A Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity.

Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact accuracy and robustness of the measurements. A deep learning model based on the U-Net architecture is proposed to recover SCG signals contaminated by motion noise induced by walking. The model performance was evaluated through qualitative visualization, as well as quantitative analyses. Quantitative analyses included distance-based comparisons before and after applying our model. Analyses also included assessments of the model's efficacy in improving the performance of downstream tasks related to health parameter estimation during walking. Experimental findings revealed that the denoising model improved similarity to clean signals by approximately 90%. The performance of the model in enhancing heart rate estimation demonstrated a mean absolute error of 1.21 BPM and a root-mean-squared error (RMSE) of 1.97 BPM during walking after denoising with 9.16 BPM and 10.38 BPM improvements, respectively, compared to without denoising. Furthermore, the RMSEs of aortic opening and aortic closing time estimation after denoising for one dataset with catheter ground truth were 7.29 ms and 19.71 ms during walking, respectively, with 50.33 ms and 51.91 ms RMSE improvements compared to without denoising. And for another dataset with ICG-derived PEP ground truth, the RMSE of aortic opening time estimation after denoising was 10.21 ms during walking, with 38.74 ms RMSE improvement compared to without denoising. The proposed model attenuates motion noise from corrupted SCG signals while preserving cardiac information. This development paves the way for improved ambulatory cardiac health monitoring using wearable accelerometers during daily activities.

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.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework.

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10-3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10-3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

Leveraging Physiological Markers to Quantify the Transient Effects of Traumatic Stress and Non-Invasive Neuromodulation.

Millions around the world suffer from traumatic stress (stress caused by traumatic memories). Transcutaneous cervical vagus nerve stimulation (tcVNS) has been shown to counteract physiological changes associated with traumatic stress. However, little is known regarding the approximate timecourse of tcVNS effects. This knowledge of how quickly tcVNS takes effect is needed to optimize closed-loop tcVNS systems that can mitigate traumatic stress in a timely manner. To address this gap, we studied N=26 participants with history of prior trauma. Participants wore electrocardiogram, photoplethysmogram, seismocardiogram, and respiratory effort sensors throughout a double-blind protocol involving traumatic stress and active tcVNS (n=12) or sham stimulation (n=14). From the physiological signals, we extracted cardiovascular and respiratory markers and studied their dynamics during the traumatic stress and stimulation conditions. We decoupled the short-term transient responses from longer-term cumulative changes by centering each condition's response with respect to data immediately prior to the condition. We thereby elucidate a diverse set of transient physiological responses to tcVNS and traumatic stress. These responses demonstrate that tcVNS-induced changes occur within seconds and have the potential to reduce acute physiological manifestations of traumatic stress.Clinical relevance- Traumatic stress can overpower an individual within seconds and often occurs outside the clinic. This analysis focuses on transient physiological responses to traumatic memories and tcVNS captured using multimodal physiological sensing. We demonstrate that tcVNS-induced changes occur within seconds and have the potential to mitigate some of the short-term effects of traumatic stress.

Early Prediction of Impending Exertional Heat Stroke With Wearable Multimodal Sensing and Anomaly Detection.

We employed wearable multimodal sensing (heart rate and triaxial accelerometry) with machine learning to enable early prediction of impending exertional heat stroke (EHS). US Army Rangers and Combat Engineers (N = 2,102) were instrumented while participating in rigorous 7-mile and 12-mile loaded rucksack timed marches. There were three EHS cases, and data from 478 Rangers were analyzed for model building and controls. The data-driven machine learning approach incorporated estimates of physiological strain (heart rate) and physical stress (estimated metabolic rate) trajectories, followed by reconstruction to obtain compressed representations which then fed into anomaly detection for EHS prediction. Impending EHS was predicted from 33 to 69 min before collapse. These findings demonstrate that low dimensional physiological stress to strain patterns with machine learning anomaly detection enables early prediction of impending EHS which will allow interventions that minimize or avoid pathophysiological sequelae. We describe how our approach can be expanded to other physical activities and enhanced with novel sensors.

Intravascular Lymphoma as a Cause of Recurrent Strokes - Case Report and Review of the Literature.

