Stroke accelerates and uncouples intrinsic and synaptic excitability maturation of mouse hippocampal DCX+ adult-born granule cells
Bibliographic record
Abstract
Stroke robustly stimulates adult neurogenesis in the hippocampal dentate gyrus. It is currently unknown whether this process induces beneficial or maladaptive effects, but morphological and behavioral studies have reported aberrant neurogenesis and impaired hippocampal-dependent memory following stroke. However, the intrinsic function and network incorporation of adult-born granule cells (ABGCs) after ischemia is unclear. Using patch-clamp electrophysiology, we evaluated doublecortin-positive (DCX+) ABGCs as well as DCX− dentate gyrus granule cells 2 weeks after a stroke or sham operation in DCX/DsRed transgenic mice of either sex. The developmental status, intrinsic excitability, and synaptic excitability of ABGCs were accelerated following stroke, while dendritic morphology was not aberrant. Regression analysis revealed uncoupled development of intrinsic and network excitability, resulting in young, intrinsically hyperexcitable ABGCs receiving disproportionately large glutamatergic inputs. This aberrant functional maturation in the subgroup of ABGCs in the hippocampus may contribute to defective hippocampal function and increased seizure susceptibility following stroke. SIGNIFICANCE STATEMENT Stroke increases hippocampal neurogenesis but the functional consequences of the postlesional response is mostly unclear. Our findings provide novel evidence of aberrant functional maturation of newly generated neurons following stroke. We demonstrate that stroke not only causes an accelerated maturation of the intrinsic and synaptic parameters of doublecortin-positive, new granule cells in the hippocampus, but that this accelerated development does not follow physiological dynamics due to uncoupled intrinsic and synaptic maturation. Hyperexcitable immature neurons may contribute to disrupted network integration following 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".