Microstructural Hippocampal Alterations in Alzheimer's Disease: A Systematic Review and Meta‐Analysis of Diffusion Kurtosis Imaging
Bibliographic record
Abstract
BACKGROUND: The hippocampus is highly vulnerable in Alzheimer's disease (AD), with early microstructural changes potentially detectable via diffusion kurtosis imaging (DKI). Previous studies report promising DKI findings in AD, necessitating systematic evaluation. To compare hippocampal DKI metrics, particularly mean kurtosis (MK), between AD patients and healthy controls (HCs) and explore factors influencing these differences. METHODS: for heterogeneity, and Egger's/Begg's tests for publication bias (significance: p < 0.05). RESULTS: Ten studies (215 AD patients, 217 HCs; mean age: 65-75 years) using 3.0 T MRI were included. Eight articles were included in the meta-analysis to compare MK between groups. AD patients exhibited significantly reduced bilateral hippocampal MK compared to HCs (left: SMD = -1.32, 95% confidence interval [95% CI] [-1.97 to -0.66]; right: SMD = -1.22 [-1.88 to -0.56]; both p < 0.001), indicating compromised microstructural complexity. Subgroup analyses revealed more pronounced MK reductions in studies with higher male ratios (>42%; left: SMD = -1.87; right: SMD = -1.91; p < 0.05). Age, echo time, repetition time, and diffusion directions did not significantly influence effect sizes. Sensitivity analyses confirmed robustness, and publication bias was detected, but trim-and-fill analyses revealed no missing studies. CONCLUSION: Reduced hippocampal MK in AD reflects microstructural degeneration, with sex-related differences in effect magnitude.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".