Piecing it together: Relationships between hippocampal subfields and cognitive impairment along the Alzheimer’s disease spectrum
Bibliographic record
Abstract
Abstract Background The ability to form hippocampal‐dependent memories declines steadily in normal aging, and exponentially in people with probable Alzheimer’s disease (AD). Correspondingly, the whole hippocampal formation follows the same trajectories of decline. Critically, pathological changes to distinguishable hippocampal subfields and related cognitive abilities are not well characterized in. Herein, we investigated how these hippocampal subfield and neuropsychological memory measures interact. Method Structural MRI and neuropsychological data were collected from 29 biomarker‐confirmed individuals on the AD spectrum (ADS) and a comparison group of 17 biomarker‐negative healthy adults to examine the interactions between subfield‐specific hippocampal integrity and cognitive markers. Cognitive assessments included the Wechsler Logical Memory (WMS‐IV) and Hopkins Verbal Learning (HVLT‐R) tests. High resolution hippocampal subfield volumes (CA1, CA2, CA3, dentate gyrus, and subiculum) were measured from a dedicated T2‐weighted MRI slab of the hippocampi (TSE; 0.4 x 0.4 x 2.0 mm), co‐registered to a routine T1‐weighted MRI (MPRAGE; 1.0 x 1.0 x 1.0 mm), using automated hippocampal subfield segmentation (ASHS) software and the UPENN atlas consisting of scans of MCI individuals and older adults (Yushkevich et al., 2015). We performed a cross‐sectional analysis of the relationship between hippocampal subfield volume and behavioral performance on measures of learning and declarative memory. Relationships between hippocampal subfields and neuropsychological subtests were probed using multivariate models contrasting ADS and controls, while controlling for age, education, and intracranial volume. Result We show that hippocampal subfield volumes robustly predict performance on subtests of WMS‐IV, but not HVLT‐R, regardless of group. Moreover, we report unique contributions of CA3 on learning, subiculum on recognition, and CA2 and CA3 on recall. By dividing the hippocampal formation into distinguishable subfields using ultra high‐resolution MRI, we further elucidate their contributions to ADS‐related neurocognitive declines in learning and memory. Conclusion Subfield‐specific atrophy in the human hippocampus may constitute a sensitive measure of neurocognitive decline in patients on the AD spectrum. These results suggest that the atrophy of specific hippocampal subfields selectively influence subprocesses of learning and memory, and that these measures may be used to better model disease trajectories among individuals on the Alzheimer’s disease spectrum.
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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 | 0.000 | 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 | 0.001 |
| 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".