B.3 Early motor cortex dysconnectivity and compensatory neuronal reactivity in acute stroke is dependent on the side of stroke
Bibliographic record
Abstract
Background: We aim to assess the resting state functional connectivity (RSFC) and reactivity with functional near-infrared spectroscopy (fNIRS) in patients with acute stroke compared to age, sex and comorbidity-matched subjects. Methods: Patients with acute anterior circulation stroke syndrome localizing to the right (RH) or left hemisphere (LH) were enrolled. RSFC was assessed using group-level seed-based (Primary Motor cortex,PMC) correlation analysis. Finger-tapping-associated relative oxygen Hemoglobin (ΔHbO) changes were analyzed with generalized linear model regression. Results: 127 participants (RH stroke, 51; LH stroke, 43; control, 33) enrolled at a median of 21 (15,29) hours after symptom onset. Compared to the control group, the RSFC with the affected PMC (LH stroke) was reduced over the affected somatosensory cortex (SSC) in the minor ischemic stroke (IS) (r = -0.14 (-0.3,-0.01)), minor intracerebral hemorrhage (ICH) (-0.48 (-0.78,-0.18)) and major ICH groups (-0.2 (-0.4,-0.01). In the FT task compared to the control groups in LH stroke, ΔHbO was increased over the affected SSC in minor IS (β11.2(1.9,20.5)) and major ICH group (β11.7 (1.4,22.1)). In the FT task in RH stroke, ΔHbO was increased over the unaffected PMC in minor IS (β12.1(2.3,21.8)), major IS (β14.9 (0.3,29.5)), minor ICH (β25.7 (10.1,41.2)) and major ICH (β13.4 (1.1,25.6). Conclusions: Motor cortex dysconnectivity may be worse over the LH stroke. In RH stroke, there is early compensatory increased neuronal activity over the unaffected PMC. These results suggest differential acute remodelling in RH and LH strokes.
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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 | 0.000 | 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) | 0.004 | 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".