Neural Plasticity and Neurorehabilitation (NPNL)

The articles in this Special Issue on Recovery of Function After Neurological Injury include an impressive range of clinical diagnoses, scientific approaches, and theoretical frameworks that demonstrate the breadth and depth of occupational therapy in the restoration of function after neurological injury. An emerging theme throughout is the need for the profession of occupational therapy to identify and use more efficient methods for diagnosing and treating people with neurological injuries to improve their quality of life and the impact of care.

Importance. Complex telehealth interventions can facilitate remote occupational therapy services and improve access for people living with chronic neurological conditions. Understanding the factors that influence the uptake of these technologies is important.

Objective. To explore the fit between electromyography (EMG) biofeedback and telerehabilitation for stroke survivors, optimize EMG biofeedback interventions, and, more broadly, support other efforts to develop complex telerehabilitation interventions.

Design. Pre-implementation mixed-methods analysis of usability and acceptability data collected during a pilot and feasibility study.

Setting. Community.

Participants. Adult stroke survivors with hemiparesis (N = 11; M age = 54 yr).

Intervention. Game-based EMG biofeedback system for arm sensorimotor rehabilitation, delivered via telehealth.

Outcomes and Measures. Post-Study System Usability Questionnaire, an extended Unified Theory of Acceptance and Use of Technology model questionnaire, and semistructured interview. We coded the interview data using questionnaire constructs.

Results.. Participants used an EMG biofeedback intervention at home. Quantitative measures show high levels of perceived usability and acceptability, supported by qualitative findings describing specific facilitators and barriers.

Conclusions and Relevance. Pre-implementation studies can improve the design and relevance of complex telehealth interventions. One major conclusion from this study is the influence of therapy providers on acceptability and usability of complex telehealth interventions.

Plain-Language Summary. This study contributes to an emerging body of literature that examines the use of complex telehealth interventions with survivors of neurological injury. The findings highlight the value and support the development and use of complex telehealth interventions, which have the potential to improve remote access to occupational therapy for clients living with chronic neurological conditions. Complex telehealth interventions can open doors for survivors of neurological injury who face barriers to accessing occupational therapy and would benefit from technology-enabled therapy at home.

Background. Integrity of the corticospinal tract (CST) is an important biomarker for upper limb motor function following stroke. However, when structurally compromised, other tracts may become relevant for compensation or recovery of function.

Methods. We used the ENIGMA Stroke Recovery data set, a multicenter, retrospective, and cross-sectional collection of patients with upper limb impairment during the chronic phase of stroke to test the relevance of tracts in individuals with less and more severe (laterality index of CST fractional anisotropy ≥0.25) CST damage in an observational study design. White matter integrity was quantified using fractional anisotropy for the CST, the superior longitudinal fascicle, and the callosal fibers interconnecting the primary motor cortices between hemispheres. Optic radiations served as a control tract as they have no a priori relevance for the motor system. Pearson correlation was used for testing correlation with upper limb motor function (Fugl-Meyer upper extremity).

Results. From 1235 available data sets, 166 were selected (by imaging, Fugl-Meyer upper extremity, covariates, stroke location, and stage) for analyses. Only individuals with severe CST damage showed a positive association of fractional anisotropy in both callosal fibers interconnecting the primary motor cortices (r[21]=0.49; P=0.025) and superior longitudinal fascicle (r[21]=0.51; P=0.018) with Fugl-Meyer upper extremity.

Conclusions. Our data support the notion that individuals with more severe damage of the CST depend on residual pathways for achieving better upper limb outcome than those with less affected CST.

Keywords. corticospinal tract; white matter; neuroimaging; diffusion tensor imaging; stroke; upper extremity

Background. Microstructural changes in the corpus callosum (CC) are associated with more severe motor impairment in the paretic hand, poor recovery, and general disability. The purpose of this study was to determine if CC microstructure predicts bimanual motor performance in chronic stroke survivors.

Methods. We examined the relationship between the fractional anisotropy (FA) across the CC, in both the sensorimotor and non-sensorimotor regions, and movement times for two self-initiated and self-paced bimanual tasks in 41 chronic stroke survivors. Using publicly available control datasets (n = 52), matched closely for imaging acquisition parameters, we also explored the effect of stroke and age on callosal microstructure.

Results. In mild-to-moderate chronic stroke survivors with relatively localized lesions to the motor areas, lower callosal FA values, suggestive of a more disorganized microstructure, were associated with slower bimanual performance. Associations were strongest for the primary motor fibers (b = −2.19 ± 1.03, p = .035), followed closely by premotor/supplementary motor (b = −2.07 ± 1.07, p = .041) and prefrontal (b = −1.92 ± 0.97, p = .05) fibers of the callosum. Secondary analysis revealed that compared to neurologically age-similar adults, chronic stroke survivors exhibited significantly lower mean FA in all regions of the CC, except the splenium.

Conclusion. Remote widespread changes in the callosal genu and body are associated with slower performance on cooperative bimanual tasks that require precise and interdependent coordination of the hands. Measures of callosal microstructure may prove to be a useful predictor of real-world bimanual performance in chronic stroke survivors.

Keywords. Diffusion tensor imaging; stroke; corpus callosum; bimanual; recovery; behavior

Electromyography (EMG) biofeedback delivered via telerehabilitation can increase access to occupational therapy services for stroke survivors with severe impairment, but there is limited research on its acceptability. This study identified factors influencing the acceptability of a complex, muscle biofeedback system (Tele-REINVENT) for upper extremity sensorimotor stroke telerehabilitation among stroke survivors. We conducted interviews with stroke survivors (n = 4) who used Tele-REINVENT at home for 6 weeks and analyzed the data with reflexive thematic analysis. Biofeedback, customization, gamification, and predictability affected the acceptability of Tele-REINVENT among stroke survivors. Across themes, features and experiences that gave participants agency and control were more acceptable. Our findings contribute to the design and development of at-home EMG biofeedback interventions, which can improve access to advanced occupational therapy treatment options for those who need it most.

Background and objectives. Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. Here, we examined the impact of brain age, a measure of neurobiological aging derived from whole brain structural neuroimaging, on post-stroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good versus poor outcomes.

Methods. We conducted a cross-sectional observational study using a multi-site dataset of 3D brain structural MRIs and clinical measures from ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a three-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good versus poor outcomes in patients with matched lesion damage.

Results. We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β=0.21; 95% CI: 0.04, 0.38, P=0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (β=-0.28; 95% CI: -0.41, -0.15, P<0.001) and across multiple domains of function (β=-0.14; 95% CI: -0.22, -0.06, P<0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI: 3%, 58%, P=0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (OR=1.04, 95% CI: 1.01, 1.08, P=0.004).

Conclusions. We provide evidence that younger brain age is associated with superior post-stroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of post-stroke outcomes compared to focal injury measures alone, opening new possibilities for potential therapeutic targets.

White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.

Keywords. white matter hyperintensity (WMH), stroke, lesion segmentation, SAMSEG, FSL, BIANCA

Although automated methods for stroke lesion segmentation exist, many researchers still rely on manual segmentation as the gold standard. Our detailed, standardized protocol for stroke lesion tracing on high-resolution 3D T1-weighted (T1w) magnetic resonance imaging (MRI) has been used to trace over 1,300 stroke MRI. In the current study, we describe the protocol, including a step-by-step method utilized for training multiple individuals to trace lesions (“tracers”) in a consistent manner and suggestions for distinguishing between lesioned and non-lesioned areas in stroke brains. Inter-rater and intra-rater reliability were calculated across six tracers trained using our protocol, resulting in an average intraclass correlation of 0.98 and 0.99, respectively, as well as a Dice similarity coefficient of 0.727 and 0.839, respectively. This protocol provides a standardized guideline for researchers performing manual lesion segmentation in stroke T1-weighted MRI, with detailed methods to promote reproducibility in stroke research.

