Amide proton transfer could be a surrogate imaging marker for predicting vascular cognitive impairment
Bibliographic record
Abstract
BACKGROUD: Emerging evidence suggests an overlap in the underlying pathways contributing to both cerebral small vessel disease (CSVD) and the neurodegenerative disease. Studies investigating the progression of CSVD should incorporate markers that reflect neurodegenerative lesions. OBJECTIVE: We aim to investigate whether Amide proton transfer (APT) can serve as a potential marker for reflecting vascular cognitive impairment (VCI). METHOD: Participants were categorized into one of three groups based on their Montreal Cognitive Assessment (MoCA) scores: normal control group (age,54.9 ± 7.9; male, 52.9%), mild cognitive impairment (MCI) group (age,55.7 ± 6.9; male, 42.6%), or vascular dementia (VaD) group (age,57.6 ± 5.5, male, 58.5%). One way analysis of variance was performed to compare the demographic and APT variables between groups. Multiple logistic regression analysis wwas constructed to examine the relationship between APT values and VCI grouping. A hierarchical linear regression model was employed to examine the associations between patients' demographic factors, imaging markers, APT values, and MoCA. RESULTS: The APT values of frontal white matter, hippocampus, amygdala, and thalamus were significantly different among different groups (p < 0.05). The APT values of frontal white matter, amygdala, and thalamus indicate a significant positive effect on MCI grouping. the APT values of frontal white matter, amygdala, and thalamus indicate a significant positive effect on VaD grouping. The demographic data, CSVD imaging markers and APT values can account for 5.1%, 20.1% and 27.7% of the variation in MoCA, respectively. CONCLUSION: APT imaging can partially identifying and predicting the occurrence of VCI.
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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 | 0.009 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".