Metabolic brain networks in dementia with Lewy bodies: from prodromal to manifest disease stages
Bibliographic record
Abstract
BACKGROUND: F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive DLB biomarker. We evaluated a multivariate, quantifiable metabolic network biomarker, termed DLB-related pattern (DLBRP), for its further clinical translation across centres and disease stages. METHODS: We analysed demographic, clinical and FDG PET imaging data of 1180 participants from 14 tertiary centres and two multicentre datasets. We included 379 DLB, 28 mild cognitive impairment-LB (MCI-LB), 195 dementia due to Alzheimer's disease (ADD), 172 MCI-AD without α-synuclein co-pathology (MCI-AD-S-), and 73 MCI-AD with α-synuclein co-pathology (S+) patients, along with a comparative group of 333 normal controls (NCs). From the scans, we calculated the expression of DLBRP, AD-related pattern (ADRP) and Parkinson's disease-related pattern (PDRP) and compared them across groups. DLBRP scores were correlated with clinical measurements. RESULTS: Across independent cohorts, DLBRP robustly distinguished DLB from NCs (sensitivity >89%, specificity >90%), and scores correlated with Unified Parkinson's Disease Rating Scale Part III and independently predicted Mini-Mental State Examination. DLBRP was elevated already in MCI-LB. In a small longitudinal dataset, we observed steady increases in DLBRP expression with scores exceeding the diagnostic threshold prior to dementia onset. DLBRP and PDRP discriminated DLB from ADD (sensitivity, 74%-90%; specificity, 80%). In MCI-AD groups, ADRP was expressed, whereas DLBRP and PDRP were increased only in MCI-AD-S+, although comparatively less than in MCI-LB. CONCLUSIONS: This study demonstrates the value of DLBRP in diagnosing prodromal and manifest DLB and distinguishing them from their AD counterparts. While overlap between patterns may reflect actual co-pathology, this possibility cannot be accepted without thorough pathological confirmation. The current findings support the use of DLBRP in patient evaluation and in future trial design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".