Keywords. stroke, manual segmentation, MRI segmentation, stroke lesion, segmentation protocol, T1w MRI, ITK-SNAP

Stroke is a leading cause of adult disability in the United States. High doses of repeated task-specific practice have shown promising results in restoring upper limb function in chronic stroke. However, it is currently challenging to provide such doses in clinical practice. At-home telerehabilitation supervised by a clinician is a potential solution to provide higher-dose interventions. However, telerehabilitation systems developed for repeated task-specific practice typically require a minimum level of active movement. Therefore, severely impaired people necessitate alternative therapeutic approaches. Measurement and feedback of electrical muscle activity via electromyography (EMG) have been previously implemented in the presence of minimal or no volitional movement to improve motor performance in people with stroke. Specifically, muscle neurofeedback training to reduce unintended co-contractions of the impaired hand may be a targeted intervention to improve motor control in severely impaired populations. Here, we present the preliminary results of a low-cost, portable EMG biofeedback system (Tele-REINVENT) for supervised and unsupervised upper limb telerehabilitation after stroke. We aimed to explore the feasibility of providing higher doses of repeated task-specific practice during at-home training. Therefore, we recruited 5 participants (age = 44–73 years) with chronic, severe impairment due to stroke (Fugl-Meyer = 19–40/66). They completed a 6-week home-based training program that reinforced activity of the wrist extensor muscles while avoiding coactivation of flexor muscles via computer games. We used EMG signals to quantify the contribution of two antagonistic muscles and provide biofeedback of individuated activity, defined as a ratio of extensor and flexor activity during movement attempt. Our data suggest that 30 1-h sessions over 6 weeks of at-home training with our Tele-REINVENT system is feasible and may improve individuated muscle activity as well as scores on standard clinical assessments (e.g., Fugl-Meyer Assessment, Action Research Arm Test, active wrist range of motion) for some individuals. Furthermore, tests of neuromuscular control suggest modest changes in the synchronization of electroencephalography (EEG) and EMG signals within the beta band (12–30 Hz). Finally, all participants showed high adherence to the training protocol and reported enjoying using the system. These preliminary results suggest that using low-cost technology for home-based telerehabilitation after severe chronic stroke is feasible and may be effective in improving motor control via feedback of individuated muscle activity.

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.

Immersive virtual reality using a head-mounted display (HMD-VR) is increasing in use for motor learning and motor skill training. However, it remains unclear how visual information for action is processed in an HMD-VR environment. In the real world, actions towards three-dimensional (3D) objects are processed analytically and are immune to perceptual effects, such as processing object dimensions irrelevant to performing the action (i.e., holistic processing). However, actions towards two-dimensional (2D) objects are processed holistically and are susceptible to perceptual effects. In HMD-VR, distances are often underestimated, and the environment can appear flatter compared to the real world. Thus, actions towards virtual 3D objects in HMD-VR may be processed more like 2D objects and involve holistic processing, which is susceptible to perceptual effects. In an initial study, we used a Garner interference task to examine whether vision-for-action in HMD-VR is processed holistically and hypothesized that vision-for-action towards virtual 3D objects in HMD-VR would result in a Garner interference effect, suggesting holistic processing. We found Garner interference effects for reaction times to reach maximum grip aperture and to complete movement. These results show that visual processing of actions towards virtual 3D objects in HMD-VR may involve holistic processing of object shape. These findings demonstrate that visual information for action in HMD-VR is processed differently compared to real 3D objects and is susceptible to perceptual effects, which could affect motor skill training in HMD-VR.

Keywords. immersive virtual reality, head-mounted display, virtual 3D grasping, vision-for-action, garner interference

Background and objectives. It is difficult to predict post-stroke outcome for people with severe motor impairment, as both clinical tests and corticospinal tract (CST) microstructure may not reliably indicate severe motor impairment. Here, we test whether imaging biomarkers beyond the CST relate to severe upper limb impairment post-stroke by evaluating white matter microstructure in the corpus callosum (CC). In an international, multisite hypothesis-generating observational study we determined if: a) CST asymmetry index can differentiate between individuals with mild-moderate and severe upper limb impairment; and b) CC biomarkers relate to upper limb impairment within individuals with severe impairment post-stroke. We hypothesised that CST asymmetry index would differentiate between mild-moderate and severe impairment, but CC microstructure would relate to motor outcome for individuals with severe upper limb impairment.

Methods. Seven cohorts with individual diffusion imaging and motor impairment (Fugl Meyer-Upper Limb) data were pooled. Hand-drawn regions-of-interest were used to seed probabilistic tractography for CST (ipsilesional/contralesional) and CC (prefrontal/premotor/motor/sensory/posterior) tracts. Our main imaging measure was mean fractional anisotropy. Linear mixed-effect regression explored relationships between candidate biomarkers and motor impairment, controlling for observations nested within cohorts, as well as age, sex, time post-stroke and lesion volume.

Results. Data from 110 individuals (30 mild-moderate, 80 with severe motor impairment) were included. In the full sample, greater CST asymmetry index (i.e., lower fractional anisotropy in the ipsilesional hemisphere, p<.001) and larger lesion volume (p=.139) were negatively related to impairment. In the severe subgroup, CST asymmetry index was not reliably associated with impairment across models. Instead, lesion volume and CC microstructure explained impairment in the severe group beyond CST asymmetry index (p’s<.010).

Conclusions. Within a large cohort of individuals with severe upper limb impairment, CC microstructure related to motor outcome post-stroke. Our findings demonstrate that CST microstructure does relate to upper limb outcome across the full range of motor impairment but was not reliably associated within the severe subgroup. Therefore, CC microstructure may provide a promising biomarker for severe upper limb outcome post-stroke, which may advance our ability to predict recovery in people with severe motor impairment after stroke.

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.

Scientific research has become highly intertwined with digital information, however scientific publication remains based on the static text and figures of principal articles. This discrepancy constrains complex scientific data into 2D static figures, hindering our ability to effectively exchange the complex and extensive information that underlies modern research. Here, we demonstrate how the viewing of digital data can be directly integrated into the existing publication system through both web based and augmented reality (AR) technologies. We additionally provide a framework that makes these capabilities available to the scientific community. Ultimately, augmenting articles with data can modernize scientific communication by bridging the gap between the digital basis of present-day research and the natural limitations of printable articles.

Background. Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment.

Methods and Results. Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P<0.025), controlling for age, sex, lesion volume, and lesioned hemisphere. In exploratory analyses, we tested for a sensorimotor impairment and sex interaction and relationships between lesion volume, sensorimotor damage, and hippocampal volume. Greater sensorimotor impairment was significantly associated with ipsilesional (P=0.005; β=0.16) but not contralesional (P=0.96; β=0.003) hippocampal volume, independent of lesion volume and other covariates (P=0.001; β=0.26). Women showed progressively worsening sensorimotor impairment with smaller ipsilesional (P=0.008; β=−0.26) and contralesional (P=0.006; β=−0.27) hippocampal volumes compared with men. Hippocampal volume was associated with lesion size (P<0.001; β=−0.21) and extent of sensorimotor damage (P=0.003; β=−0.15).

