Critical Period After Stroke Study (CPASS): A phase II clinical trial testing an optimal time for motor recovery after stroke in humans.

Restoration of human brain function after injury is a signal challenge for translational neuroscience. Rodent stroke recovery studies identify an optimal or sensitive period for intensive motor training after stroke: near-full recovery is attained if task-specific motor training occurs during this sensitive window. We extended these findings to adult humans with stroke in a randomized controlled trial applying the essential elements of rodent motor training paradigms to humans. Stroke patients were adaptively randomized to begin 20 extra hours of self-selected, task-specific motor therapy at ≤30 d (acute), 2 to 3 mo (subacute), or ≥6 mo (chronic) after stroke, compared with controls receiving standard motor rehabilitation. Upper extremity (UE) impairment assessed by the Action Research Arm Test (ARAT) was measured at up to five time points. The primary outcome measure was ARAT recovery over 1 y after stroke. By 1 y we found significantly increased UE motor function in the subacute group compared with controls (ARAT difference = +6.87 ± 2.63, P = 0.009). The acute group compared with controls showed smaller but significant improvement (ARAT difference = +5.25 ± 2.59 points, P = 0.043). The chronic group showed no significant improvement compared with controls (ARAT = +2.41 ± 2.25, P = 0.29). Thus task-specific motor intervention was most effective within the first 2 to 3 mo after stroke. The similarity to rodent model treatment outcomes suggests that other rodent findings may be translatable to human brain recovery. These results provide empirical evidence of a sensitive period for motor recovery in humans.

Shoulder position and handedness differentially affect excitability and intracortical inhibition of hand muscles.

Individuals with stroke show distinct differences in hand function impairment when the shoulder is in adduction, within the workspace compared to when the shoulder is abducted, away from the body. To better understand how shoulder position affects hand control, we tested the corticomotor excitability and intracortical control of intrinsic and extrinsic hand muscles important for grasp in twelve healthy individuals. Motor evoked potentials (MEP) using single and paired-pulse transcranial magnetic stimulation were elicited in extensor digitorum communis (EDC), flexor digitorum superficialis (FDS), first dorsal interosseous (FDI), and abductor pollicis brevis (APB). The shoulder was fully supported in horizontal adduction (ADD) or abduction (ABD). Separate mixed-effect models were fit to the MEP parameters using shoulder position (or upper-extremity [UE] side) as fixed and participants as random effects. In the non-dominant UE, EDC showed significantly greater MEPs in shoulder ABD than ADD. In contrast, the dominant side EDC showed significantly greater MEPs in ADD compared to ABD; %facilitation of EDC on dominant side showed significant stimulus intensity x position interaction, EDC excitability was significantly greater in ADD at 150% of the resting threshold. Intrinsic hand muscles of the dominant UE received significantly more intracortical inhibition (SICI) when the shoulder was in ADD compared to ABD; there was no position-dependent modulation of SICI on the non-dominant side. Our findings suggest that these resting-state changes in hand muscle excitabilities reflect the natural statistics of UE movements, which in turn may arise from as well as shape the nature of shoulder-hand coupling underlying UE behaviors.

Inaccurate Use of the Upper Extremity Fugl-Meyer Negatively Affects Upper Extremity Rehabilitation Trial Design: Findings From the ICARE Randomized Controlled Trial.

