White matter lesion load is associated with resting state functional MRI activity and amyloid pet but not FDG in mild cognitive impairment and early alzheimer's disease patients
Bibliographic record
Abstract
PURPOSE: To quantify and investigate the interactions between multimodal MRI/positron emission tomography (PET) imaging metrics in elderly patients with early Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls. MATERIALS AND METHODS: Thirteen early AD, 17 MCI patients, and 14 age-matched healthy aging controls from the Alzheimer's Disease Neuroimaging Initiative database were selected based on availability of data. Default mode network (DMN) functional connectivity and fractional amplitude of low frequency fluctuation (fALFF) were obtained for resting state functional MRI (RS-fMRI). White matter lesion load (WMLL) was quantified from MRI T2-weighted FLAIR images. Amyloid deposition with PET [(18)F]-Florbetapir tracer and metabolism of glucose by means of [(18)F]-fluoro-2-deoxyglucose (FDG) images were quantified using ratio of standard uptake values (rSUV). RESULTS: Whole-brain WMLL and amyloid deposition were significantly higher (P < 0.005) in MCI and AD patients compared with controls. RS-fMRI results showed significantly reduced (corrected P < 0.05) DMN connectivity and altered fALFF activity in both MCI and AD groups. FDG uptake results showed hypometabolism in AD and MCI patients compared with controls. Correlations (P < 0.05) were found between WMLL and amyloid load, FDG uptake and amyloid load, as well as between amyloid load (rSUV) and fALFF. CONCLUSION: Our quantitative results of four MRI and PET imaging metrics (fALFF/DMN, WMLL, amyloid, and FDG rSUV values) agree with published values. Significant correlations between MRI metrics, including WMLL/functional activity and PET amyloid load suggest the potential of MRI and PET-based biomarkers for early detection of AD.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".