Deep Learning-Based MRI Analysis Reveals Lewy Body Co-Pathology Accelerates Brain Aging in Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) and Lewy body (LB) pathology frequently co-occur. Recent advances in cerebrospinal fluid (CSF) α-synuclein seed amplification assays (SAA) enable in vivo detection of LB pathology, offering new opportunities to elucidate its combined effects with AD on neurodegeneration. We trained a deep learning model on multi-cohort MRI scans from 4,355 cognitively unimpaired individuals to estimate brain age and applied it to 803 cognitively impaired participants, who were classified into four AD/LB pathology subgroups using the p-tau181/Aβ42 ratio to specify AD pathology and SAA status to determine LB pathology. The co-pathology subgroup (AD+LB+) exhibited the most accelerated brain aging, with saliency maps revealing more pronounced neurodegeneration, aligning with its steeper longitudinal atrophy in various neuroanatomical regions and corresponding cognitive deficits. These findings underscore LB pathology’s synergistic role in amplifying AD-related neurodegeneration, highlighting the importance of combined biomarker assays and targeted interventions for individuals harboring co-existing AD and LB pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".