OBJECTIVE: To determine the extent to which estimates of sample and effect size in stroke rehabilitation trials can be affected by simple summation of ordinal Upper Extremity Fugl-Meyer (UEFM) items compared with a Rasch-rescaled UEFM. DESIGN: Rasch analysis of Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) phase III trial data, comparing 3 upper extremity (UE) motor treatments in stroke survivors enrolled 45.8±22.4 days poststroke. Participants underwent a structured UE motor training known as the Accelerated Skill Acquisition Program, usual and customary care, or dose-equivalent care. UEFM data from baseline, postintervention, and 6 and 12 months later were included for analysis. SETTING: Outpatient stroke rehabilitation. PARTICIPANTS: ICARE participants (N=361). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Item difficulties, person abilities, and sample size. RESULTS: Because of their ordinality, summed raw UEFM scores measured motor impairment inconsistently across different ranges of stroke severity relative to the rescaled UEFM. In the full ICARE sample, raw UEFM understated scores relative to the rescaled UEFM by 7.4 points for the most severely impaired, but overstated scores by up to 8.4 points toward the ceiling. As a result, 50.9% of all UEFM observations showed a residual error greater than 10% of the total UEFM score. Relative to the raw scores, the rescaled UEFM improved the effect size of change in motor impairment between baseline and 1 year (d=0.35). For a hypothetical 3-arm trial resembling ICARE, UEFM rescaling reduced the required sample size by 32% (n=108) compared with raw UEFM (n=159). CONCLUSIONS: In UE rehabilitation trials, a rescaled UEFM potentially decreases sample size by one-third, decreasing costs, duration, and the number of subjects exposed to experimental risks. This benefit is obtained through increased measurement efficiency. Reductions in ceiling effects are also possible. These findings apply to ICARE-like trials. Confirmatory validation in another phase III trial is needed.

The Critical Period After Stroke Study (CPASS) Upper Extremity Treatment Protocol.

Objective: To present the development of a novel upper extremity (UE) treatment and assess how it was delivered in the Critical Periods After Stroke Study (CPASS), a phase II randomized controlled trial (RCT). Design: Secondary analysis of data from the RCT. Setting: Inpatient and outpatient settings the first year after stroke. Participants: Of the 72 participants enrolled in CPASS (N=72), 53 were in the study groups eligible to receive the treatment initiated at ≤30 days (acute), 2-3 months (subacute), or ≥6 months (chronic) poststroke. Individuals were 65.1±10.5 years of age, 55% were women, and had mild to moderate UE motor capacity (Action Research Arm Test=17.2±14.3) at baseline. Intervention: The additional 20 hours of treatment began using the Activity Card Sort (ACS), a standardized assessment of activities and participation after stroke, to identify UE treatment goals selected by the participants that were meaningful to them. Treatment activities were broken down into smaller components from a standardized protocol and process that operationalized the treatments essential elements. Main Outcome Measures: Feasibility of performing the treatment in a variety of clinical settings in an RCT and contextual factors that influenced adherence. Results: A total of 49/53 participants fully adhered to the CPASS treatment. The duration and location of the treatment sessions and the UE activities practiced during therapy are presented for the total sample (n=49) and per study group as an assessment of feasibility and the contextual factors that influenced adherence. Conclusions: The CPASS treatment and therapy goals were explicitly based on the meaningful activities identified by the participants using the ACS as a treatment planning tool. This approach provided flexibility to customize UE motor therapy without sacrificing standardization or quantification of the data regardless of the location and UE impairments of participants within the first year poststroke.

Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke.

Objective: This study aims to investigate the validity of machine learning-derived amount of real-world functional upper extremity (UE) use in individuals with stroke. We hypothesized that machine learning classification of wrist-worn accelerometry will be as accurate as frame-by-frame video labeling (ground truth). A second objective was to validate the machine learning classification against measures of impairment, function, dexterity, and self-reported UE use. Design: Cross-sectional and convenience sampling. Setting: Outpatient rehabilitation. Participants: Individuals (>18 years) with neuroimaging-confirmed ischemic or hemorrhagic stroke >6-months prior (n = 31) with persistent impairment of the hemiparetic arm and upper extremity Fugl-Meyer (UEFM) score = 12-57. Methods: Participants wore an accelerometer on each arm and were video recorded while completing an "activity script" comprising activities and instrumental activities of daily living in a simulated apartment in outpatient rehabilitation. The video was annotated to determine the ground-truth amount of functional UE use. Main outcome measures: The amount of real-world UE use was estimated using a random forest classifier trained on the accelerometry data. UE motor function was measured with the Action Research Arm Test (ARAT), UEFM, and nine-hole peg test (9HPT). The amount of real-world UE use was measured using the Motor Activity Log (MAL). Results: The machine learning estimated use ratio was significantly correlated with the use ratio derived from video annotation, ARAT, UEFM, 9HPT, and to a lesser extent, MAL. Bland-Altman plots showed excellent agreement between use ratios calculated from video-annotated and machine-learning classification. Factor analysis showed that machine learning use ratios capture the same construct as ARAT, UEFM, 9HPT, and MAL and explain 83% of the variance in UE motor performance. Conclusion: Our machine learning approach provides a valid measure of functional UE use. The accuracy, validity, and small footprint of this machine learning approach makes it feasible for measurement of UE recovery in stroke rehabilitation trials.