Conclusions. The present study identifies novel associations between chronic poststroke sensorimotor impairment and ipsilesional hippocampal volume that are not caused by lesion size and may be stronger in women.

Lesion load of the corticospinal tract (CST-LL), a measure of overlap between a stroke lesion and the CST, is one of the strongest predictors of motor outcomes following stroke. CST-LL is typically calculated by using a probabilistic map of the CST originating from the primary motor cortex (M1). However, higher order motor areas also have projections that contribute to the CST and motor control. In this retrospective study, we examined whether evaluating CST-LL from additional motor origins is more strongly associated with post-stroke motor severity than using CST-LL originating from M1 only. We found that lesion load to both the ventral premotor (PMv) cortex and M1 were more strongly related to stroke motor severity indexed by Fugl-Meyer Assessment cut-off scores than CST-LL of M1 alone, suggesting that higher order motor regions add clinical relevance to motor impairment.

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.

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long‐term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas‐based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network‐based hippocampal segmentation method, does not rely solely on a single atlas‐based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well‐accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

Keywords. convolutional neural network, hippocampus, image segmentation, lesion, MRI, stroke

Introduction. Repetitive peripheral sensory stimulation (RPSS) followed by 4-hour task-specific training (TST) improves upper limb motor function in subjects with stroke who experience moderate to severe motor upper limb impairments. Here, we compared effects of RPSS vs sham followed by a shorter duration of training in subjects with moderate to severe motor impairments in the chronic phase after stroke.

Methods. This single-center, randomized, placebo-controlled, parallel-group clinical trial compared effects of 18 sessions of either 1.5 h of active RPSS or sham followed by a supervised session that included 45 min of TST of the paretic upper limb. In both groups, subjects were instructed to perform functional tasks at home, without supervision. The primary outcome measure was the Wolf Motor Function Test (WMFT) after 6 weeks of treatment. Grasp and pinch strength were secondary outcomes.

Results. In intention-to-treat analysis, WMFT improved significantly in both active and sham groups at 3 and 6 weeks of treatment. Grasp strength improved significantly in the active, but not in the sham group, at 3 and 6 weeks. Pinch strength improved significantly in both groups at 3 weeks, and only in the active group at 6 weeks.

Conclusions. The between-group difference in changes in WMFT was not statistically significant. Despite the short duration of supervised treatment, WMFT improved significantly in subjects treated with RPSS or sham. These findings are relevant to settings that impose constraints in duration of direct contact between therapists and patients. In addition, RPSS led to significant gains in hand strength.

Importance. Virtual reality in head-mounted displays (HMD-VR) may be a valuable tool in occupational therapy to address anxiety. Findings from the virtual reality exposure therapy (VRET) literature may facilitate translation of HMD-VR to occupational therapy psychosocial practice.

Objective. To explore how HMD-VR has been used to treat anxiety through VRET and could be translated to occupational therapy.

Data Sources. We searched seven electronic databases for articles published between 2000 and 2020: CINAHL, Cochrane Library, Embase, ERIC, Ovid MEDLINE, PsycINFO, and Web of Science. Search terms included HMD-VR constructs, products, and therapy concepts.

Study Selection and Data Collection. We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to report studies implementing VRET to treat anxiety. At least two reviewers assessed each citation, and a third resolved disagreements. Articles were included if they were in English, reported experimental data, and used HMD-VR. Letters, commentaries, book chapters, technical descriptions, theoretical papers, conference proceedings (≤4 pages), and reviews were excluded.

Findings. Twenty-eight studies used HMD-VR to treat posttraumatic stress disorder (n = 3), specific phobias (n = 19), and performance-based social anxiety (n = 6); protocols and levels of evidence varied (randomized controlled trials, n = 11; controlled trials without randomization, n = 6; case–control or cohort studies, n = 11). Qualitative examination indicates HMD-VR is an effective treatment tool.

Conclusions and Relevance. HMD-VR can be a valuable tool for occupational therapy to simulate environments where clients with anxiety disorders participate. Eliciting presence through multisensory features and body representation may enhance outcomes.

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P < 0.004). We tested subacute (≤90 days) and chronic (≥180 days) stroke subgroups separately, with exploratory analyses in early stroke (≤21 days) and across all time. Sub-analyses in chronic stroke were also performed based on class of sensorimotor deficits (impairment, activity limitations) and side of lesioned hemisphere. Worse sensorimotor behaviour was associated with a smaller ipsilesional thalamic volume in both early (n = 179; d = 0.68) and subacute (n = 274, d = 0.46) stroke. In chronic stroke (n = 404), worse sensorimotor behaviour was associated with smaller ipsilesional putamen (d = 0.52) and nucleus accumbens (d = 0.39) volumes, and a larger ipsilesional lateral ventricle (d = −0.42). Worse chronic sensorimotor impairment specifically (measured by the Fugl-Meyer Assessment; n = 256) was associated with smaller ipsilesional putamen (d = 0.72) and larger lateral ventricle (d = −0.41) volumes, while several measures of activity limitations (n = 116) showed no significant relationships. In the full cohort across all time (n = 828), sensorimotor behaviour was associated with the volumes of the ipsilesional nucleus accumbens (d = 0.23), putamen (d = 0.33), thalamus (d = 0.33) and lateral ventricle (d = −0.23). We demonstrate significant relationships between post-stroke sensorimotor behaviour and reduced volumes of deep grey matter structures that were spared by stroke, which differ by time and class of sensorimotor measure. These findings provide additional insight into how different cortico-thalamo-striatal circuits support post-stroke sensorimotor outcomes.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments.

With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

The right hemisphere is involved with the integrative processes necessary to achieve global coherence during reasoning and discourse processing. Specifically, the right temporal lobe has been proven to facilitate the processing of distant associate relationships, such as generating novel ideas. Previous studies showed a specific swing of alpha and gamma oscillatory activity over the right parieto-occipital lobe and the right anterior temporal lobe respectively, when people solve semantic problems with a specific strategy, i.e., insight problem-solving. In this study, we investigated the specificity of the right parietal and temporal lobes for semantic integration using transcranial Random Noise Stimulation (tRNS). We administered a set of pure semantics (i.e., Compound Remote Associates [CRA]) and visuo-semantic problems (i.e., Rebus Puzzles) to a sample of 31 healthy volunteers. Behavioral results showed that tRNS stimulation over the right temporal lobe enhances CRA accuracy (+12%), while stimulation on the right parietal lobe causes a decrease of response time on the same task (−2,100 ms). No effects were detected for Rebus Puzzles. Our findings corroborate the involvement of the right temporal and parietal lobes when solving purely semantic problems but not when they involve visuo-semantic material, also providing causal evidence for their postulated different roles in the semantic integration process and promoting tRNS as a candidate tool to boost verbal reasoning in humans.

