Morphometry of medial temporal lobe subregions using high‐resolution T2‐weighted MRI in ADNI3: Why, how, and what's next?
Bibliographic record
Abstract
This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high-resolution T2-weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface-based tools to characterize MTL neurodegeneration across biomarker-defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high-resolution imaging of the MTL in ADNI Phase 4. HIGHLIGHTS: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high-resolution T2-weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions. These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements. More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface-based modeling techniques. Surface-based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria. These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".