); and then, any resultant mother clusters were iteratively subdivided using the same scheme. Also, in a randomly selected 2/3 subset of our cohort, a bootstrapping cluster analysis with 100 permutations was performed to assess the statistical robustness of proposed clusters. RESULTS: Approximately 91% of the MNI-152 brain mask was covered by 793 infarct lesions across patients. The covered area of brain was parcellated into 4 mother, 16 main, and 123 tiny clusters at the first hierarchy level. Upon iterative clustering subdivision of mother clusters, the brain tissue was eventually parcellated into 1 mother cluster (62.6 cm3), 181 main clusters (total volume 1107.3 cm3), and 917 tiny clusters (total volume of 264.8 cm3). In bootstrap analysis, only 0.12% of voxels, were labelled as "unstable" - with a greater reachability distance in cluster scheme compared to their corresponding mean bootstrapped reachability distance. On visual assessment, the mother/main clusters were formed along supply territories of main cerebral arteries at initial hierarchical levels, and then tiny clusters emerged in deep white matter and gray matter nuclei prone to small vessel ischemic infarcts. CONCLUSIONS: Applying voxel-wise data-driven hierarchical density clustering on a large number of infarct lesions, we have parcellated the brain tissue into clusters of voxels that tend to be part of same infarct lesion, and presumably representing end-arterial supply territories. This hierarchical stroke atlas of the brain is shared publicly, and can potentially be applied for future infarct location-outcome analysis.

Mindfulness Meditation Effects on Poststroke Spasticity: A Feasibility Study.

This study examined the feasibility of an adapted 2-week mindfulness meditation protocol for chronic stroke survivors. In addition, preliminary effects of this adapted intervention on spasticity and quality of life in individuals after stroke were explored. Ten chronic stroke survivors with spasticity listened to 2 weeks of short mindfulness meditation recordings, adapted from Jon Kabat-Zinn's Mindfulness-Based Stress Reduction course, in a pre/post repeated measures design. Measures of spasticity, quality of life, mindfulness, and anxiety, along with qualitative data from participants' daily journals, were assessed. On average, participants reported meditating 12.5 days of the full 15 days (mean 12.5 days, SD 0.94, range 8-15 days). Seven of the 10 participants wrote comments in their journals. In addition, there were no adverse effects due to the intervention. Exploratory preliminary analyses also showed statistically significant improvements in spasticity in both the elbow (P = .032) and wrist (P = .023) after 2 weeks of meditation, along with improvements in quality of life measures for Energy (P = .013), Personality (P = .026), and Work/Productivity (P = .032). This feasibility study suggests that individuals with spasticity following stroke are able to adhere to a 2-week home-based mindfulness meditation program. In addition, preliminary results also suggest that this adapted, short mindfulness meditation program might be a promising approach for individuals with spasticity following stroke. Future research should expand on these preliminary findings with a larger sample size and control group.