Interpreting the CPASS Trial: Do Not Shift Motor Therapy to the Subacute Phase.

The Critical Periods After Stroke Study (CPASS, n = 72) showed that, compared to controls, an additional 20 hours of intensive upper limb therapy led to variable gains on the Action Research Arm Test depending on when therapy was started post-stroke: the subacute group (2-3 months) improved beyond the minimal clinically important difference and the acute group (0-1 month) showed smaller but statistically significant improvement, but the chronic group (6-9 months) did not demonstrate improvement that reached significance. Some have misinterpreted CPASS results to indicate that all inpatient motor therapy should be shifted to outpatient therapy delivered 2 to 3 months post-stroke. Instead, however, CPASS argues for a large dose of motor therapy delivered continuously and cumulatively during the acute and subacute phases. When interpreting trials like CPASS, one must consider the substantial dose of early usual customary care (UCC) motor therapy that all participants received. CPASS participants averaged 27.9 hours of UCC occupational therapy (OT) during the first 2 months and 9.8 hours of UCC OT during the third and fourth months post-stroke. Any recovery experienced would therefore result not just from CPASS intensive motor therapy but the combined effects of experimental therapy plus UCC. Statistical limitations also did not allow direct comparisons of the acute and subacute group outcomes in CPASS. Instead of shifting inpatient therapy hours to the subacute phase, CPASS argues for preserving inpatient UCC. We also recommend conducting multi-site dosing trials to determine whether additional intensive motor therapy delivered in the first 2 to 3 months following inpatient rehabilitation can further improve outcomes.

Machine Learning Improves Functional Upper Extremity Use Capture in Distal Radius Fracture Patients.

Current outcome measures, including strength/range of motion testing, patient-reported outcomes (PROs), and motor skill testing, may provide inadequate granularity in reflecting functional upper extremity (UE) use after distal radius fracture (DRF) repair. Accelerometry analysis also has shortcomings, namely, an inability to differentiate functional versus nonfunctional movements. The objective of this study was to evaluate the accuracy of machine learning (ML) analyses in capturing UE functional movements based on accelerometry data for patients after DRF repair. In this prospective study, six patients were enrolled 2-6 weeks after DRF open reduction and internal fixation (ORIF). They all performed standardized activities while wearing a wrist accelerometer, and the data were analyzed by an ML algorithm. These activities were also videotaped and evaluated by visual inspection. Our novel ML algorithm was able to predict from accelerometry data whether the limb was performing a movement rated as functional, with accuracy of 90.4% ± 3.6% for within-subject modeling and 79.8% ± 8.9% accuracy for between-subject modeling. The application of ML algorithms to accelerometry data allowed for capture of functional UE activity in patients after DRF open reduction and internal fixation and accurately predicts functional UE use. Such analyses could improve our understanding of recovery and enhance routine postoperative rehabilitation in DRF patients.

Why Are Stroke Rehabilitation Trial Recruitment Rates in Single Digits?

