Transcriptomic Profiling Reveals Discrete Poststroke Dementia Neuronal and Gliovascular Signatures
Bibliographic record
Abstract
Poststroke dementia (PSD) is associated with pathology in frontal brain regions, in particular dorsolateral prefrontal cortex (DLPFC) neurons and white matter, remote from the infarct. We hypothesised that PSD results from progressive DLPFC neuronal damage, associated with frontal white matter gliovascular unit (GVU) alterations. We investigated the transcriptomic profile of the neurons and white matter GVU cells previously implicated in pathology. Laser-capture microdissected neurons, astrocytes and endothelial cells were obtained from the Cognitive Function After Stroke cohort of control, PSD and poststroke non-dementia (PSND) human subjects. Gene expression was assessed using microarrays and pathway analysis to compare changes in PSD with controls and PSND. Neuronal findings were validated using NanoString technology and compared with those in the bilateral common carotid artery stenosis (BCAS) mouse model. Comparing changes in PSD compared to controls with changes in PSND compared to controls identified transcriptomic changes associated specifically with dementia. DLPFC neurons showed defects in energy production (tricarboxylic acid (TCA) cycle, adenosine triphosphate (ATP) binding and mitochondria), signalling and communication (MAPK signalling, Toll-like receptor signalling, endocytosis). Similar changes were identified in neurons isolated from BCAS mice. Neuronal findings accompanied by altered astrocyte communication and endothelium immune changes in the frontal white matter, suggesting GVU dysfunction. We propose a pathogenic model in PSD whereby neuronal changes are associated with frontal white matter GVU dysfunction leading to astrocyte failure in supporting neuronal circuits resulting in delayed cognitive decline associated with PSD. Therefore, targeting these processes could potentially ameliorate the dementia seen in PSD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".