Cognitive-and lifestyle-related microstructural variation in the ageing human hippocampus
Bibliographic record
Abstract
Abstract Ageing is a biological process associated with the natural degeneration of various regions of the brain. Alteration of neural tissue in the hippocampus with ageing typically results in cognitive decline that may serve as a risk factor for dementia and other neurodegenerative diseases. Modifiable lifestyle factors may help preserve hippocampal neural tissue (microstructure) and slow down neurodegeneration and thus promote healthy cognition in old age. In this study, we sought to identify potential modifiable lifestyle factors that may help preserve microstructure in the hippocampus. We used data from 494 subjects (36-100 years old) without clinical cognitive impairment from the Human Connectome Project-Aging study. We estimated hippocampal microstructure using myelin-sensitive (T1w/T2w ratio), inflammation-sensitive (MD) and fibre-sensitive (FA) MRI markers. Non-negative matrix factorization was used to integrate the signals of these images into a multivariate spatial signature of microstructure covariance across the hippocampus. The associations between hippocampal microstructural patterns and lifestyle factors & cognition were identified using partial least squares analysis. Our results reveal that the preservation of axon density and myelin in regions corresponding to subicular regions and CA1 to CA3 regions of the hippocampi of younger adults is associated with improved performance in executive function tasks, however, this is also associated with a decreased performance in memory tasks. We also show that microstructure is preserved across the hippocampus when there is normal hearing levels, physical fitness and normal insulin levels in younger adults of our study even in the presence of cardiovascular risk factors like high body mass index, blood pressure, triglycerides and blood glucose known to be associated with hippocampal neurodegeneration. This preservation is not observed in older adults when there are no normal levels of insulin, physical fitness and hearing. Taken together, our results suggest that certain lifestyle factors like normal hearing, physical fitness and normal insulin levels may help preserve hippocampal microstructure which may be useful in maintaining optimum performance on executive function tasks and potentially other modes of cognition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".