Background: Intravascular lymphoma is an uncommon cause of ischemic strokes. Because of its rarity and atypical pattern, most diagnoses are made post-mortem. Case study: We present a case of a 68-year-old male with multiple cardiovascular risk factors and recent SARS-CoV-2 infection who presented with recurrent strokes. Because of his stroke risk factors, he was initially managed with a sequentially escalating antithrombotic regimen. A malignant process was low on the differential at this point given his lack of systemic symptoms. When he continued to have new strokes despite these measures, including a spinal cord infarct, a broad workup was sent including for hypercoagulable states, vasculitis, and intravascular lymphoma. Eventually, a skin biopsy of a cherry angioma returned positive for lymphoma cells. He was treated with methotrexate followed by chemotherapy and rituximab. Unfortunately, he did not improve and was made comfort measures only by his family. Conclusion: This case illustrates the importance of considering intravascular lymphoma as a potential etiology of recurrent strokes, as early diagnosis and treatment are important for preventing irreversible neurological damage.

Prognostication in Prolonged and Chronic Disorders of Consciousness.

Patients with prolonged disorders of consciousness (DOCs) longer than 28 days may continue to make significant gains and achieve functional recovery. Occasionally, this recovery trajectory may extend past 3 (for nontraumatic etiologies) and 12 months (for traumatic etiologies) into the chronic period. Prognosis is influenced by several factors including state of DOC, etiology, and demographics. There are several testing modalities that may aid prognostication under active investigation including electroencephalography, functional and anatomic magnetic resonance imaging, and event-related potentials. At this time, only one treatment (amantadine) has been routinely recommended to improve functional recovery in prolonged DOC. Given that some patients with prolonged or chronic DOC have the potential to recover both consciousness and functional status, it is important for neurologists experienced in prognostication to remain involved in their care.

Sensitivity to Change and Responsiveness of the Upper Extremity Fugl-Meyer Assessment in Individuals With Moderate to Severe Acute Stroke.

BACKGROUND: The Fugl-Meyer Assessment-Upper Extremity (FMA-UE) is a widely used outcome measure for quantifying motor impairment in stroke recovery. Meaningful change (responsiveness) in the acute to subacute phase of stroke recovery has not been determined. OBJECTIVE: Determine responsiveness and sensitivity to change of the FMA-UE from 1-week to 6-weeks (subacute) after stroke in individuals with moderate to severe arm impairment who received standard clinical care. METHODS: A total of 51 participants with resulting moderate and severe UE hemiparesis after stroke had FMA-UE assessment at baseline (within 2 weeks of stroke) and 6-weeks later. Sensitivity to change was assessed using Glass's delta, standardized response means (SRM), standard error of measure (SEM), and minimal detectable change (MDC). Responsiveness was assessed with the minimal clinically important difference (MCID), estimated using receiver operating characteristic curve analysis with patient-reported global rating of change scales (GROC) and a provider-reported modified Rankin Scale (mRS) as anchors. RESULTS: The MCID estimates were 13, 12, and 9 anchored to the GROC Arm Weakness, GROC Recovery, and mRS. Glass's delta and the SRM revealed large effect sizes, indicating high sensitivity to change, (∆ = 1.24, 95% CI [0.64, 1.82], SRM = 1.10). Results for the SEM and MDC were 2.46 and 6.82, respectively. CONCLUSION: The estimated MCID for the FMA-UE for individuals with moderate to severe motor impairment from 1 to 6-weeks after stroke is 13. These estimates will provide clinical context for FMA-UE change scores by helping to identify the change in upper-extremity motor impairment that is both beyond measurement error and clinically meaningful.

Distinguishing Distinct Neural Systems for Proximal vs Distal Upper Extremity Motor Control After Acute Stroke.

