The feasibility of functional near-infrared spectroscopy (fNIRS) to measure rehabilitation-induced changes in upper limb movement related to motor learning interventions in chronic stroke
Bibliographic record
Abstract
Background: Functional near-infrared spectroscopy (fNIRS) offers a non-invasive approach to monitoring rehabilitation-induced brain activity changes following motor learning interventions in stroke patients. This study aimed to explore the extent of brain activity and motor performance changes resulting from such interventions. Methods: Seven participants with chronic stroke (63.6 ± 7.5 years old; six males, one female) underwent a ten-day intervention consisting of aerobic exercise priming combined with task-specific motor practice using the Kinesiological Instrument for Normal and Altered Reaching Movements (KINARM) End-Point robotic system. Motor performance was evaluated using the Wolf Motor Function Test (WMFT), and brain activity was measured with fNIRS, focusing on key Regions of Interest (ROIs) such as the motor cortex, somatosensory cortex, and prefrontal cortex. Results: Clinical tests such as the WMFT showed moderate improvements in upper limb recovery, but these changes were not statistically significant (p < 0.05). Similarly, overall fNIRS analysis revealed that changes in brain activity before and after the intervention were not statistically significant (p < 0.05). However, significant changes were observed in certain ROIs regarding Oxyhemoglobin (HbO) concentrations and time-to-peak values in specific cases. Conclusion: Although motor performance improvements were modest and not statistically significant, fNIRS detected significant changes in brain activity in certain brain areas before and after the intervention. These findings highlight the potential of fNIRS as a biomarker for rehabilitation-induced neuroplasticity and offer insights for enhancing stroke recovery interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".