Resting State Interhemispheric Motor Connectivity and White Matter Integrity Correlate with Motor Impairment in Chronic Stroke
Bibliographic record
Abstract
Functional and structural reorganization in the brain occurs after stroke. The ability to predict motor outcomes may depend on patterns of brain functional and structural connectivity. We tested the hypothesis that alterations in motor transcallosal and corticospinal connections correlate with motor impairment in patients with chronic stroke. Eleven ischemic stroke patients underwent the Upper Extremity Fugl-Meyer (UE-FM) assessment, resting state functional magnetic resonance imaging, and diffusion tensor imaging (DTI). Twelve healthy control subjects underwent DTI. We assessed the temporal coupling in neural activity between interhemispheric motor cortex, and white matter integrity by means of fractional anisotropy (FA), in the transcallosal motor fibers and corticospinal tract. Partial correlation analyses were performed to determine whether these connectivity measures correlate with Upper UE-FM scores. Patients compared to controls had reduced FA in common voxels of transcallosal motor and ipsilesional corticospinal fibers. Within the patient group those with higher interhemispheric motor cortex connectivity and higher FA in the transcallosal motor fibers were less impaired. The results show that markers of functional and structural motor cortex connectivity correlate with motor impairment in the chronic stage of stroke.
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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".