Associations between amyloid‐β, white matter disease, functional brain networks, and mobility function: Possible indicators of reserve and resilience
Bibliographic record
Abstract
Abstract Background Undetected AD pathology in cognitively normal older adults may increase risk for mobility decline by both increasing white matter pathology and directly interfering with functional networks through Aβ deposition. Methods Thirty‐one cognitively normal older adults (75.01 ± 4.15 y, 35.5% female) enrolled in the Brain Networks and Mobility Function (B‐NET) study received PiB PET and MRI. A global cortical PiB average for each participant was calculated by coregistering PET to MRI using Freesurfer v5.6 to generate masks and thresholding at 1.21 to create PiB positivity groups (PiB‐ = 16, PiB+ = 15). White matter hyperintensity (WMH) volume was calculated with the Lesion Segmentation Toolbox (LST) implemented in SPM12. Mobility function was assessed using the expanded Short Physical Performance Battery (eSPPB), a 4‐meter walk, and a 400‐meter walk. Cognitive measures included the Montreal Cognitive Assessment (MOCA) and Digit Symbol Substitution Task (DSST). Associations were explored using linear regression, correcting for age, sex, and BMI in the fully adjusted mobility model while age, sex, and education were used in the fully adjusted cognition and WMH model. Results PiB+ individuals had significantly faster 4‐meter gait speed (p<0.05) and a trend for higher DSST (p=0.052). These associations were independent of WMH volume. PiB+ individuals also had significantly higher total (p<0.05) and motor WMH (p<0.05). Conclusions The observation that PiB+ individuals had better physical function than PiB‐ was unexpected given existing literature showing associations between regional Aβ volume and slower gait speed. It was also unexpected given that the PiB+ group had higher WMH volume, which is associated with slower gait speed. The current sample, which includes primarily individuals with good physical and cognitive function, may represent a resilient phenotype. Continued recruitment of more diverse participants with lower physical function will be an important addition to this sample. Future analyses will allow for the inclusion of APOE and an increased sample size. Whole‐brain functional connectivity will also be assessed in this cohort using graph‐theory based methods.
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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 | 0.001 |
| Research integrity | 0.000 | 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".