Everyday decision-making is supported by a dual-system of control comprised of parallel goal-directed and habitual systems. Over the past decade, the two-stage Markov decision task has become popularized for its ability to dissociate between goal-directed and habitual decision-making. While a handful of studies have implemented decision-making tasks online, only one study has validated the task by comparing in-person and web-based performance on the two-stage task in children and young adults. To date, no study has validated the dissociation of goal-directed and habitual behaviors in older adults online. Here, we implemented and validated a web-based version of the two-stage Markov task using parameter simulation and recovery and compared behavioral results from online and in-person participation on the two-stage task in both young and healthy older adults. We found no differences in estimated free parameters between online and in-person participation on the two-stage task. Further, we replicate previous findings that young adults are more goal-directed than older adults both in-person and online. Overall, this work demonstrates that the implementation and use of the two-stage Markov decision task for remote participation is feasible in the older adult demographic, which would allow for the study of decision-making with larger and more diverse samples.

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.

Keywords. biofeedback; stroke; telerehabilitation; electromyography; human-computer interface

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs (i.e., self‐regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain‐based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Importance. Head-mounted displays for virtual reality (HMD–VR) may be used as a therapeutic medium in physical rehabilitation because of their ability to immerse patients in safe, controlled, and engaging virtual worlds.

Objective. To explore how HMD–VR has been used in adult physical rehabilitation.

Data Sources. A systematic search of MEDLINE, Embase, Cochrane Library, CINAHL, Web of Science, PsycINFO, and ERIC produced 11,453 abstracts, of which 777 underwent full-text review.

Study Selection and Data Collection. This scoping review includes 21 experimental studies that reported an assessment or intervention using HMD–VR in a physical rehabilitation context and within the scope of occupational therapy practice.

Findings. HMD–VR was used for assessment and intervention for patients with a range of disorders, including stroke, multiple sclerosis, spinal cord injury, and Parkinson’s disease.

Conclusions and Relevance. HMD–VR is an emerging technology with many uses in adult physical rehabilitation. Higher quality clinical implementation studies are needed to examine effects on patient outcomes.

What This Article Adds. We review existing research on how immersive virtual reality (e.g., using head-mounted displays) has been used for different clinical populations in adult physical rehabilitation and highlight emerging opportunities in this field for occupational therapists.

Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain–computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain–computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12–30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle–computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.

Keywords: biofeedback; stroke; brain–computer interface; neurorehabilitation; corticomuscular coherence; electromyography; co-contraction; virtual reality

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.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

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.

Keywords: brain–computer interface; neurofeedback; immersive virtual reality; head-mounted display; electroencephalography; presence; embodiment

Accurate stroke lesion segmentation is a critical step in the neuroimaging processing pipeline for assessing the relationship between poststroke brain structure, function, and behavior. Many multimodal segmentation algorithms have been developed for acute stroke neuroimaging, yet few algorithms are effective with only a single T1‐weighted (T1w) anatomical MRI. This is a critical gap because multimodal MRI is not commonly available due to time and cost constraints in the stroke rehabilitation setting. Although several attempts to automate the segmentation of chronic lesions on single‐channel T1w MRI have been made, these approaches have not been systematically evaluated on a large dataset. We performed an exhaustive review of the literature and identified one semiautomated and three fully automated approaches for segmentation of chronic stroke lesions using T1w MRI within the last 10 years: Clusterize, automated lesion identification (ALI), Gaussian naïve Bayes lesion detection (lesionGnb), and lesion identification with neighborhood data analysis (LINDA). We evaluated each method on a large T1w stroke dataset (N = 181). LINDA was the most computationally expensive approach, but performed best across the three main evaluation metrics (median values: dice coefficient = 0.50, Hausdorff's distance = 36.34 mm, and average symmetric surface distance = 4.97 mm). lesionGnb had the highest recall/least false negatives (median = 0.80). However, across the automated methods, many lesions were either misclassified (ALI: 28, lesionGnb: 39, LINDA: 45) or not identified (ALI: 24, LINDA: 23, lesionGnb: 0). Segmentation accuracy in all automated methods were influenced by size (small: worst) and stroke territory (brainstem, cerebellum: worst) of the lesion. To facilitate reproducible science, our analysis files have been made publicly available online.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being ‘good’ or ‘bad.’ Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to ‘bad’ quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). LONI-QC’s functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

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.

Rehabilitation for stroke patients with severe motor impairments is burdensome and demanding because most of the current rehabilitation options require some volitional movement to train the affected side. However, research has shown that survivors of severe stroke may receive modest benefits from action observation, virtual reality (VR), and brain-computer interfaces (BCIs). These approaches have shown some success in strengthening key motor pathways thought to support motor recovery after stroke. The purpose of this study was to combine the principles of action observation, VR, and BCI in a platform called REINVENT and assess its effects on four chronic stroke patients across different levels of motor impairment. REINVENT acquires post-stroke EEG signals that indicate an attempt to move and drives the movement of a virtual avatar arm, allowing patient-driven action observation neurofeedback in VR. In addition, synchronous electromyography (EMG) data were also captured to monitor overt muscle activity. Our results show that this EEG-based BCI can be used by stroke survivors across a wide range of motor disabilities. Finally, individual results suggest that patients with more severe motor impairments benefit the most from EEG-based neurofeedback, while patients with more mild impairments may benefit more from EMG-based feedback, harnessing existing sensorimotor pathways. Future research is needed to confirm these findings in a larger and more diverse population.

The solution to a problem might manifest itself as a burst of unexpected, unpredictable clarity. Such Eureka! events, or Insight moments, are among the most fascinating mysteries of human cognition, whose neurophysiological substrate seems to include a role for oscillatory activity within the α and γ bands in the right parietal and temporal brain regions. We tested this hypothesis on thirty-one healthy participants using transcranial Alternating Current Stimulation (tACS) to externally amplify α (10 Hz) and γ (40 Hz) activity in the right parietal and temporal lobes, respectively. During γ-tACS over the right temporal lobe, we observed an increase in accuracy on a verbal insight task. Furthermore, electroencephalography (EEG) data revealed an increase in γ spectral power over bilateral temporal lobes after stimulation. Additionally, resting-state functional MRI data acquired before the stimulation session suggested a correlation between behavioral response to right temporal lobe tACS and functional connectivity of bilateral temporal lobes, in line with the bilateral increase in γ band revealed by EEG. Overall, results suggest the possibility of enhancing the probability of generating Eureka! moments in humans by means of frequency-specific noninvasive brain stimulation.

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.

Keywords: stroke, mindfulness, spasticity, rehabilitation

The link between the local structure of the primary motor cortex and motor function has been well documented. However, motor function relies on a network of interconnected brain regions and the link between the structural properties characterizing these distributed brain networks and motor function remains poorly understood. Here, we examined whether distributed patterns of brain structure, extending beyond the primary motor cortex can help classify two forms of motor function: corticospinal excitability and intracortical inhibition. To this effect, we recorded high-resolution structural magnetic resonance imaging scans in 25 healthy volunteers. To measure corticospinal excitability and inhibition in the same volunteers, we recorded motor evoked potentials (MEPs) elicited by single-pulse transcranial magnetic stimulation and short-interval intracortical inhibition (SICI) in a separate session. Support vector machine (SVM) pattern classification was used to identify distributed multi-voxel gray-matter areas, which distinguished subjects who had lower and higher MEPs and SICIs. We found that MEP and SICI classification could be predicted based on a widely distributed, largely non-overlapping pattern of voxels in frontal, parietal, temporal, occipital, and cerebellar regions. Thus, structural properties distributed over the brain beyond the primary motor cortex relate to motor function.

