Functional Network Reorganization Following Subcortical Stroke: Applying a Lesional-Functional Imaging Approach (P4.041)
Bibliographic record
Abstract
Objective: To investigate the distributed, polysynaptic network impacts of subcortical stroke in individual patients. We hypothesized that disrupted thalamocortical circuitry facilitates emergence of post-stroke hallucinations via aberrant visual network reorganization. Background: Brainstem lesions causing peduncular hallucinosis produce vivid visual hallucinations occasionally accompanied by sleep disorders. Overlapping brainstem regions modulate visual pathways and REM sleep functions via gating of thalamocortical networks. Design/Methods: In order to identify brain regions with altered connectivity that might support the subjective experience of hallucinations, we investigated the integrity of ponto-geniculate-occipital circuits using seed-based resting-state functional connectivity MRI in a patient compared to 46 healthy controls. Conclusions: Focal injury to the left rostrodorsal pons is sufficient to cause REM sleep behavior disorder and peduncular hallucinosis, suggesting an overlapping mechanism in both syndromes. Damage to this region produced a pattern of altered functional connectivity consistent with disrupted visual cortex connectivity via de-afferentation of thalamocortical pathways. This study is supported by a Canadian Institutes of Health Research Fellowship and the Richard and Edith Strauss Fellowship in Clinical Medicine to M.R.G.
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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) | 0.003 | 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".