The Italian Alzheimer's Disease Neuroimaging Initiative (I-ADNI): Validation of Structural MR Imaging
Bibliographic record
Abstract
BACKGROUND: The North American Alzheimer's Disease Neuroimaging Initiative (NA-ADNI) was the first program to develop standardized procedures for Alzheimer's disease (AD) imaging biomarker collection. OBJECTIVE: We describe the validation of acquisition and processing of structural magnetic resonance imaging (MRI) in different Italian academic AD clinics following NA-ADNI procedures. METHODS: 373 patients with subjective memory impairment (n = 12), mild cognitive impairment (n = 92), Alzheimer's dementia (n = 253), and frontotemporal dementia (n = 16) were enrolled in 9 Italian centers. 22 cognitively healthy elderly controls were also included. MRI site qualification and MP-RAGE quality assessment was applied following the NA-ADNI procedures. Indices of validity were: (i) NA-ADNI phantom's signal-to-noise and contrast-to-noise ratio, (ii) proportion of images passing quality control, (iii) comparability of automated intracranial volume (ICV) estimates across scanners, and (iv) known-group validity of manual hippocampal volumetry. RESULTS: Results on Phantom and Volunteers scans showed that I-ADNI acquisition parameters were comparable with those one of the ranked-A ADNI scans. Eighty-seven percent of I-ADNI MPRAGE images were ranked of high quality in comparison of 69% of NA-ADNI. ICV showed homogeneous variances across scanners except for Siemens scanners at 3.0 Tesla (p = 0.039). A significant difference in hippocampal volume was found between AD and controls on 1.5 Tesla scans (p < 0.001), confirming known group validity test. CONCLUSION: This study has provided standardization of MRI acquisition and imaging marker collection across different Italian clinical units and equipment. This is a mandatory step to the implementation of imaging biomarkers in clinical routine for early and differential diagnosis.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".