Hippocampal‐to‐ventricle ratio (HVR) is better related to age and cognition than Hippocampal Volume
Bibliographic record
Abstract
Abstract Background Hippocampal volume (HCvol) is an important biomarker in the study of neurodegeneration (1‐3). Given its high variability (4) HCvol is commonly normalized using the intracranial volume (HCvol/ICV) (5). The Hippocampal‐to‐ventricle ratio (HVR) considers instead the nearby expanding ventricular space and has shown stronger negative association with age and positive association with delayed memory compared to HCvol in healthy aging (6). Method Using a Convolutional Neural Network (7) we extracted the volumes from the hippocampus and the surrounding temporal horn of the lateral ventricles to obtain the HCvol, HCvol/ICV and HVR on the baseline T1w MRI scans from the ADNI dataset (CN = 502, MCI = 814, AD = 323). We converted these measurements to Z‐scores using the mean and standard deviation of the CN cohort to facilitate comparisons. We used Pearson’s correlation coefficient to compare the relationships between each of the three Hippocampal measures with age, ADAS13, and the Immediate, Learning, Forgetting and Forgetting‐percentage metrics of the Rey’s Auditory Verbal Learning Test (RAVLT). Finally, we compared the correlations of HCvol/ICV and HVR with a bias‐corrected‐and‐accelerated bootstrap (BCa; 10,000 resamples) (8). Result The correlations of HCvol and HCvol/ICV were similar for age (‐.41 [‐.32, ‐.48] vs ‐.38 [‐.3, ‐.45]), ADAS13 (‐.28 [‐.19, ‐.45] vs ‐.26 [‐.17, ‐.45]) and RAVLT subtests (absolute values: .2 [0, .4] vs .2 [.01, .41]). HVR showed significantly stronger negative correlations with age compared to HCvol/ICV (Fig. 1; CN: BCa = .144 [.102‐.187]; MCI: BCa = .128 [.096‐.160]; AD: BCa = .068 [.012‐.122]). While the negative correlations between HVR and ADAS13 scores were higher than HCvol, the comparison was not statistically significant (Fig. 2). Figure 3 shows the relationships between HC measures and RAVLT subtests: HVR had stronger correlations than HCvol for CN and AD on the Immediate subtest, while HCvol’s relationship with Forgetting score was higher for MCI. None of these were statistically significant. Conclusion Considering ventricular expansion and hippocampal shrinkage in the HVR better explains the variance related to age and cognition than using hippocampal volume by itself in healthy controls and patients with mild cognitive impairment and dementia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".