Neuroplasticity of Cortical Planning for Initiating Stepping Poststroke: A Case Series
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Therapeutic exercise improves balance and walking ability in individuals after stroke. The extent to which motor planning improves with therapeutic exercise is unknown. This case series examined how outpatient physical therapy affects motor planning and motor performance for stepping. CASE DESCRIPTION: Individuals poststroke performed self-initiated stepping before (baseline), after (postintervention), and 1 month after (retention) intervention. Amplitude and duration of the movement-related cortical potential (MRCP) was measured using an electroencephalograph from the Cz electrode. Electromyography (EMG) of biceps femoris (BF) was collected. Additionally, clinical measures of motor impairment and function were evaluated at all 3 time points by a blinded assessor. INTERVENTION: Two types of outpatient physical therapy were performed for 6 weeks: CONVENTIONAL (n = 3) and FAST (n = 4, Fast muscle Activation and Stepping Training). OUTCOMES: All 7 participants reduced MRCP duration, irrespective of the type of physical therapy. The MRCP amplitude and BF EMG onset changes were more variable. Clinical outcomes improved or were maintained for all participants. The extent of motor impairment was associated with MRCP amplitude. DISCUSSION: Changes in MRCP duration suggest that outpatient physical therapy may promote neuroplasticity of motor planning of stepping movements after stroke; however, a larger sample is needed to determine whether this finding is valid.This case series suggests motor planning for initiating stepping may improve after 6 weeks of outpatient physical therapy for persons with stroke.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A307).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".