Retrograde and anterograde trans-synaptic viral tracing of neuronal connections reveals local and distant effects of ischemic stroke on dendritic spines
Bibliographic record
Abstract
Focal stroke leads to complex neurological disturbances with variable recovery. One explanation for this variability is that stroke disrupts local and remote neural circuits via the connectome, termed 'diaschisis'. Past studies have yielded mixed effects of stroke on dendritic structure in distant regions. However, a previous limitation was the lack of sampling specifically from neurons directly connected to those within the infarct. To overcome this, we used retrograde and anterograde trans-synaptic AAVs to examine dendritic spine density in neurons that provide inputs to, or receive outputs (pre- and post-synaptic) from primary forelimb somatosensory cortex at 1 or 6 weeks after stroke. For both pre- and post-synaptic neurons, spine density was generally lower in superficial and deep neurons in peri-infarct and motor cortex at 1 week, which recovered by 6 weeks. By contrast, no changes in spine density were observed in ipsilateral secondary somatosensory (S2) or contralateral primary somatosensory cortex at 1 week, although there was an increase in spines in select S2 neurons at 6 weeks. Our data show that some cortical connections are more disrupted by stroke than others, particularly those in peri-infarct and motor cortex which could serve as an important substrate for stroke recovery and future therapies.
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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".