Increased functional connectivity after stroke correlates with behavioral scores in non-human primate model
Bibliographic record
Abstract
Here we characterized the functional connectivity (FC) changes occurring after a controlled MCA stroke in a primate model. We hypothesize that if FC can inform about the neural changes after a stroke in the non-human primate (NHP) stroke model, then significant FC changes after the stroke would have to correlate with the remaining behavioral capacities. Eleven cynomolgus monkeys underwent an experimental middle cerebral artery occlusion while five monkeys remained as the control group. One month later the neurological function was assessed with a set of fine motor tasks and the Nonhuman Primate Stroke Scale (NHPSS). Structural and functional connectivity analyses were done to compare both groups. Three FC changes showed significant behavioral correlations: right sensorimotor-right lateral intraparietal FC with the six-well task; left posterior intraparietal-left dorsal premotor FC with the hill task; and right visual-left primary motor FC with the NHPSS. In the three instances, stronger FC correlated with better behavioral outcome. The results show that the functional changes correlating with behavioral outcomes involved sensorimotor cortices that were not restricted to the affected hemisphere. These results show that the FC analysis in NHP stroke model is a relevant methodology suitable to inform the neural changes occurring after a 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 | 0.000 |
| 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".