Abstract WP567: The Association Between Decreased Cerebral Blood Flow in Transient Ischemic Attack Patients and Cognition
Bibliographic record
Abstract
Patients with transient ischemic attack (TIA) have a 4-fold increase in developing late-life dementia. Cerebral blood flow has shown to be reduced approximately 30% in patients with Alzheimer’s disease (AD). Vascular abnormalities may initiate and aggravate AD pathology prior to the development of clinical symptoms. We hypothesize that middle-aged patients with a history of TIA will have lower cerebral blood flow (CBF) in the hippocampus, thalamus, white matter and grey matter compared to healthy controls. Additionally, we hypothesize that individuals with lower cerebral blood flow in our regions of interest will have lower cognitive scores. Participants between the ages of 45-85 years old without dementia were subject to a clinical brain MRI arterial-spin-labelling CBF sequence accompanied by a cognitive battery. The cognitive battery included the Montreal Cognitive Assessment (MOCA) and a composite memory score created with the Addenbrooke’s Cognitive Examination, the Brief Visuospatial Memory Test Revised, and the Rey Auditory Verbal Learning Test. TIA patients had their assessments within 14-days of ictus. Atlas-based imaging analysis was performed using FSL. Sixty-three (63) healthy controls (63 + 10 years of age) and 51 TIA (69 + 9 years of age) participants were analyzed. In an independent one-tailed t-test, TIA patients had significantly (p<0.05) lower CBF than healthy controls in the left/right hippocampus and thalamus, and right white matter. A multiple regression was performed to predict MOCA and composite memory scores from CBF, while correcting for age, gender and premorbid intelligence. Lower hemispheric CBF in the hippocampus, thalamus, white matter and grey matter each significantly predicted lower MOCA scores (F(4,101), R 2 =[0.1701-0.2013], p<0.05) and lower composite memory scores (F(4,101), R 2 =[0.2429-0.2864], p<0.05) in all participants. In conclusion , TIA patients have significantly lower CBF values in regions of interest compared to healthy control subjects and decreased CBF values in all participants are associated with poorer cognitive scores. By investigating neuroimaging biomarkers associated with cognition in mid-life, we may begin to understand why TIA patients have a higher risk of developing AD in late-life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".