Exploring the relationship between prefrontal cortex activation, standing balance, and fatigue in people post-stroke: A fNIRS study
Bibliographic record
Abstract
BackgroundBalance impairments and fatigue are common after stroke and impact physical therapy assessments and treatments. Reasons are multifactorial and include motor dysfunction and changes to cortical activation poststroke. The prefrontal cortex (PFC) is involved in motor control; yet, limited research has explored cortical activation during common physical therapy balance tasks or the link with fatigue.ObjectivesDuring standing balance tasks, the objective was to determine whether PFC activation levels: (1) change between tasks, (2) are asymmetric, and (3) are associated with fatigue.MethodsPatients with hemiparesis were recruited from an inpatient stroke unit and functional near-infrared spectroscopy was applied bilaterally over the PFC to measure cortical activation during balance tasks. Fatigue was assessed using the Fatigue Severity Scale (FSS).ResultsNine participants were included. PFC activation during semi-tandem stance showed greater amplitude than during double-leg stance, indicating more cortical activation. Bilateral PFC activation was observed during both tasks. Participants with greater fatigue (higher FSS score) showed more activation in the ipsilesional PFC than the contralesional PFC.ConclusionPFC activation may occur when performing more challenging balance postures, potentially indicating compensatory activation, and may be linked with greater fatigue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".