Amyloid accumulation and functional connectivity in normal aging and preclinical Alzheimer’s Disease: a longitudinal analysis of the OASIS‐3 cohort
Bibliographic record
Abstract
Abstract Background Pathological changes in the brain begin accumulating decades before the appearance of cognitive symptoms in Alzheimer’s Disease (AD). The deposition of amyloid‐beta (Aβ) proteins and other neurotoxic changes occur, leading to disruption in functional connections between brain networks. Discrete characterization of the changes that take place in preclinical AD has the potential to help treatment development by targeting the neuropathological mechanisms to prevent cognitive decline and dementia from occurring entirely. Previous research has focused on cross sectional differences in the brains of patients with mild cognitive impairment (MCI) or AD and healthy controls or has concentrated on the stages immediately preceding cognitive symptoms. The present study emphasizes early preclinical phases of neurodegeneration. Method We use a longitudinal approach to examine the brain changes that take place during early stages of cognitive decline in the OASIS‐3 dataset. Among 1098 participants, 274 passed the inclusion criteria (i.e. had at least two repeated measures of cognitive status and amyloid levels). We use mixed‐effect linear models to examine rates of amyloid accumulation in those who showed cognitive decline compared to those who did not change in cognitive status. Result Over 90% of participants were healthy at baseline. Over 8‐10 years, some participants progressed to very mild dementia (n = 48) while others stayed healthy (n = 226). Participants with cognitive decline have faster amyloid accumulation and greater increases in functional connectivity in similar brain regions, namely among lateral temporal, motor, and some lateral prefrontal cortex areas. These changes were linked to increases in functional connectivity of select default mode, frontoparietal and motor components. Conclusion Our findings advance the understanding of initial amyloid staging in preclinical Alzheimer’s, as well as contribute to the determination of the functional changes that occur among brain network components as the disease progresses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".