David J. Lin’s research while affiliated with MGH Institute for Health Professions and other places

Background Variability in movement is critical for performance under dynamic conditions. Stroke causes focal injury to the motor system, disrupts voluntary motor control, and leads to less smooth and more variable upper extremity movements. Few studies have characterized trial-by-trial variation in upper extremity movement smoothness and its clinical and neuroanatomic correlates in the first week post-stroke. Objective To evaluate trial-by-trial variation in upper extremity movement smoothness during planar reaching and relate it to clinical outcomes and neuroanatomical injury after acute stroke. Methods Twenty-two patients (4.4 ± 1.7 days post-stroke) and 22 able-bodied adults completed a planar center-out reaching task. Smoothness was quantified with spectral arc length (SPARC). Median and interquartile range (IQR, a quantification of trial-by-trial variation) of SPARC values were assessed. Patients completed a clinical assessment battery acutely and at 90 days post-stroke. MRI-derived stroke lesions were analyzed to estimate basal ganglia, motor cortex, and corticospinal tract injury. Intraclass correlation, Spearman’s correlation, and multivariate regression evaluated trial-by-trial variation and its relation to clinical assessments, outcomes, and neuroanatomical injury. Results Post-stroke reaching was less smooth and more variable (larger IQR) compared to able-bodied adults. Variability in post-stroke smoothness was primarily driven by within-subject, trial-by-trial variation. More variable smoothness, even after controlling for median smoothness, related to worse performance on clinical assessments and 90-day outcomes. More variable smoothness related to greater corticospinal tract injury (ρ = 0.537, P = .011), but not to basal ganglia or motor cortex injury. Conclusion Trial-by-trial variation of movement is valuable for understanding sensorimotor control post-stroke and has implications for targeted neurorehabilitation.

Current doses and intensities of post-stroke rehabilitation therapy provided as “usual care” are paltry compared to the magnitudes needed to drive large behaviorally-relevant reductions in neurologic impairments. There is convergent evidence indicating that high dose, high intensity rehabilitation is effective for improving outcomes after stroke with large effect sizes compared to usual care. Here we highlight some of this evidence (focusing on studies of upper extremity motor rehabilitation) and then ask the simple question— why are we not delivering high doses and intensities of rehabilitation in clinical practice? We contend that reasons for lack of implementation of high dose, high intensity rehabilitation have to do with questionable conceptual, ideological, and economic assumptions. In addition, there are practical challenges, which we argue can be overcome with technology. Current practice (we refer primarily to the context of US healthcare) in stroke rehabilitation is itself built on very little evidence, indeed considerably less than the cumulative evidence indicating that high dose, high intensity rehabilitation would be more effective. Our hope is that this Perspective will help persuade multiple stake holders (neurologists, physiatrists, therapists, researchers, patients, policy makers, and insurance companies) to advocate for higher doses and intensities of rehabilitation. There is certainly more research to be done on new ways to deliver high-dose, high-intensity neurorehabilitation, as well as zeroing in on its best timing and dosing, and how to best combine it with drugs and physiological stimulation. In the meantime, our view is that a large body of convergent evidence already justifies seeking to incorporate higher doses and intensities of therapy into current clinical practice as the new standard of care.

Background Patients discharged after acute brain injuries require ongoing medical care to support recovery and treat secondary neurologic complications. Most therapeutic trials for interventions after acute brain injuries use measures of disability (i.e., the Modified Rankin Scale) as primary outcomes, but systematically collecting these outcomes as part of clinical care remains challenging. In addition, understanding patients’ perspectives on recovery is critical to providing personalized care and ultimately improving outcomes. Methods The Post-ICU Neurorecovery clinic at a tertiary care hospital documented two outcome measures as part of routine clinical care: 1) Modified Rankin Scale (mRS), and 2) Free-text response to “What is the single most important thing the NeuroRecovery clinic can do to support you/your loved one in the journey of recovery”. Weekly clinic reminders to providers to use a SmartPhrase that integrated these outcome measures into clinical documentation was implemented. A qualitative content analysis of the SmartPhrase responses was conducted. mRS scores were examined in relation to results from qualitative content analysis. Results After the implementation of weekly clinical email reminders, documentation of the smartphrase improved from 29 % to 60 % for all clinic visits over a pilot period of 11 months (July 2022-May 2023). Physical health (n = 82, 37 %), functional recovery (n = 37, 17 %), mental health (n = 31, 14 %), and social health (n = 18, 8 %) were the most common themes (codes) abstracted from the free-text responses. Themes varied by mRS levels; as mRS scores increased (i.e., increased disability), patients reported greater need for physical health support. Conclusion Standardized, systematic documentation of outcomes in Neurorecovery clinics may provide an opportunity to develop patient-centric and disability level-specific goals for recovery.