Keywords
Cortical excitability; Cortical inhibition; TMS; MRI

We routinely make fine motor adjustments to maintain optimal motor performance. These adaptations have been attributed to both implicit, error-based mechanisms, and explicit, strategy-based mechanisms. However, little is known about the neural basis of implicit versus explicit learning. Here, we aimed to use anodal transcranial direct current stimulation (tDCS) to probe the relationship between different brain regions and learning mechanisms during a visuomotor adaptation task in humans. We hypothesized that anodal tDCS over the cerebellum (CB) should increase implicit learning while anodal tDCS over the dorsolateral prefrontal cortex (dlPFC), a region associated with higher-level cognition, should facilitate explicit learning. Using a horizontal visuomotor adaptation task that measures explicit/implicit contributions to learning (Taylor et al., 2014), we found that dlPFC stimulation significantly improved performance compared to the other groups, and weakly increased explicit learning. However, CB stimulation had no effects on either target error or implicit learning. Previous work showed variable CB stimulation effects only on a vertical visuomotor adaptation task (Jalali et al., 2017), so in Experiment 2, we conducted the same study using a vertical context to see if we could find effects of CB stimulation. We found only weak effects of CB stimulation on target error and implicit learning, and now the dlPFC effect did not replicate. To resolve this discrepancy, in Experiment 3, we examined the effect of context (vertical vs. horizontal) on implicit and explicit contributions and found that individuals performed significantly worse and used greater implicit learning in the vertical screen condition compared to the horizontal screen condition. Across all experiments, however, there was high inter-individual variability, with strong influences of a few individuals, suggesting that these effects are not consistent across individuals. Overall, this work provides preliminary support for the idea that different neural regions can be engaged to improve visuomotor adaptation, but shows that each region’s effects are highly context-dependent and not clearly dissociable from one another. This holds implications especially in neurorehabilitation, where an intact neural region could be engaged to potentially compensate if another region is impaired. Future work should examine factors influencing interindividual variability during these processes.

Keywords: Visuomotor adaptation-learning, tDCS, Explicit learning, implicit learning, Cerebellum, dorsolateral prefrontal cortex (DLPFC), Context-dependent learning

Background. Increased activity in the lesioned hemisphere has been related to improved poststroke motor recovery. However, the role of the dominant hemisphere—and its relationship to activity in the lesioned hemisphere—has not been widely explored.

Objective. Here, we examined whether the dominant hemisphere drives the lateralization of brain activity after stroke and whether this changes based on if the lesioned hemisphere is the dominant hemisphere or not.

Methods. We used fMRI to compare cortical motor activity in the action observation network (AON), motor-related regions that are active both during the observation and execution of an action, in 36 left hemisphere dominant individuals. Twelve individuals had nondominant, right hemisphere stroke, twelve had dominant, left-hemisphere stroke, and twelve were healthy age-matched controls. We previously found that individuals with left dominant stroke show greater ipsilesional activity during action observation. Here, we examined if individuals with nondominant, right hemisphere stroke also showed greater lateralized activity in the ipsilesional, right hemisphere or in the dominant, left hemisphere and compared these results with those of individuals with dominant, left hemisphere stroke.

Results. We found that individuals with right hemisphere stroke showed greater activity in the dominant, left hemisphere, rather than the ipsilesional, right hemisphere. This left-lateralized pattern matched that of individuals with left, dominant hemisphere stroke, and both stroke groups differed from the age-matched control group.

Conclusions. These findings suggest that action observation is lateralized to the dominant, rather than ipsilesional, hemisphere, which may reflect an interaction between the lesioned hemisphere and the dominant hemisphere in driving lateralization of brain activity after stroke. Hemispheric dominance and laterality should be carefully considered when characterizing poststroke neural activity.

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

Non-invasive brain stimulation (NIBS) has been widely explored as a way to safely modulate brain activity and alter human performance for nearly three decades. Research using NIBS has grown exponentially within the last decade with promising results across a variety of clinical and healthy populations. However, recent work has shown high inter-individual variability and a lack of reproducibility of previous results. Here, we conducted a small preliminary study to explore the effects of three of the most commonly used excitatory NIBS paradigms over the primary motor cortex (M1) on motor learning (Sequential Visuomotor Isometric Pinch Force Tracking Task) and secondarily relate changes in motor learning to changes in cortical excitability (MEP amplitude and SICI). We compared anodal transcranial direct current stimulation (tDCS), paired associative stimulation (PAS₂₅), and intermittent theta burst stimulation (iTBS), along with a sham tDCS control condition. Stimulation was applied prior to motor learning. Participants (n = 28) were randomized into one of the four groups and were trained on a skilled motor task. Motor learning was measured immediately after training (online), 1 day after training (consolidation), and 1 week after training (retention). We did not find consistent differential effects on motor learning or cortical excitability across groups. Within the boundaries of our small sample sizes, we then assessed effect sizes across the NIBS groups that could help power future studies. These results, which require replication with larger samples, are consistent with previous reports of small and variable effect sizes of these interventions on motor learning.

Lesion analyses are critical for drawing insights about stroke injury and recovery, and their importance is underscored by growing efforts to collect and combine stroke neuroimaging data across research sites. However, while there are numerous processing pipelines for neuroimaging data in general, few can be smoothly applied to stroke data due to complications analyzing the lesioned region. As researchers often use their own tools or manual methods for stroke MRI analysis, this could lead to greater errors and difficulty replicating findings over time and across sites. Rigorous analysis protocols and quality control pipelines are thus urgently needed for stroke neuroimaging. To this end, we created the Pipeline for Analyzing Lesions after Stroke (PALS; DOI: https://doi.org/10.5281/zenodo.1266980), a scalable and user-friendly toolbox to facilitate and ensure quality in stroke research using T1-weighted MRIs. The PALS toolbox offers four modules integrated into a single pipeline, including (1) reorientation to radiological convention, (2) lesion correction for healthy white matter voxels, (3) lesion load calculation, and (4) visual quality control. In the present paper, we discuss each module and provide validation and example cases of our toolbox using multi-site data. Importantly, we also show that lesion correction with PALS significantly improves similarity between manual lesion segmentations by different tracers (z=3.43, p=0.0018). PALS can be found online at https://github.com/npnl/PALS. Future work will expand the PALS capabilities to include multimodal stroke imaging. We hope PALS will be a useful tool for the stroke neuroimaging community and foster new clinical insights.

Insight processes that peak in “unpredictable moments of exceptional thinking” are often referred to as Aha! or Eureka moments. During insight, connections between previously unrelated concepts are made and new patterns arise at the perceptual level while new solutions to apparently insolvable problems suddenly emerge to consciousness. Given its unpredictable nature, the definition, and behavioral and neurophysiological measurement of insight problem solving represent a major challenge in contemporary cognitive neuroscience. Numerous attempts have been made, yet results show limited consistency across experimental approaches. Here we provide a comprehensive overview of available neuroscience of insight, including: i) a discussion about the theoretical definition of insight and an overview of the most widely accepted theoretical models, including those debating its relationship with creativity and intelligence; ii) an overview of available tasks used to investigate insight; iii) an ad-hoc quantitative meta-analysis of functional magnetic resonance imaging studies investigating the Eureka moment, using activation likelihood estimation maps; iv) a review of electroencephalographic evidence in the time and frequency domains, as well as v) an overview of the application of non-invasive brain stimulation techniques to causally assess the neurobiological basis of insight as well as enhance insight-related cognition.