Background: Recruitment of patients in early subacute rehabilitation trials (<30 days post-stroke) presents unique challenges compared to conventional stroke trials recruiting individuals >6 months post-stroke. Preclinical studies suggest treatments be initiated sooner after stroke, thus requiring stroke rehabilitation trials be conducted within days post-stroke. How do specific inclusion and exclusion criteria affect trial recruitment rates for early stroke rehabilitation trials? Objectives: Provide estimates of trial recruitment based on screening and enrollment data from a phase II early stroke rehabilitation trial. Methods: CPASS, a phase II intervention trial screened ischemic stroke patients in acute care (18-months, N = 395) and inpatient rehabilitation (22-months, N = 673). Patients were stratified by upper extremity (UE) impairment into mild (NIHSS motor arm = 0, 1); moderate (NIHSS = 2, 3); severe (NIHSS = 4) and numbers of patients disqualified due to CPASS exclusion criteria determined. We also examined if a motor-specific evaluation (Action Research Arm Test, ARAT) increases the pool of eligible patients disqualified by the NIHSS motor arm item. Results: CPASS recruitment in acute care (5.3%) and inpatient rehabilitation (5%) was comparable to prior trials. In acute care, a short stay (7-17-days), prior stroke (13.5% in moderately; 13.2% in severely impaired) disqualified the majority. In inpatient rehabilitation, the majority (40.8%) were excluded for "too mild" impairment. The next majority were disqualified for reaching inpatient rehabilitation "too late" to participate in an early stroke trial (15% in moderately; 24% in severely impaired). Mean ARAT in the "too mild" showed significant impairment and potential to benefit from participation in select UE rehabilitation trials. Conclusions: Screening of ischemic stroke patients while they are still in acute care is crucial to successful recruitment for early stroke rehabilitation trials. A significant proportion of eligible patients are lost to "short length of stay" in acute care, and arrive to inpatient rehabilitation "too late" for an early rehabilitation trial. Additional screening of mildly impaired patients using a motor function specific scale will benefit the trial recruitment and generalizability. Trial Registration Number: http://www.clinicaltrials.gov Identifier: NCT02235974.

Characterizing upper extremity motor behavior in the first week after stroke.

BACKGROUND: Animal models of brain recovery identify the first days after lesioning as a time of great flux in sensorimotor function and physiology. After rodent motor system lesioning, daily skill training in the less affected forelimb reduces skill acquisition in the more affected forelimb. We asked whether spontaneous human motor behaviors of the less affected upper extremity (UE) early after stroke resemble the animal training model, with the potential to suppress clinical recovery. METHODS: This prospective observational study used a convenience sample of patients (n = 25, mean 4.5 ±1.8) days after stroke with a wide severity range; Controls were hospitalized for non-neurological conditions (n = 12). Outcome measures were Accelerometry, Upper-Extremity Fugl-Meyer (UEFM), Action Research Arm Test (ARAT), Shoulder Abduction/ Finger Extension Test (SAFE), NIH Stroke Scale (NIHSS). RESULTS: Accelerometry indicated total paretic UE movement was reduced compared to controls, primarily due to a 44% reduction of bilateral UE use. Unilateral paretic movement was unchanged. Thus, movement shifted early after stroke; bilateral use was reduced and unilateral use of the non-paretic UE was increased by 77%. Low correlations between movement time and motor performance prompted an exploratory factor analysis (EFA) revealing a 2-component solution; motor performance tests load on one component (motor performance) whereas accelerometry-derived variables load on a second orthogonal component (quantity of movement). CONCLUSIONS: Early after stroke, spontaneous overall UE movement is reduced, and movement shifts to unilateral use of the non-paretic UE. Two mechanisms that could influence motor recovery may already be in place 4.5 ± 1.8 days post stroke: (1) the overuse of the less affected UE, which could set the stage for learned non-use and (2) skill acquisition in the non-paretic limb that could impede recovery. Accurate UE motor assessment requires two independent constructs: motor performance and quantity of movement. These findings provide opportunities and measurement methods for studies to develop new behaviorally-based stroke recovery treatments that begin early after onset.

Impact of Targeted Assistance of Multiarticular Finger Musculotendons on the Coordination of Finger Muscles During Isometric Force Production.

