Remapping the Somatosensory Cortex after Stroke: Insight from Imaging the Synapse to Network
Bibliographic record
Abstract
Together, thousands of neurons with similar function make up topographically oriented sensory cortex maps that represent contralateral body parts. Although this is an accepted model for the adult cortex, whether these same rules hold after stroke-induced damage is unclear. After stroke, sensory representations damaged by stroke remap onto nearby surviving neurons. Here, we review the process of sensory remapping after stroke at multiple levels ranging from the initial damage to synapses, to their rewiring and function in intact sensory circuits. We introduce a new approach using in vivo 2-photon calcium imaging to determine how the response properties of individual somatosensory cortex neurons are altered during remapping. One month after forelimb-area stroke, normally highly limb-selective neurons in surviving peri-infarct areas exhibit remarkable flexibility and begin to process sensory stimuli from multiple limbs as remapping proceeds. Two months after stroke, neurons within remapped regions develop a stronger response preference. Thus, remapping is initiated by surviving neurons adopting new roles in addition to their usual function. Later in recovery, these remapped forelimb-responsive neurons become more selective, but their new topographical representation may encroach on map territories of neurons that process sensory stimuli from other body parts. Neurons responding to multiple limbs may reflect a transitory phase in the progression from their involvement in one sensorimotor function to a new function that replaces processing lost due to 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".