Contrast Sensitivity Predicts 30-month Functional Brain Network Integrity in Cognitively Unimpaired Older Adults: the Brain Networks and Mobility Study
Bibliographic record
Abstract
BACKGROUND: Visual contrast sensitivity (CS) is critical to many functions in older adults and is associated with brain network community structure, but the direction of the relationship between CS and the brain remains unclear. METHODS: We evaluated whether baseline binocular CS predicts 30-month functional brain network organization in 172 community-dwelling older adults (mean age 76.4 ± 4.8 years, 56.4% female, 11.6% non-White/Hispanic) that underwent functional MRI at rest and during a motor imagery task. We constructed separate distance regression models for each of the 8 subnetworks covering the entire brain, while controlling for the baseline brain networks, sex, and the number of volumes removed during motion scrubbing from head motion in the scanner. RESULTS: Worse baseline CS predicted lower community structure integrity at 30 months in the visual network (β = 0.0115; p < .0001), dorsal attention network (β = 0.0075; p = .0089), and default mode network both at rest (β = 0.0173; p < .0001) and during the motor imagery task (default mode network, β = 0.0103; p = .0002). No other networks showed significant associations. The dorsal attention network did not have a relationship with CS at baseline but was significant at 30 months. Similar findings were observed in models that additionally controlled for baseline Montreal Cognitive Assessment and change in Montreal Cognitive Assessment score over 30 months. CONCLUSIONS: Poor CS may identify a subset of older adults at risk of future decrements in brain circuits important for vision, cognitive, and mobility functions. Future studies should explore if improving CS increases functional brain health.
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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 | 0.003 |
| 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 | 0.001 |
| 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".