Recovery of Function After Acquired Neurological Injury.

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.

Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

Pre-Implementation Analysis of the Usability and Acceptability of a Poststroke Complex Telehealth Biofeedback Intervention.

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.

White matter hyperintensities modify relationships between corticospinal tract damage and motor outcomes after stroke.

Motor outcomes after stroke relate to corticospinal tract (CST) damage. Concurrent damage from white matter hyperintensities (WMHs) might impact neurological capacity for recovery after CST injury. Here, we evaluated if WMHs modulate the relationship between CST damage and post-stroke motor impairment outcome. We included 223 individuals from the ENIGMA Stroke Recovery Working Group. CST damage was indexed with weighted CST lesion load (CST-LL). Mixed effects beta-regression models were fit to test the impact of CST-LL, WMH volume, and their interaction on motor impairment. WMH volume related to motor impairment above and beyond CST-LL (ß = 0.178, p = 0.022). We tested if relationships varied by WMH severity (mild vs. moderate-severe). In individuals with mild WMHs, motor impairment related to CST-LL (ß = 0.888, p < 0.001) with a CST-LL x WMH interaction (ß = -0.211, 0.026). In individuals with moderate-severe WMHs, motor impairment related to WMH volume (ß = 0.299, p = 0.044), but did not significantly relate to CST-LL or a CST-LL x WMH interaction. WMH-related damage may be under-recognised in stroke research as a factor contributing to variability in motor outcomes. Our findings emphasize the importance of brain structural reserve in motor outcomes after brain injury.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

Optimizing automated white matter hyperintensity segmentation in individuals with stroke.

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.

White Matter Integrity and Chronic Poststroke Upper Limb Function: An ENIGMA Stroke Recovery Analysis.

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.

Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke.

BACKGROUND AND OBJECTIVES: Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke 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 vs poor outcomes. METHODS: We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the 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 3-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 vs 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 (ß = 0.21; 95% CI 0.04-0.38, p = 0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (ß = -0.28; 95% CI -0.41 to -0.15, p < 0.001) and across multiple domains of function (ß = -0.14; 95% CI -0.22 to -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 (odds ratio 1.04, 95% CI 1.01-1.08, p = 0.004). DISCUSSION: We provide evidence that younger brain age is associated with superior poststroke 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 poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

Acceptability of a Telerehabilitation Biofeedback System Among Stroke Survivors: A Qualitative Analysis.

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.

Corpus Callosal Microstructure Predicts Bimanual Motor Performance in Chronic Stroke Survivors: a Preliminary Cross-Sectional Study.

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.

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.

Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.

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

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

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

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

Observational Study of Neuroimaging Biomarkers of Severe Upper Limb Impairment After Stroke.

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.

A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement.

Low-intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation, applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional MRI (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. The objective of this work was to develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency and reproducibility (ContES checklist). A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists through the International Network of the tES-fMRI Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC on the basis of a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed by using the checklist. Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (i) technological factors, (ii) safety and noise tests and (iii) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article. In conclusion, use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies and increase methodological transparency and reproducibility.

A standardized protocol for manually segmenting stroke lesions on high-resolution T1-weighted MR images.

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.

Corticospinal Tract Lesion Load Originating From Both Ventral Premotor and Primary Motor Cortices Are Associated With Post-stroke Motor Severity.

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.

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.

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.

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

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