Displacement of Sensory Maps and Disorganization of Motor Cortex After Targeted Stroke in Mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recovery from stroke is hypothesized to involve the reorganization of surviving cortical areas. To study the functional organization of sensorimotor cortex at multiple time points before and after stroke, we performed longitudinal light-based motor mapping of transgenic mice expressing light-sensitive channelrhodopsin-2 in layer 5 cortical neurons. METHODS: Pulses of light stimulation were targeted to an array of cortical points, whereas evoked forelimb motor activity was recorded using noninvasive motion sensors. Intrinsic optical signal imaging produced maps of the forelimb somatosensory cortex. The resulting motor and sensory maps were repeatedly generated for weeks before and after small (0.2 mm3) photothrombotic infarcts were targeted to forelimb motor or sensory cortex. RESULTS: Infarcts targeted to forelimb sensory or motor areas caused decreased motor output in the infarct area and spatial displacement of sensory and motor maps. Strokes in sensory cortex caused the sensory map to move into motor cortex, which adopted a more diffuse structure. Stroke in motor cortex caused a compensatory increase in peri-infarct motor output, but did not affect the position or excitability of sensory maps. CONCLUSIONS: After stroke in motor cortex, decreased motor output from the infarcted area was offset by peri-infarct excitability. Sensory stroke caused a new sensory map to form in motor cortex, which maintained its center position, despite becoming more diffuse. These data suggest that surviving regions of cortex are able to assume functions from stroke-damaged areas, although this may come at the cost of alterations in map structure.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".