The Fugl Meyer Assessment (FMA) is a widely-used assessment for tracking motor function recovery post-stroke. Due to the limited access to rehabilitation, there exists a need for remote and automated assessment solutions. Wearable sensors and data-driven methods have shown promise for enabling automatic upper extremity FMA (FMA-UE) estimation, but minimizing user input motion and aligning with current clinical activities will aid the adoption of sensor-based assessments. In this work, we present an FMA-UE estimator which can make score predictions for a key subset of the assessment (70% of all items) using data from inertial measurement units (IMUs) placed on the arms and the trunk from three volitional reaching motions representative of functional daily activities. We collected a dataset of eleven stroke participants performing a subset of FMA-UE, and three reaching motions. The FMA-UE of each participant was assessed by an occupational therapist providing the labeled score for the training data. The estimator was trained on windowed data during FMA-UE motions and was able to make score estimates from reaching motions. Through leave-one-subjectout cross validation, the estimator achieved a normalized RMSE of 7%, which is comparable to or below the established minimal clinically important difference and minimal detectable change of FMA-UE of post-stroke individuals. Comparison experiments of various model designs also revealed the importance of trunk-based features inspired by compensation strategies common post stroke and features extracted from the hand sensor. The proposed estimator has the potential to broaden the possibility of automatic assessment via wearable sensors.

Objective Patient‐reported outcome measures (PROMs), which capture patients' perspectives on the consequences of health and disease, are widely used in neurological care and research. However, it is unclear how PROMs relate to performance‐rated impairments. Sociodemographic factors are known to affect PROMs. Direct damage to brain regions critical for self‐awareness (i.e., parietal regions and the salience/ventral‐attention network) may also impair self‐report outcomes. This study examined the relationship between PROMs and performance‐based measures in stroke survivors with arm motor impairments. We hypothesized that PROMs would be distinct from performance‐based outcomes, influenced by sociodemographic factors, and linked to damage in brain circuits involved in self‐perception. Methods We longitudinally assessed 54 stroke survivors using patient‐reported and performance‐rated measures at 4 timepoints. We used factor analysis to reveal the outcome battery's factorial structure. Linear regression examined the association between classes of measures and sociodemographics. Voxel‐lesion‐symptom‐mapping, region‐of‐interest‐based analysis, and voxel‐lesion‐network‐mapping investigated the relationship between classes of outcomes and stroke‐related injury. Results Performance‐based and patient‐reported measures formed distinct factors, consistent across recovery phases. Higher education (β1 = 0.36, p = 0.02) and income adequacy (β2 = 0.48, p = 0.05) were associated with patient‐reported, but not performance‐rated outcomes. Greater parietal lobe injury, irrespective of hemisphere, was associated with worse patient‐reported outcomes; greater corticospinal tract injury related to worse performance‐rated outcomes. Lesions with greater functional connectivity to the salience/ventral‐attention network were associated with worse patient‐reported outcomes (r = −0.35, p = 0.009). Interpretation Our findings reveal important differences between performance‐rated and patient‐reported outcomes, each with specific associated factors and anatomy post‐stroke. Incorporating sociodemographic and neuroanatomic characteristics into neurorehabilitation strategies may inform and optimize patient outcomes. ANN NEUROL 2025;97:242–253

Focal brain lesions (such as with stroke) cause functional changes in local and distributed neural systems. While there is a long history of post-stroke neurophysiological assessment using electroencephalography (EEG), the observed neurophysiological changes have rarely been related to specific lesion locations. Therefore, the relationships between anatomical injury and physiological changes after stroke remain unclear. Voxel-based lesion symptom mapping (VLSM) is a tool for statistically relating stroke lesion locations to "symptoms", but current VLSM methods are restricted to symptoms that can be defined by a single value. Therefore, current VLSM techniques are unable to map the relationships between anatomical injury and multidimensional neurophysiological data such as EEG, which contains rich spatio-temporal information across different channels and frequency bands. Here we present a novel algorithm, EEG Voxel-based Lesion Mapping (EEG-VLM), that produces the set of significant relationships between precise neuroanatomical injury locations and neurophysiology (defined by a cluster of adjacent EEG channels and frequency bands). Further, the algorithm provides statistical analyses to define the overall significance of each neural structure-function relationship by correcting for multiple comparisons using a permutation test. Applying EEG-VLM to a dataset of recordings from chronic stroke patients performing a cued upper extremity movement task, we found that subjects with lesions in frontal subcortical white matter have reduced ipsilesional parietal cue-evoked EEG responses. These results are consistent with damage to a frontal-parietal network that has been associated with impairments in attention. EEG-VLM is a novel and unbiased method for relating neurophysiologic changes after stroke with neuroanatomic lesions. In the context of focal brain lesions associated with neurological impairments, we propose that this method will enable improved mechanistic understanding, facilitate biomarker development, and guide neurorehabilitation strategies.