In Vivo Calcium Imaging Reveals Functional Rewiring of Single Somatosensory Neurons after Stroke
Bibliographic record
Abstract
Functional mapping and microstimulation studies suggest that recovery after stroke damage can be attributed to surviving brain regions taking on the functional roles of lost tissues. Although this model is well supported by data, it is not clear how activity in single neurons is altered in relation to cortical functional maps. It is conceivable that individual surviving neurons could adopt new roles at the expense of their usual function. Alternatively, neurons that contribute to recovery may take on multiple functions and exhibit a wider repertoire of neuronal processing. In vivo two-photon calcium imaging was used in adult mice within reorganized forelimb and hindlimb somatosensory functional maps to determine how the response properties of individual neurons and glia were altered during recovery from ischemic damage over a period of 2-8 weeks. Single-cell calcium imaging revealed that the limb selectivity of individual neurons was altered during recovery from ischemia, such that neurons normally selective for a single contralateral limb processed information from multiple limbs. Altered limb selectivity was most prominent in border regions between stroke-altered forelimb and hindlimb macroscopic map representations, and peaked 1 month after the targeted insult. Two months after stroke, individual neurons near the center of reorganized functional areas became more selective for a preferred limb. These previously unreported forms of plasticity indicate that in adult animals, seemingly hardwired cortical neurons first adopt wider functional roles as they develop strategies to compensate for loss of specific sensory modalities after forms of brain damage such as 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) | 0.000 | 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 | 0.001 |
| 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".