Multi-tracer PET correlation analysis reveals disease-specific patterns in Parkinson’s disease and asymptomatic LRRK2 pathogenic variant carriers compared to healthy controls
Bibliographic record
Abstract
Several genetic pathogenic variants increase the risk of Parkinson's disease (PD) with pathogenic variants in the leucine-rich repeat kinase 2 (LRRK2) gene being among the most common. A joint pattern analysis based on multi-set canonical correlation analysis (MCCA) was utilized to extract PD and LRRK2 pathogenic variant-specific spatial patterns in relation to healthy controls (HCs) from multi-tracer Positron Emission Tomography (PET) data. Spatial patterns were extracted for individual subject cohorts, as well as for pooled subject cohorts, to explore whether complementary spatial patterns of dopaminergic denervation are different in the asymptomatic and symptomatic stages of PD. The MCCA results are also compared to the traditional univariate analysis, which serves as a reference. We identified PD-induced spatial distribution alterations common to DAT and VMAT2 in both asymptomatic LRRK2 pathogenic variant carriers and PD subjects. The inclusion of HCs in the analysis demonstrated that the dominant common PD-induced pattern is related to an overall dopaminergic terminal density denervation, followed by asymmetry and rostro-caudal gradient with deficits in the less affected side still being the best marker of disease progression. The analysis was able to capture a trend towards PD-related patterns in the LRRK2 pathogenic variant carrier cohort with increasing age in line with the known increased risk of this patient cohort to develop PD as they age. The advantage of this method thus resides in its ability to identify not only regional differences in tracer binding between groups, but also common disease-related alterations in the spatial distribution patterns of tracer binding, thus potentially capturing more complex aspects of disease induced alterations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".