Neurological injuries often cause degraded motor control. While rehabilitation efforts typically focus on movement kinematics, abnormal muscle activation patterns are often the primary source of impairment. Muscle-based therapies are likely more effective than joint-based therapy. In this paper, we examined the feasibility of biomimetic input mimicking the action of human musculotendons in altering hand muscle coordination. Twelve healthy subjects produced a submaximal isometric dorsal fingertip force, while a custom actuator provided assistance mirroring the actions of either the extrinsic extensor or the intrinsic muscles of the index finger. The biomimetic inputs reduced the activation level of all task-related muscles, but the degree of change was different across the muscles, resulting in significant changes in their coordination (co-contraction ratios) and force-electromyography correlations. Each biomimetic assistance particularly increased the neural coupling between its targeted muscle and the antagonist muscle. Subjects appeared to fully take advantage of the assistance, as they provided minimal level of effort to achieve the task goal. The targeted biomimetic assistance may be used to retrain activation patterns post-stroke by effectively modulating connectivity between the muscles in the functional context and could be beneficial to restore hand function and reduce disability.

Grasp-Based Functional Coupling Between Reach- and Grasp-Related Components of Forelimb Muscle Activity.

How are appropriate combinations of forelimb muscles selected during reach-to-grasp movements in the presence of neuromotor redundancy and important task-related constraints? The authors tested whether grasp type or target location preferentially influence the selection and synergistic coupling between forelimb muscles during reach-to-grasp movements. Factor analysis applied to 14-20 forelimb electromyograms recorded from monkeys performing reach-to-grasp tasks revealed 4-6 muscle components that showed transport/preshape- or grasp-related features. Weighting coefficients of transport/preshape-related components demonstrated strongest similarities for reaches that shared the same grasp type rather than the same target location. Scaling coefficients of transport/preshape- and grasp-related components showed invariant temporal coupling. Thus, grasp type influenced strongly both transport/preshape- and grasp-related muscle components, giving rise to grasp-based functional coupling between forelimb muscles.

Neuronal Correlates of Functional Coupling between Reach- and Grasp-Related Components of Muscle Activity.

Coordinated reach-to-grasp movements require precise spatiotemporal synchrony between proximal forelimb muscles (shoulder, elbow) that transport the hand toward a target during reach, and distal muscles (wrist, digit) that simultaneously preshape and orient the hand for grasp. The precise mechanisms through which the redundant neuromuscular circuitry coordinates reach with grasp, however, remain unclear. Recently, Geed and Van Kan (2016) demonstrated, using exploratory factor analysis (EFA), that limited numbers of global, template-like transport/preshape- and grasp-related muscle components underlie the complexity and variability of intramuscular electromyograms (EMGs) of up to 21 distal and proximal muscles recorded while monkeys performed reach-to-grasp tasks. Importantly, transport/preshape- and grasp-related muscle components showed invariant spatiotemporal coupling, which provides a potential mechanism for coordinating forelimb muscles during reach-to-grasp movements. In the present study, we tested whether ensemble discharges of forelimb neurons in the cerebellar nucleus interpositus (NI) and its target, the magnocellular red nucleus (RNm), a source of rubrospinal fibers, function as neuronal correlates of the transport/preshape- and grasp-related muscle components we identified. EFA applied to single-unit discharges of populations of NI and RNm neurons recorded while the same monkeys that were used previously performed the same reach-to-grasp tasks, revealed neuronal components in the ensemble discharges of both NI and RNm neuronal populations with characteristics broadly similar to muscle components. Subsets of NI and RNm neuronal components were strongly and significantly crosscorrelated with subsets of muscle components, suggesting that similar functional units of reach-to-grasp behavior are expressed by NI and RNm neuronal populations and forelimb muscles. Importantly, like transport/preshape- and grasp-related muscle components, their NI and RNm neuronal correlates showed invariant spatiotemporal coupling. Clinical and lesion studies have reported disruption of coupling between reach and grasp following cerebellar damage; the present results expand on those studies by identifying a neuronal mechanism that may underlie cerebellar contributions to spatiotemporal coordination of distal and proximal limb muscles during reaching to grasp. We conclude that finding similar functional units of behavior expressed at multiple levels of information processing along interposito-rubrospinal pathways and forelimb muscles supports the hypothesis that functionally related populations of NI and RNm neurons act synergistically in the control of complex coordinated motor behaviors.