Thalamus anatomy predicts cognitive performance and hippocampal atrophy in aging adults: a UK Biobank study
Bibliographic record
Abstract
Abstract The thalamus has extensive inter-connectedness with different brain regions in serving cognitive processes. In a community-dwelling aging population from the United Kingdom, this study examined the independent contribution of thalamus volume loss to cognitive performances and the longitudinal anatomical relationship between the thalamus and the interconnected hippocampus. We accessed MRI data from 4348 cognitively unimpaired older adults from the UK Biobank, of whom 653 participants had follow-up MRI. We estimated regional brain volumes using T1-weighted MRI. Linear models tested the association between the thalamus volume and a cognitive composite score derived from digit-symbol substitution and trail-making tests. We used latent change score models to test the longitudinal associations between thalamus volume at baseline and the trajectory of hippocampal atrophy, and vice versa. Baseline thalamus volume was positively associated with the cognitive composite score (β=0.055±0.018, P = 0.002, R2 = 0.09). A larger baseline thalamus volume predicted slower hippocampal atrophy (γT→dH=−0.048±0.015,P=0.001,R2=0.09), while larger hippocampal volume at baseline predicted faster thalamic atrophy (γH→dT=0.043±0.022,P=0.048,R2=0.04). Sex-stratified analysis revealed that hippocampal volume significantly predicted thalamic atrophy only in women. This study revealed that thalamic volume loss was associated with impaired processing speed and executive function. Thalamus and hippocampus anatomy showed bidirectional longitudinal associations and demonstrated sex differences. These findings underscore the thalamus anatomy as an important marker of brain health in the aging population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".