Micro-ultrasound based characterization of cerebrovasculature following focal ischemic stroke and upon short-term rehabilitation
Bibliographic record
Abstract
Notwithstanding recanalization treatments in the acute stage of stroke, many survivors suffer long-term impairments. Physical rehabilitation is the only widely available strategy for chronic-stage recovery, but its optimization is hindered by limited understanding of its effects on brain structure and function. Using micro-ultrasound, behavioral testing, and electrophysiology, we investigated the impact of skilled reaching rehabilitation on cerebral hemodynamics, motor function, and neuronal activity in a rat model of focal ischemic stroke. A 50 MHz micro-ultrasound transducer and intracortical electrophysiology were utilized to characterize neurovascular changes three weeks following focal ischemia elicited by endothelin-1 injection into the sensorimotor cortex. Sprague-Dawley rats were rehabilitated through tray reaching, and their fine skilled reaching was assessed via the Montoya staircase. Focal ischemia led to a sustained deficit in forelimb reaching; and increased tortuosity of the penetrating vessels in the perilesional cortex; with no lateralization of spontaneous neuronal activity. Rehabilitation improved skilled reaching; decreased cortical vascularity; was associated with elevated peri- vs. contralesional hypercapnia-induced flow homogenization and increased perilesional spontaneous cortical neuronal activity. Our study demonstrated neurovascular plasticity accompanying rehabilitation-elicited functional recovery in the subacute stage following stroke, and multiple micro-ultrasound-based markers of cerebrovascular structure and function modified in recovery from ischemia and upon rehabilitation.
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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 | 0.000 | 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".