Rapid and Bihemispheric Reorganization of Neuronal Activity in Premotor Cortex after Brain Injury
Bibliographic record
Abstract
Brain injuries cause hemodynamic changes in several distant, spared areas from the lesion. Our objective was to better understand the neuronal correlates of this reorganization in awake, behaving female monkeys. We used reversible inactivation techniques to “injure” the primary motor cortex, while continuously recording neuronal activity of the ventral premotor cortex in the two hemispheres, before and after the onset of behavioral impairments. Inactivation rapidly induced profound alterations of neuronal discharges that were heterogeneous within each and across the two hemispheres, occurred during movements of either the affected or nonaffected arm, and varied during different phases of grasping. Our results support that extensive, and much more complex than expected, neuronal reorganization takes place in spared areas of the bihemispheric cortical network involved in the control of hand movements. This broad pattern of reorganization offers potential targets that should be considered for the development of neuromodulation protocols applied early after brain injury. SIGNIFICANCE STATEMENTIt is well known that brain injuries cause changes in several distant, spared areas of the network, often in the premotor cortex. This reorganization is greater early after the injury and the magnitude of early changes correlates with impairments. However, studies to date have used noninvasive brain imaging approaches or have been conducted in sedated animals. Therefore, we do not know how brain injuries specifically affect the activity of neurons during the generation of movements. Our study clearly shows how a lesion rapidly impacts neurons in the premotor cortex of both hemispheres. A better understanding of these complex changes can help formulate hypotheses for the development of new treatments that specifically target neuronal reorganization induced by lesions in the brain.
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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".