Mapping preclinical compensation in Parkinson's disease: An imaging genomics approach
Bibliographic record
Abstract
Mutations in the Parkin (PARK2) and PINK1 gene (PARK 6) can cause recessively inherited Parkinson's disease (PD). The presence of a single Parkin or PINK1 mutation is associated with a dopaminergic nigrostriatal dysfunction and conveys an increased risk to develop PD throughout lifetime. Therefore neuroimaging of non-manifesting individuals with a mutant Parkin or PINK1 allele opens up a window for the investigation of preclinical and very early phases of PD in vivo. Here we review how functional magnetic resonance imaging (fMRI) can be used to identify compensatory mechanisms that help to prevent development of overt disease. In two separate experiments, Parkin mutation carriers displayed stronger activation of rostral supplementary motor area (SMA) and right dorsal premotor cortex (PMd) during a simple motor sequence task and anterior cingulate motor area and left rostral PMd during internal movement selection as opposed to externally cued movements. The additional recruitment of the rostral SMA and right rostral PMd during the finger sequence task was also observed in a separate group of nonmanifesting mutation carriers with a single heterozygous PINK1 mutation. Because mutation carriers were not impaired at performing the task, the additional recruitment of motor cortical areas indicates a compensatory mechanism that effectively counteracts the nigrostriatal dysfunction. These first results warrant further studies that use these imaging genomics approach to tap into preclinical compensation of PD. Extensions of this line of research involve fMRI paradigms probing nonmotor brain functions. Additionally, the same fMRI paradigms should be applied to nonmanifesting mutation carriers in genes linked to autosomal dominant PD. This will help to determine how "generically" the human brain compensates for a preclinical dopaminergic dysfunction.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".