Comparing Macro‐ and Micro‐structural Predictors of Subsequent Cognitive Impairment in the BLSA
Bibliographic record
Abstract
BACKGROUND: Neuroimaging biomarkers offer valuable insights into the development of MCI or dementia. Recent evidence from the Baltimore Longitudinal Study of Aging (BLSA) Neuroimaging cohort suggests that differences in structural and functional changes over time may provide reliable markers of progression, particularly in subsequently impaired (SI) older adults. However, further research is essential to identify the most sensitive markers of SI during the preclinical stages. This study investigates MRI-based macro- and micro-structural predictors that distinguish SI from cognitively normal (CN) older adults. METHOD: The cohort included 509 CN BLSA participants aged 50+ who had longitudinal cognitive assessments, including adjudication for cognitive status, and 3T MRI scans. MRI-based metrics included DTI parameters (FA, MD, RD and AD) for white matter (WM) integrity, cortical thickness, regional volumes, and machine learning-derived atrophy scores. A total of 154 MRI-based biomarker ROIs were examined (Table 1). Of the 509 CN, 80 individuals developed SI during follow-up (median time to SI: 4.6 years). Linear mixed-effects models were used to examine the associations between cognitive status and longitudinal MRI biomarkers, adjusting for baseline age, sex, education years, APOE e4 status (APOE e4 carrier vs. non-carrier), and race. Models with regional volumes were also adjusted for ICV at age 70. RESULT: Table 2 lists the baseline demographic and clinical characteristics for CN and SI individuals. Longitudinal analyses revealed significantly faster declines in DTI WM tract measures and cortical thickness in SI compared to CN individuals, with SI males primarily driving the WM changes in commissural and association tracts and SI females showing greater atrophy in occipital regions (Figure 1). CONCLUSION: The observed findings emphasize the utility of selected DTI and cortical thickness measures as sensitive markers of early changes in brain integrity. Further, we found sex-specific patterns in trajectories associated with cognitive status, with SI females showing more pronounced macrostructural brain changes in cortical regions and SI males exhibiting more significant changes in WM microstructural integrity. Our study highlights the importance of sex-stratified analyses in identifying early brain changes that predict later cognitive impairment.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".