Keywords
Insight; Eureka; Aha; Cognition; fMRI; EEG; ERPs; Non-invasive brain stimulation; Neuroenhancement; NIBS; Creativity

Immersive, head-mounted virtual reality (HMD-VR) provides a unique opportunity to understand how changes in sensory environments affect motor learning. However, potential differences in mechanisms of motor learning and adaptation in HMD-VR versus a conventional training (CT) environment have not been extensively explored. Here, we investigated whether adaptation on a visuomotor rotation task in HMD-VR yields similar adaptation effects in CT and whether these effects are achieved through similar mechanisms. Specifically, recent work has shown that visuomotor adaptation may occur via both an implicit, error-based internal model and a more cognitive, explicit strategic component. We sought to measure both overall adaptation and balance between implicit and explicit mechanisms in HMD-VR versus CT. Twenty-four healthy individuals were placed in either HMD-VR or CT and trained on an identical visuomotor adaptation task that measured both implicit and explicit components. Our results showed that the overall timecourse of adaption was similar in both HMD-VR and CT. However, HMD-VR participants utilized a greater cognitive strategy than CT, while CT participants engaged in greater implicit learning. These results suggest that while both conditions produce similar results in overall adaptation, the mechanisms by which visuomotor adaption occurs in HMD-VR appear to be more reliant on cognitive strategies.

Immersive, head-mounted virtual reality (HMD-VR) can be a potentially useful tool for motor rehabilitation. However, it is unclear whether the motor skills learned in HMD-VR transfer to the non-virtual world and vice-versa. Here we used a well-established test of skilled motor learning, the Sequential Visual Isometric Pinch Task (SVIPT), to train individuals in either an HMD-VR or conventional training (CT) environment. Participants were then tested in both environments. Our results show that participants who train in the CT environment have an improvement in motor performance when they transfer to the HMD-VR environment. In contrast, participants who train in the HMD-VR environment show a decrease in skill level when transferring to the CT environment. This has implications for how training in HMD-VR and CT may affect performance in different environments.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method to modulate the local field potential in neural tissue and consequently, cortical excitability. As tDCS is relatively portable, affordable, and accessible, the applications of tDCS to probe brain-behavior connections have rapidly increased in the last 10 years. One of the most promising applications is the use of tDCS to modulate excitability in the motor cortex after stroke and promote motor recovery. However, the results of clinical studies implementing tDCS to modulate motor excitability have been highly variable, with some studies demonstrating that as many as 50% or more of patients fail to show a response to stimulation. Much effort has therefore been dedicated to understand the sources of variability affecting tDCS efficacy. Possible suspects include the placement of the electrodes, task parameters during stimulation, dosing (current amplitude, duration of stimulation, frequency of stimulation), individual states (e.g., anxiety, motivation, attention), and more. In this review, we first briefly review potential sources of variability specific to stroke motor recovery following tDCS. We then examine how the anatomical variability in tDCS placement [e.g., neural target(s) and montages employed] may alter the neuromodulatory effects that tDCS exerts on the post-stroke motor system.

Modifying sensory aspects of the learning environment can influence motor behavior. Although the effects of sensory manipulations on motor behavior have been widely studied, there still remains a great deal of variability across the field in terms of how sensory information has been manipulated or applied. Here, the authors briefly review and integrate the literature from each sensory modality to gain a better understanding of how sensory manipulations can best be used to enhance motor behavior. Then, they discuss 2 emerging themes from this literature that are important for translating sensory manipulation research into effective interventions. Finally, the authors provide future research directions that may lead to enhanced efficacy of sensory manipulations for motor learning and rehabilitation.

Integration of research in the fields of neural control of movement and biomechanics (collectively referred to as movement science) with the field of human occupation directly benefits both areas of study. Specifically, incorporating many of the quantitative scientific methods and analyses employed in movement science can help accelerate the development of rehabilitation-relevant research in occupational therapy (OT) and occupational science (OS). Reciprocally, OT and OS, which focus on the performance of everyday activities (occupations) to promote health and well-being, provide theoretical frameworks to guide research on the performance of actions in the context of social, psychological, and environmental factors. Given both fields' mutual interest in the study of movement as it relates to health and disease, the authors posit that combining OS and OT theories and principles with the theories and methods in movement science may lead to new, impactful, and clinically relevant knowledge. The first step is to ensure that individuals with OS or OT backgrounds are academically prepared to pursue advanced study in movement science. In this article, the authors propose 2 strategies to address this need.

Keywords: occupational therapy, occupational science, movement science, neural control of movement, neuroscience

There are few effective treatments for rehabilitation of severe motor impairment after stroke. We developed a novel closed-loop neurofeedback system called REINVENT to promote motor recovery in this population. REINVENT (Rehabilitation Environment using the Integration of Neuromuscular-based Virtual Enhancements for Neural Training) harnesses recent advances in neuroscience, wearable sensors, and virtual technology and integrates low-cost electroencephalography (EEG) and electromyography (EMG) sensors with feedback in a head-mounted virtual reality display (VR) to provide neurofeedback when an individual's neuromuscular signals indicate movement attempt, even in the absence of actual movement. Here we describe the REINVENT prototype and provide evidence of the feasibility and safety of using REINVENT with older adults.

Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.

The laterality index (LI) is one way to assess hemispheric dominance in a variety of tasks, such as language, cognitive functions, and changes in laterality in clinical populations, such as after stroke. In stroke neuroimaging, however, an optimal method of calculating the LI remains controversial, largely due to lesion variability in post-stroke brains.

Two main methods of calculating LI have evolved in neuroimaging literature. The first, more traditional approach counts the number of active voxels in a given region of interest (ROI) for each hemisphere. This method has been criticized for its inability to account for differences in signal intensity. Hence, a second approach calculates laterality based on the percent signal change within a given region; however, this method also has problems, such as difficulty handling negative values.

A laterality toolbox that addresses some of these issues has been implemented in the statistical neuroimaging analysis package SPM, which provides users with options of using either method, along with more advanced statistical tests for robust LI calculations [2] No such toolbox is yet available for FSL. Therefore, we developed a series of scripts to calculate LI in FSL using both voxel count and percent signal change methods. However, in the interest of space, here we present only results from the more robust method of the two (voxel count method).

A vast number of clinical disorders may involve changes in brain structure that are correlated with cognitive function and behavior (e.g., depression, schizophrenia, stroke, etc.). Reliably understanding the relationship between specific brain structures and relevant behaviors in worldwide clinical populations could dramatically improve healthcare decisions around the world. For instance, if a reliable relationship between brain structure after stroke and functional motor ability was established, brain imaging could be used to predict prognosis/recovery potential for individual patients. However, high heterogeneity in clinical populations in both individual neuroanatomy and behavioral outcomes make it difficult to develop accurate models of these potentially subtle relationships.

Large neuroimaging studies (n > 10,000) would provide unprecedented power to successfully relate clinical neuroanatomy changes with behavioral measures. While these sample sizes might be difficult for any one individual to collect, the ENIGMA Center for Worldwide-Medicine, Imaging, and Genomics has successfully pioneered meta-and mega-analytic methods to accomplish this task. ENIGMA [http://enigma.ini.usc.edu] brings together a global alliance of over 500 international researchers from over 35 countries to pool together neuroimaging data on different disease states in hopes of discovering critical brain-behavior relationships. Individual investigators with relevant data run ENIGMA analysis protocols on their own data and send back an output folder containing the analysis results to be combined with data from other sites for a meta-analysis. In this way, large sample sizes can be acquired without the hassle of large-scale data transfers or actual neuroimaging data sharing.

