Brain size links neurodevelopment to neurodegeneration in Parkinson's disease
Bibliographic record
Abstract
Genetic studies have advanced our understanding of Parkinson's disease (PD) pathogenesis, establishing a role for autophagy and lysosomal and mitochondrial dysfunction. However, how genetic risk translates into neuronal vulnerability remains mostly unknown. Using recent genome-wide association studies and neuroimaging data from 25,000 participants (age 44 to 85) in UK Biobank, we show that higher polygenic risk score (PRS) of PD correlates with greater cortical surface area, white matter fractional anisotropy and subcortical volumes. Mendelian randomization supports a causal relation from increased brain size to PD, and cortical regions showing the greatest polygenic expansion in surface area show the greatest atrophy in PD. Consistent with a genetically mediated increase in cortical surface area, we also find that PD polygenic risk score correlates with greater educational attainment, socioeconomic status, and other cognitive traits. Lifespan gene expression and pathway-specific analyses identify mitochondrial, autophagy, lysosomal, and neurodevelopmental pathways as separate mechanisms of genetic vulnerability. We show that a portion of PD susceptibility originates from neurodevelopmental processes that regulate neuronal proliferation. In further support of a neurodevelopmental mechanism of genetic risk, we also observed an association between PD PRS and increased cortical and subcortical size in 9-11 year olds from the ABCD cohort. Genetically-determined increases in brain size confer vulnerability to PD in later life, supporting the existence of shared neurobiological pathways between brain development and neurodegeneration.
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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".