BACKGROUND AND OBJECTIVES: The classic and singular pattern of distal greater than proximal upper extremity motor deficits after acute stroke does not account for the distinct structural and functional organization of circuits for proximal and distal motor control in the healthy CNS. We hypothesized that separate proximal and distal upper extremity clinical syndromes after acute stroke could be distinguished and that patterns of neuroanatomical injury leading to these 2 syndromes would reflect their distinct organization in the intact CNS. METHODS: Proximal and distal components of motor impairment (upper extremity Fugl-Meyer score) and strength (Shoulder Abduction Finger Extension score) were assessed in consecutively recruited patients within 7 days of acute stroke. Partial correlation analysis was used to assess the relationship between proximal and distal motor scores. Functional outcomes including the Box and Blocks Test (BBT), Barthel Index (BI), and modified Rankin scale (mRS) were examined in relation to proximal vs distal motor patterns of deficit. Voxel-based lesion-symptom mapping was used to identify regions of injury associated with proximal vs distal upper extremity motor deficits. RESULTS: A total of 141 consecutive patients (49% female) were assessed 4.0 ± 1.6 (mean ± SD) days after stroke onset. Separate proximal and distal upper extremity motor components were distinguishable after acute stroke (p = 0.002). A pattern of proximal more than distal injury (i.e., relatively preserved distal motor control) was not rare, observed in 23% of acute stroke patients. Patients with relatively preserved distal motor control, even after controlling for total extent of deficit, had better outcomes in the first week and at 90 days poststroke (BBT, ρ = 0.51, p < 0.001; BI, ρ = 0.41, p < 0.001; mRS, ρ = 0.38, p < 0.001). Deficits in proximal motor control were associated with widespread injury to subcortical white and gray matter, while deficits in distal motor control were associated with injury restricted to the posterior aspect of the precentral gyrus, consistent with the organization of proximal vs distal neural circuits in the healthy CNS. DISCUSSION: These results highlight that proximal and distal upper extremity motor systems can be selectively injured by acute stroke, with dissociable deficits and functional consequences. Our findings emphasize how disruption of distinct motor systems can contribute to separable components of poststroke upper extremity hemiparesis.

Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping.

OBJECTIVE: The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. METHODS: Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0 × 3.0 × 3.0 mm3) and quantitative T2 values (1.9 × 1.9 × 3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24 h, n = 23) or acute (24 h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. RESULTS: In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p < 0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p < 0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). CONCLUSION: The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. SIGNIFICANCE: Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.

Neurorecovery after Critical COVID-19 Illness.

With the hundreds of millions of people worldwide who have been, and continue to be, affected by pandemic coronavirus disease (COVID-19) and its chronic sequelae, strategies to improve recovery and rehabilitation from COVID-19 are critical global public health priorities. Neurologic complications have been associated with acute COVID-19 infection, usually in the setting of critical COVID-19 illness. Neurologic complications are also a core feature of the symptom constellation of long COVID and portend poor outcomes. In this article, we review neurologic complications and their mechanisms in critical COVID-19 illness and long COVID. We focus on parallels with neurologic disease associated with non-COVID critical systemic illness. We conclude with a discussion of how recent findings can guide both neurologists working in post-acute neurologic rehabilitation facilities and policy makers who influence neurologic resource allocation.

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.

Synthetic seismocardiogram generation using a transformer-based neural network.

OBJECTIVE: To design and validate a novel deep generative model for seismocardiogram (SCG) dataset augmentation. SCG is a noninvasively acquired cardiomechanical signal used in a wide range of cardivascular monitoring tasks; however, these approaches are limited due to the scarcity of SCG data. METHODS: A deep generative model based on transformer neural networks is proposed to enable SCG dataset augmentation with control over features such as aortic opening (AO), aortic closing (AC), and participant-specific morphology. We compared the generated SCG beats to real human beats using various distribution distance metrics, notably Sliced-Wasserstein Distance (SWD). The benefits of dataset augmentation using the proposed model for other machine learning tasks were also explored. RESULTS: Experimental results showed smaller distribution distances for all metrics between the synthetically generated set of SCG and a test set of human SCG, compared to distances from an animal dataset (1.14× SWD), Gaussian noise (2.5× SWD), or other comparison sets of data. The input and output features also showed minimal error (95% limits of agreement for pre-ejection period [PEP] and left ventricular ejection time [LVET] timings are 0.03 ± 3.81 ms and -0.28 ± 6.08 ms, respectively). Experimental results for data augmentation for a PEP estimation task showed 3.3% accuracy improvement on an average for every 10% augmentation (ratio of synthetic data to real data). CONCLUSION: The model is thus able to generate physiologically diverse, realistic SCG signals with precise control over AO and AC features. This will uniquely enable dataset augmentation for SCG processing and machine learning to overcome data scarcity.

SMS-text messaging for collecting outcome measures after acute stroke.