ENIGMA protocols were initially developed to harmonize processing methods of imaging researchers around the world and they require a moderate level of familiarity with several programming languages and environments. However, ENIGMA’s recent success has attracted greater interest in collaborative neuroimaging and protocols must be adjusted to allow for all levels of experience, as, the success of this approach depends on individual collaborators running these ENIGMA protocols on their data. Here, we worked on simplifying these protocols so even a novice programmer could use them. In this way, we hope to expand the feasibility of collecting critical clinical data from collaborators who may have less experience with neuroim-aging techniques.

Background. Two thirds of stroke survivors experience motor impairment resulting in long-term disability. The anatomical substrate is often the disruption of cortico-subcortical pathways. It has been proposed that reestablishment of cortico-subcortical communication relates to functional recovery.

Objective. In this study, we applied a novel training protocol to augment ipsilesional cortico-subcortical connectivity after stroke. Chronic stroke patients with severe motor impairment were provided online feedback of blood-oxygenation level dependent signal connectivity between cortical and subcortical regions critical for motor function using real-time functional magnetic resonance imaging neurofeedback.

Results. In this proof of principle study, 3 out of 4 patients learned to voluntarily modulate cortico-subcortical connectivity as intended.

Conclusions. Our results document for the first time the feasibility and safety for patients with chronic stroke and severe motor impairment to self-regulate and augment ipsilesional cortico-subcortical connectivity through neurofeedback using real-time functional magnetic resonance imaging.

Redundancy is an important feature of the motor system, as abundant degrees of freedom are prominent at every level of organization across the central and peripheral nervous systems, and musculoskeletal system. This basic feature results in a system that is both flexible and robust, and which can be sustainably adapted through plasticity mechanisms in response to intrinsic organismal changes and dynamic environments. While much early work of motor system organization has focused on synaptic-based plasticity processes that are driven via experience, recent investigations of neuron-glia interactions, epigenetic mechanisms and large-scale network dynamics have revealed a plethora of plasticity mechanisms that support motor system organization across multiple, overlapping spatial and temporal scales. Furthermore, an important role of these mechanisms is the regulation of intrinsic variability. Here, we review several of these mechanisms and discuss their potential role in neurorehabilitation.

Non-invasive brain stimulation (NIBS) may enhance motor recovery after neurological injury through the causal induction of plasticity processes. Neurological injury, such as stroke, often results in serious long-term physical disabilities, and despite intensive therapy, a large majority of brain injury survivors fail to regain full motor function. Emerging research suggests that NIBS techniques, such as transcranial magnetic (TMS) and direct current (tDCS) stimulation, in association with customarily used neurorehabilitative treatments, may enhance motor recovery. This paper provides a general review on TMS and tDCS paradigms, the mechanisms by which they operate and the stimulation techniques used in neurorehabilitation, specifically stroke. TMS and tDCS influence regional neural activity underlying the stimulation location and also distant interconnected network activity throughout the brain. We discuss recent studies that document NIBS effects on global brain activity measured with various neuroimaging techniques, which help to characterize better strategies for more accurate NIBS stimulation. These rapidly growing areas of inquiry may hold potential for improving the effectiveness of NIBS-based interventions for clinical rehabilitation.

Brain-neural machine interfaces (BNMIs) are systems that allow a user to control an artificial device, such as a computer cursor or a robotic limb, through imagined movements that are measured as neural activity. They provide the potential to restore mobility for those with motor deficiencies caused by stroke, spinal cord injury, or limb amputations. Such systems would have been considered a topic of science fiction a few decades ago but are now being increasingly developed in both research and industry. Workers in this area are charged with fabricating BNMIs that are safe, effective, easy to use, and affordable for clinical populations.

Our experiences with others affect how we perceive their actions. In particular, activity in bilateral premotor and parietal cortices during action observation, collectively known as the action observation network (AON), is modulated by one's expertise with the observed actions or individuals. However, conflicting reports suggest that AON activity is greatest both for familiar and unfamiliar actions. The current study examines the effects of different types and amounts of experience (e.g., visual, interpersonal, personal) on AON activation. fMRI was used to scan 16 healthy participants without prior experience with individuals with amputations (novices), 11 experienced occupational therapists (OTs) who had varying amounts of experience with individuals with amputations, and one individual born with below-elbow residual limbs (participant CJ), as they viewed video clips of goal-matched actions performed by an individual with residual limbs and by an individual with hands. Participants were given increased visual exposure to actions performed by both effectors midway through the scanning procedure. Novices demonstrated a large AON response to the initial viewing of an individual with residual limbs compared to one with hands, but this signal was attenuated after they received visual exposure to both effectors. In contrast, OTs, who had moderate familiarity with residual limbs, demonstrated a lower AON response upon initial viewing-similar to novices after they received visual exposure. At the other extreme, CJ, who has extreme familiarity with residual limbs both visually and motorically, shows a largely increased left-lateralized AON response, exceeding that of novices and experienced OTs, when viewing the residual limb compared to hand actions. These results suggest that a nuanced model of AON engagement is needed to explain how cases of both extreme experience (CJ) and extreme novelty (novices) can result in the greatest AON activity.

Background and Purpose. Much recent interest surrounds the use of action observation, which is observing another individual performing a motor task, in stroke rehabilitation, to promote motor recovery by engaging similar brain regions to action execution. This may be especially useful in individuals with limited mobility. Here, we assess how cortical motor activity during action observation is affected by stroke and by stroke-related motor deficits.

Methods. We used functional MRI to compare brain activity during right and left hand action observation in right-handed nondisabled participants and participants who were right-handed before left hemisphere stroke. All participants performed the same actions after their functional MRI.

Results. Nondisabled participants show greater bilateral cortical motor activity when observing actions made using the left hand, whereas participants with stroke show greater ipsilesional cortical motor activity when observing actions made using the right (paretic) hand (P<0.05; corrected). For both groups, action processing is modulated by motor capability: cortical motor activity is greater when observing the hand with lower motor scores (P<0.05; corrected). Furthermore, for stroke, the extent of ipsilesional activity correlates with lesion volume (P=0.049), in a pattern that suggests adaptive plasticity.

Conclusions. We found that action observation activates specific motor plans in damaged motor circuits after stroke, and this activity is related to motor capability to perform the same actions. Cortical motor activity during action observation may be relevant to motor learning, and to motor relearning in stroke rehabilitation.

Although creativity has been called the most important of all human resources, its neural basis is still unclear. In the current study, we used fMRI to measure neural activity in participants solving a visuospatial creativity problem that involves divergent thinking and has been considered a canonical right hemisphere task. As hypothesized, both the visual creativity task and the control task as compared to rest activated a variety of areas including the posterior parietal cortex bilaterally and motor regions, which are known to be involved in visuospatial rotation of objects. However, directly comparing the two tasks indicated that the creative task more strongly activated left hemisphere regions including the posterior parietal cortex, the premotor cortex, dorsolateral prefrontal cortex (DLPFC) and the medial PFC. These results demonstrate that even in a task that is specialized to the right hemisphere, robust parallel activity in the left hemisphere supports creative processing. Furthermore, the results support the notion that higher motor planning may be a general component of creative improvisation and that such goal-directed planning of novel solutions may be organized top-down by the left DLPFC and by working memory processing in the medial prefrontal cortex.

