Subacute functional connectivity correlates with cognitive recovery six months after stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cognitive impairment is a common consequence of stroke, and the rewiring of the surviving brain circuits might contribute to cognitive recovery. Studies investigating how the functional connectivity of networks change across time and whether their remapping relates to cognitive recovery in stroke patients are scarce. We aimed to investigate whether resting-state functional connectivity was associated with cognitive performance in stroke patients and if any alterations in these networks were correlated with cognitive recovery. METHODS: Using an fMRI ROI-ROI approach, we compared the ipsilesional, contralesional and interhemispheric functional connectivity of three resting-state networks involved in cognition - the Default Mode (DMN), Salience (SN) and Central Executive Networks (CEN), in subacute ischemic stroke patients (time 1, n = 37, stroke onset: 24.32 ± 7.44 days, NIHSS: 2.66 ± 3.45) with cognitively healthy controls (n = 20). Patients were reassessed six months after the stroke event (time 2, n = 20, stroke onset: 182.05 ± 8.17 days) to verify the subsequent reorganization of functional connections and whether such reorganization was associated with cognitive recovery. RESULTS: At time 1, patients had weaker interhemispheric connectivity in the DMN than controls; better cognitive performance at time 1 was associated with stronger interhemispheric and ipsilesional DMN connectivity, and weaker contralesional SN connectivity. At time 2, there were no changes in functional connectivity in stroke patients, compared to time 1. Better cognitive recovery measured at time 2 (time 2 - time 1) was associated with stronger functional connectivity in the DMN, and weaker interhemispheric subacute connectivity in the SN, both from time 1. CONCLUSIONS: Stroke disrupts the functional connectivity of the DMN, not only at the lesioned hemisphere but also between hemispheres. Six months after the stroke event, we could not detect the remapping of networks. Cognitive recovery was associated with the connectivity of both the DMN and SN of time 1. Our findings may be helpful for facilitating further understanding of the potential mechanisms underlying post-stroke cognitive performance.
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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) | 0.002 | 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".