Study of the relationships between quantitative measurements of cerebral vascularization obtained by artificial intelligence, cognitive functions, and biomarkers of Alzheimer’s disease (P4-6.005)
Bibliographic record
Abstract
Objective: Explore the relationship between the diameters and the volumes of the intracranial arteries and i) cognition; ii) Alzheimer’s Disease (AD) biomarkers, specifically hippocampal and cerebral atrophy and PET amyloid burden; iii) cognitive decline. Background: With the aging of the world population, approximately 78 million people worldwide will be affected by a major neurocognitive disorder (NCD) in 2030, with up to 80% of cases being of AD type. However, its etiology remains unclear. Given the growing evidence supporting a relationship between vascular factors and AD, we explored this hypothesis using a novel artificial intelligence (AI) algorithm. Design/Methods: Of the 409 participants (42–95 years old) selected from the OASIS-3 database, 338 had a normal cognition, 53, a questionable NCD, 16, a mild NCD, and 2, a major NCD. Intracranial arteries were evaluated by time-of-flight magnetic resonance angiography (TOF-MRA) and their measurements were calculated using a newly developed AI system. Cognitive status was assessed by MMSE, CDR, and several neuropsychological tests, including Logical Memory and Trail Making tests. Amyloid burden was visualized by amyloid PET with contrast (Pittsburgh Compound B or Florbetapir) and quantified using the Centiloid scale. Brain and hippocampal volumes were assessed by T1-weighted MRI and the resulting images were processed by the Freesurfer image analysis suite. Results: The anterior choroidal arteries (AchA) had the most associations (nonlinear and negative) with cognitive functions and AD markers. Conversely, the anterior cerebral (ACA), basilar, and 1st segment of the posterior cerebral arteries (PCA-P1) had very little relationship with cognition. Among the statistically significant results, the correlations were of low magnitude. Conclusions: Using TOF-MRA imaging and AI, we were able to calculate the quantitative measurements of the intracranial arteries and thus assess their association with certain AD elements. Future research to further explore the vascular hypothesis of AD should be conducted in order to confirm these findings. Disclosure: Miss Ni has nothing to disclose. Dr. Janelle has nothing to disclose. Mr. Dumais has received personal compensation for serving as an employee of Imeka. Dr. Bocti has nothing to disclose. Kevin Whittingstall has nothing to disclose.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".