Comparisons Between Alzheimer Disease, Frontotemporal Lobar Degeneration, and Normal Aging With Brain Mapping
Bibliographic record
Abstract
Alzheimer disease (AD) and frontotemporal lobar degeneration (FTLD) are both common degenerative dementias in the under 65 age group. Although clinical criteria have been defined for both diseases, there is considerable overlap in clinical features, and hence, diagnosis still can be very difficult particularly in the early stages of the disease. As a result, there has been increasing interest in using magnetic resonance imaging to better characterize these diseases and to aid in diagnosis. Voxel-based morphometry (VBM) is an automated technique that assesses patterns of regional gray matter atrophy on magnetic resonance imaging between 2 groups of subjects. It is unbiased in that it looks throughout the whole brain and does not require any a priori assumptions concerning which structures to assess, giving it a significant advantage over traditional region of interest-based methods. Voxel-based morphometry has been widely used to assess patterns of regional atrophy in subjects with AD and FTLD. These studies have demonstrated specific patterns of regional loss in both diseases, compared the 2 diseases to look for differences that could be diagnostically useful, and have correlated regions of gray matter loss to cognitive and behavioral deficits in these subjects. This article will review the findings of these studies and discuss the role of VBM in these neurodegenerative diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".