Reliability of Changes in Brain Volume Determined by Longitudinal Voxel‐Based Morphometry
Bibliographic record
Abstract
BACKGROUND: Longitudinal magnetic resonance imaging (MRI) studies have become increasingly important to assess the changes in brain morphology during normal aging and neurodegenerative disorders. However, the reliability of longitudinal morphometric changes has not been fully evaluated. PURPOSE: To examine the reliability of longitudinal (2-year) changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) in healthy elderly subjects, patients with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). STUDY TYPE: Retrospective analysis. SUBJECTS: Twenty-four healthy elderly subjects, 28 MCI patients, and 16 AD patients. FIELD STRENGTH/SEQUENCE: A 1.5 T, magnetization-prepared rapid gradient-echo. ASSESSMENT: Longitudinal (2-year) changes in gray matter volume determined by longitudinal VBM processing, and visual assessment of image quality. STATISTICAL TESTS: Intraclass correlation coefficient (ICC) and Kruskal-Wallis test. RESULTS: The ICC maps differed among the three groups. The mean ICC was 0.81 overall (0.86 for healthy elderly subjects, 0.75 for MCI patients, and 0.76 for AD patients). The reliability was good to excellent (ICC, 0.60-1.00) for 92% of voxels (99% for healthy elderly subjects, 83% for MCI patients, and 83% for AD patients). The image quality differed significantly among the three groups (P < 0.05). DATA CONCLUSION: These results indicate that the reliability of longitudinal gray matter volume changes by VBM is good to excellent for most voxels. However, reliability may be affected by the disease, possibly due to differences in head motion during imaging. Evidence Level 3 Technical Efficacy Stage 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".