Introduction: Traditional methods for obtaining outcomes for patients after acute stroke are resource-intensive. This study aimed to examine the feasibility, reliability, cost, and acceptability of collecting outcomes after acute stroke with a short message service (SMS)-text messaging program. Methods: Patients were enrolled in an SMS-text messaging program at acute stroke hospitalization discharge. Participants were prompted to complete assessments including the modified Rankin scale (mRS) and Patient-Reported Outcomes Measurement (PROM) Information System Global-10 at 30, 60, and 90 days postdischarge via SMS-text. Agreement and cost of SMS-text data collection were compared to those obtained from traditional follow-up methods (via phone or in the clinic). Participant satisfaction was surveyed upon program conclusion. Results: Of the 350 patients who agreed to receive SMS texts, 40.5% responded to one or more assessments. Assessment responders were more likely to have English listed as their preferred language (p = 0.009), have a shorter length of hospital stay (p = 0.01), lower NIH stroke scale upon admission (p < 0.001), and be discharged home (p < 0.001) as compared to nonresponders. Weighted Cohen's kappa revealed that the agreement between SMS texting and traditional methods was almost perfect for dichotomized (good vs. poor) (κ = 0.8) and ordinal levels of the mRS score (κ = 0.8). Polychoric correlations revealed a significant association for PROM scores ( ρ = 0.4, p < 0.01 and ρ = 0.4, p < 0.01). A cost equation showed that gathering outcomes via SMS texting would be less costly than phone follow-up for cohorts with more than 181 patients. Nearly all participants (91%) found the program acceptable and not burdensome (94%), and most (53%) felt it was helpful. Poststroke outcome data collection via SMS texting is feasible, reliable, low-cost, and acceptable. Reliability was higher for functional outcomes as compared to PROMs. Conclusions: While further validation is required, our findings suggest that SMS texting is a feasible method for gathering outcomes after stroke at scale to evaluate the efficacy of acute stroke treatments.

Domain-Specific Outcome Measures in Clinical Trials of Therapies Promoting Stroke Recovery: A Suggested Blueprint.

Different deficits recover to different degrees and with different time courses after stroke, indicating that plasticity differs across the brain's neural systems after stroke. To capture these differences, domain-specific outcome measures have received increased attention. Such measures have potential advantages over global outcome scales, which combine recovery across many domains into a single score and so blur the ability to capture individual measures of stroke recovery. Use of a global end point to rate disability can overlook substantial recovery in specific domains, such as motor or language, and may not differentiate between good and poor recovery for specific neurological domains. In light of these points, a blueprint is proposed for using domain-specific outcome measures in stroke recovery trials. Key steps include selecting a domain in the context of preclinical data, picking a domain-specific clinical trial end point, anchoring inclusion criteria to this end point, scoring this end point both before and after treatment, and then pursuing regulatory approval on the basis of the domain-specific results. This blueprint is intended to foster clinical trials that, by using domain-specific end points, are able to demonstrate favorable results in clinical trials of therapies that promote stroke recovery.

Predicting the Onset of Ischemic Stroke With Fast High-Resolution 3D MR Spectroscopic Imaging.

BACKGROUND: Neurometabolite concentrations provide a direct index of infarction progression in stroke. However, their relationship with stroke onset time remains unclear. PURPOSE: To assess the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimate their value in predicting early (<6 hours) vs. late (6-24 hours) hyperacute stroke groups. STUDY TYPE: Cross-sectional cohort. POPULATION: A total of 73 ischemic stroke patients scanned at 1.8-302.5 hours after symptom onset, including 25 patients with follow-up scans. FIELD STRENGTH/SEQUENCE: A 3 T/magnetization-prepared rapid acquisition gradient echo sequence for anatomical imaging, diffusion-weighted imaging and fluid-attenuated inversion recovery imaging for lesion delineation, and 3D MR spectroscopic imaging (MRSI) for neurometabolic mapping. ASSESSMENT: Patients were divided into hyperacute (0-24 hours), acute (24 hours to 1 week), and subacute (1-2 weeks) groups, and into early (<6 hours) and late (6-24 hours) hyperacute groups. Bayesian logistic regression was used to compare classification performance between early and late hyperacute groups by using different combinations of neurometabolites as inputs. STATISTICAL TESTS: Linear mixed effects modeling was applied for group-wise comparisons between NAA, creatine, choline, and lactate. Pearson's correlation analysis was used for neurometabolites vs. time. P < 0.05 was considered statistically significant. RESULTS: Lesional NAA and creatine were significantly lower in subacute than in acute stroke. The main effects of time were shown on NAA (F = 14.321) and creatine (F = 12.261). NAA was significantly lower in late than early hyperacute patients, and was inversely related to time from symptom onset across both groups (r = -0.440). The decrease of NAA and increase of lactate were correlated with lesion volume (NAA: r = -0.472; lactate: r = 0.366) in hyperacute stroke. Discrimination was improved by combining NAA, creatine, and choline signals (area under the curve [AUC] = 0.90). DATA CONCLUSION: High-resolution 3D MRSI effectively assessed the neurometabolite changes and discriminated early and late hyperacute stroke lesions. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