Observing actions performed by others engages one's own sensorimotor regions, typically with greater activity for actions within one's own motor abilities or for which one has prior experience. However, it is unclear how experience modulates the neural response during the observation of impossible actions, beyond one's own abilities. Using fMRI, we scanned typically-developed participants as they observed actions performed by a novel biological effector (the residual limb of a woman born without arms) and a familiar biological effector (a hand). Participants initially demonstrated greater activity in the bilateral inferior and superior parietal cortices when observing actions made by the residual limb compared to the hand, with more empathic participants activating the right inferior parietal lobule, corresponding to the posterior component of the action observation network, more strongly. Activity in the parietal regions may indicate matching the kinematics of a novel effector to one's own existing sensorimotor system, a process that may be more active in more empathic individuals. Participants then received extended visual exposure to each effector, after which they showed little difference between activation in response to residual limb compared to hand actions, only in the right superior parietal lobule. This suggests that visual experience may attenuate the difference between how residual limb and hand actions are represented using one's own body representations, allowing us to flexibly map physically different others onto our own body representations.

The human putative mirror neuron system (MNS) is a key network hypothesized to play a role in many social cognitive and language-related abilities. This chapter begins by discussing basic findings on the mirror system, which encompasses motor-related brain regions that fire when an individual both performs and observes others perform actions. We then discuss how these shared action/observation regions are thought to underlie one’s ability to understand others via simulation of their actions onto one’s own motor representations. Finally, we conclude by noting how the frontal mirror region coincides with Broca’s area, a language region in the brain, leading some to propose that the MNS may also play a role in language and gesture abilities.

Occupational therapy has traditionally championed the use of meaningful occupations in rehabilitation. Emerging research in neuroscience about the putative human mirror neuron system may provide empirical support for the use of occupations to improve outcomes in rehabilitation. This article provides an interdisciplinary framework for understanding the mirror neuron system — a network of motor-related brain regions activated during the production and perception of the same actions — in relation to occupational therapy. The authors present an overview of recent research on the mirror neuron system, highlighting features that are relevant to clinical practice in occupational therapy. They also discuss the potential use of the mirror neuron system in motor rehabilitation and how it may be deficient in populations served by occupational therapy, including individuals with dyspraxia, multisensory integration disorders, and social interaction difficulties. Methods are proposed for occupational therapy to translate these neuroscience findings on the mirror neuron system into clinical applications and the authors suggest that future research in neuroscience would benefit from integrating the occupational therapy perspective.

How do we understand and empathize with individuals whose bodies are drastically different from our own? We investigated the neural processes by which an individual with a radically different body, a congenital amputee who is born without limbs, engages her own sensory-motor representations as a means to understand other people’s body actions or emotional states. Our results support the prediction that when the goal of the action is possible for the observer, one’s own motor regions are involved in processing action observation, just as when individuals viewed those similar to themselves. However, when the observed actions are not possible, mentalizing mechanisms, relying on a different set of neural structures, are additionally recruited to process the actions. Furthermore, our results indicate that when individuals view others experiencing pain in body parts that they have, the insula and somatosensory cortices are activated, consistent with previous reports. However, when an individual views others experiencing pain in body parts that she does not have, the insula and secondary somatosensory cortices are still active, but the primary somatosensory cortices are not. These results provide a novel understanding for how we understand and empathize with individuals who drastically differ from the self.

Recent research suggests that the inference of others' intentions from their observed actions is supported by two neural systems that perform complementary roles. The human putative mirror neuron system (pMNS) is thought to support automatic motor simulations of observed actions, with increased activity for previously experienced actions, whereas the mentalizing system provides reflective, non-intuitive reasoning of others' perspectives, particularly in the absence of prior experience. In the current fMRI study, we show how motor familiarity with an action and perceptual familiarity with the race of an actor uniquely modulate these two systems. Chinese participants were asked to infer the intentions of actors performing symbolic gestures, an important form of non-verbal communication that has been shown to activate both mentalizing and mirror neuron regions. Stimuli were manipulated along two dimensions: (1) actor's race (Caucasian vs. Chinese actors) and (2) participants' level of experience with the gestures (familiar or unfamiliar). We found that observing all gestures compared to observing still images was associated with increased activity in key regions of both the pMNS and mentalizing systems. In addition, observations of one's same race generated greater activity in the posterior pMNS-related regions and the insula than observations of a different race. Surprisingly, however, familiar gestures more strongly activated regions associated with mentalizing, while unfamiliar gestures more strongly activated the posterior region of the pMNS, a finding that is contrary to prior literature and demonstrates the powerful modulatory effects of both motor and perceptual familiarity on pMNS and mentalizing regions when asked to infer the intentions of intransitive gestures.

Language is a dominant part of our daily activities, playing a significant role in narrating our actions and mediating our interactions with one another. In this article, we examine emerging neuroscientific evidence that language is biologically linked to action and suggest that studying language from an occupation-based perspective can contribute a rich dimension of analysis for occupational science. We briefly review several of the ways in which language is currently being incorporated into the study of occupations and conclude by suggesting future directions for an occupation-based study of language.

Body Integrity Identity Disorder (BIID) is a condition in which people generally desire amputation of healthy limbs but can also desire paralysis, blindness, or other disabilities. The current study explored the demographics and experiences of individuals with this condition with specific attention to perceived physical differences of the affected limbs. Participants were recruited from three BIID-focused Internet forums to participate in two online surveys. There were 97 unique participants total, the largest sample size of individuals with BIID to date. It was found that individuals with BIID differ from the normal population in handedness and sexual orientation. Participants who reported differences in sensation in the affected limb(s) were also significantly more likely to report difficulty in learning/imitation, difference of feeling during use, and difference in performance of the affected limb(s). Furthermore it was found that individuals who achieved amputation almost always experienced phantom limbs and that many participants choose to use prosthetics post-amputation. These results shed light on the many facets of BIID and the perception of self and body schema. Future studies using neuroimaging may be able to better understand the neural bases of BIID.

Keywords: Body Integrity Identity Disorder, superior parietal lobule, forward model, inverse model, apotemnophilia

Human adults typically respond faster to their own face than to the faces of others. However, in Chinese participants, this self-face advantage is lost in the presence of one's supervisor, and they respond faster to their supervisor's face than to their own. While this “boss effect” suggests a strong modulation of self-processing in the presence of influential social superiors, the current study examined whether this effect was true across cultures. Given the wealth of literature on cultural differences between collectivist, interdependent versus individualistic, independent self-construals, we hypothesized that the boss effect might be weaker in independent than interdependent cultures. Twenty European American college students were asked to identify orientations of their own face or their supervisors' face. We found that European Americans, unlike Chinese participants, did not show a “boss effect” and maintained the self-face advantage even in the presence of their supervisor's face. Interestingly, however, their self-face advantage decreased as their ratings of their boss's perceived social status increased, suggesting that self-processing in Americans is influenced more by one's social status than by one's hierarchical position as a social superior. In addition, when their boss's face was presented with a labmate's face, American participants responded faster to the boss's face, indicating that the boss may represent general social dominance rather than a direct negative threat to oneself, in more independent cultures. Altogether, these results demonstrate a strong cultural modulation of self-processing in social contexts and suggest that the very concept of social positions, such as a boss, may hold markedly different meanings to the self across Western and East Asian cultures.