Quantitative susceptibility mapping reveals differences between subtypes of Lewy body dementia
Bibliographic record
Abstract
Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) are subtypes of Lewy body dementia, and are considered two ends of a disease spectrum. However, conventional MRI neuroimaging, mainly focused on grey matter volume and thickness, has failed to establish whether underlying processes differ between them. Understanding these differences could enable targeted and subtype-specific treatments to be developed. We applied quantitative susceptibility mapping (QSM), an advanced neuroimaging technique sensitive to tissue iron, to examine differences in tissue composition between these Lewy body dementia subtypes. We performed both voxel-wise and region of interest analyses to compare QSM values in 66 people with Lewy body dementia (45 DLB; 21 PDD), 86 people with Parkinson's disease with normal cognition (PD-NC) and 37 healthy controls. We also assessed relationships between QSM values and measures of both cognitive performance and overall disease severity in people with Lewy body dementia. We found that people with Lewy body dementia had higher QSM values in widespread brain regions, compared with cognitively normal people with PD, and that people with PDD had higher QSM values across many brain regions, compared with people with DLB. Further, we showed a positive relationship between QSM values and overall disease severity, measured using the Movement Disorder Society Unified Parkinson's Disease Rating Scale in people with Lewy body dementia, in the right thalamus, left pallidum, bilateral substantia nigra, bilateral middle frontal, temporal and lateral occipital lobes, right precentral and superior frontal cortices. In a region of interest analysis, we showed that people with PDD had higher QSM values than cognitively normal people with PD and controls in the substantia nigra pars reticulata. Our findings indicate neurobiological differences between subtypes of Lewy body dementia, that can be detected by exploiting QSM's sensitivity to tissue composition. Based on this, DLB and PDD could be considered as distinct conditions in the clinic and in clinical trials, and may respond to different treatments. Our finding that QSM values relate to real-world measures of overall disease severity in Lewy body dementia indicates its potential as an imaging biomarker for clinical trials of Lewy body dementia interventions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".