Voxel‐based quantitative susceptibility mapping in Parkinson's disease with mild cognitive impairment
Bibliographic record
Abstract
OBJECTIVE: Brain iron accumulation has been proposed as one of the pathomechanisms in Parkinson's disease (PD). This study aimed to examine the whole-brain pattern of iron accumulation associated with cognitive impairment in patients with PD using voxel-based quantitative susceptibility mapping analysis. METHODS: We enrolled 24 patients with PD and mild cognitive impairment, 22 patients with PD and normal cognition, and 20 age-matched healthy controls in this cross-sectional study. All participants underwent global cognitive and physical assessments and brain MRI. Using a combined method of voxel-based morphometry and quantitative susceptibility mapping, we compared the voxel-wise magnetic susceptibility of the whole brain between the groups and analyzed its correlation with the cognitive and behavioral data. RESULTS: The PD and mild cognitive impairment group had lower Montreal Cognitive Assessment (MoCA) score than the PD and normal cognition and healthy control groups. There were no gray matter volumetric differences between the groups. In contrast, the voxel-based quantitative susceptibility mapping analysis showed that the PD and mild cognitive impairment group had significantly higher quantitative susceptibility mapping values in the cuneus, precuneus, caudate head, fusiform gyrus, and orbitofrontal cortex than did the PD and normal cognition group. These quantitative susceptibility mapping values were negatively correlated with the MoCA scores in the PD patients (cuneus: r = -0.510, P < .001; caudate head: r = -0.458, P = 0.002). CONCLUSIONS: This study suggests that cognitive impairment in PD is associated with cerebral iron burden and highlights the potential of quantitative susceptibility mapping as an auxiliary biomarker for early evaluation of cognitive decline in patients with PD. © 2019 International Parkinson and Movement Disorder Society.
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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 | 0.002 |
| 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".