The Effect of Lesion Size on the Organization of the Ipsilesional and Contralesional Motor Cortex
Bibliographic record
Abstract
Recovery of hand function following lesions in the primary motor cortex (M1) is associated with a reorganization of premotor areas in the ipsilesional hemisphere, and this reorganization depends on the size of the lesion. It is not clear how lesion size affects motor representations in the contralesional hemisphere and how the effects in the 2 hemispheres compare. Our goal was to study how lesion size affects motor representations in the ipsilesional and contralesional hemispheres. In rats, we induced lesions of different sizes in the caudal forelimb area (CFA), the equivalent of M1. The effective lesion volume in each animal was quantified histologically. Behavioral recovery was evaluated with the Montoya Staircase task for 28 days after the lesion. Then, the organization of the CFA and the rostral forelimb area (RFA)--the putative premotor area in rats--in the 2 cerebral hemispheres was studied with intracortical microstimulation mapping techniques. The distal forelimb representation in the RFA of both the ipsilesional and contralesional hemispheres was positively correlated with the size of the lesion. In contrast, lesion size had no effect on the contralesional CFA, and there was no relationship between movement representations in the 2 hemispheres. Finally, only the contralesional RFA was negatively correlated with chronic motor deficits of the paretic forelimb. Our data show that lesion size has comparable effects on motor representations in premotor areas of both hemispheres and suggest that the contralesional premotor cortex may play a greater role in the recovery of the paretic forelimb following large lesions.
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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) | 0.001 | 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 | 0.001 |
| 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".