Medication Profiles at Hospital Discharge Predict Poor Outcomes After Acute Ischemic Stroke.

Objectives: To examine the relationship between medications prescribed during the first 6-months post-stroke and functional outcome. Materials and Methods: A retrospective analysis of ischemic stroke survivors enrolled in an observational stroke recovery study from June-2017 to July-2019 was performed. Survivors with favorable outcomes (modified rankin scale (mRS) score 0-2) were compared to those with unfavorable outcomes (mRS ≥3) 6-months after stroke on the following: discharge medication classes prescribed, achievement of recommended targets for blood pressure control, glycemic control, and LDL ≤70 mg/dL, medication changes, medication interactions, and medication list discrepancies. Results: Unfavorable 6-month outcomes occurred in 36/78 (46.2%) of survivors. Survivors with unfavorable outcomes were more likely to be prescribed a central nervous system-acting agent (97.2% vs 71.4%; P = .0022) and/or an anti-hyperglycemic agent (25.0% vs 9.5%; P = .009) at discharge. After adjustment of baseline covariates, total number of medications prescribed was associated with unfavorable 6-month outcomes (OR 1.13, 95% CI 1.0-1.28). Secondary stroke prevention measures were not achieved in a high proportion of survivors. Medication changes during 6-month follow up were common and survivors with unfavorable outcomes were more likely to have clinically significant drug-drug interactions. Discussion: At 6-months, survivors with unfavorable outcomes were found to be prescribed more medications, particularly central nervous system-acting and anti-hyperglycemic agents. There were also more drug-drug interactions in the medications prescribed compared to those with favorable outcomes. Together, these data suggest the need for enhanced screening of high-risk stroke survivors focused on close monitoring of polypharmacy, drug-drug interactions, and adverse events with pharmacotherapy.

Novel Platform for Quantitative Assessment of Functional Object Interactions After Stroke.

Many persons with stroke exhibit upper extremity motor impairments. These impairments often lead to dysfunction and affect performance in activities of daily living, where successful manipulation of objects is essential. Hence, understanding how upper extremity motor deficits manifest in functional interactions with objects is critical for rehabilitation. However, quantifying skill in these tasks has been a challenge. Traditional rehabilitation assessments require highly trained clinicians, are time-consuming, and yield subjective scores. This paper introduces a custom-designed device, the "MAGIC Table", that can record real-time kinematics of persons with stroke during interaction with objects, specifically a 'cup of coffee'. The task and its quantitative assessments were derived from previous basic-science studies. Six participants after stroke and six able-bodied participants moved a 3D-printed cup with a rolling ball inside, representing sloshing coffee, with 3 levels of difficulty. Movements were captured via a high-resolution camera above the table. Conventional kinematic metrics (movement time and smoothness) and novel kinematic metrics accounting for object interaction (risk and predictability) evaluated performance. Expectedly, persons with stroke moved more slowly and less smoothly than able-bodied participants, in both simple reaches and during transport of the cup-and-ball system. However, the more sensitive metric was mutual information, which captured the predictability of interactions, essential in cup transport as shown in previous theoretical research. Predictability sensitively measured differences in performance with increasing levels of difficulty. It also showed the best intraclass consistency, promising sensitive differentiation between different levels of impairment. This study highlights the feasibility of this new device and indicates that examining dynamic object interaction may provide valuable insights into upper extremity function after stroke useful for assessment and rehabilitation.