Supplementary Material for: Assessing Mild Cognitive Impairment in Parkinson’s Disease by Magnetic Resonance Quantitative Susceptibility Mapping Combined Voxel-Wise and Radiomic Analysis
Bibliographic record
Abstract
Background: The relationship between iron accumulation in the central nervous system and cognitive decline in Parkinson’s disease (PD) has not been fully elucidated. This study aimed to explore the value of quantitative susceptibility mapping in assessment of mild cognitive impairment (MCI) in PD. Methods: Sixteen PD patients with MCI (PD-MCI), sixteen normal cognition PD patients (PD-NC), and 28 healthy controls (HCs) were included. The differences in the magnetic susceptibility and Radiomic indicators among groups and their correlations with Montreal Cognitive Assessment-Basic (MoCA-B) scores and Unified Parkinson’s Disease Rating Scale Part III (UPDRS-III) were analyzed. Receiver operating characteristic curves were used to evaluate the diagnostic performance. Results: Higher iron deposition was observed in the cortical and subcortical structures of the PD patients compared with HCs, including limbic system, orbitofrontal cortex, cuneus, red nucleus, and substantia nigra. Combined magnetic susceptibility and texture index in hippocampus achieved the best diagnostic performance (area under curves: 0.828) in differentiating PD-MCI from PD-NC. The magnetic susceptibilities of the substantia nigra, red nucleus, putamen, globus pallidus, hippocampus, and thalamus were negatively correlated with the MoCA-B scores (all p < 0.05), and of the putamen and amygdala were positively correlated with the UPDRS-III scores (both p < 0.05). Conclusion: Higher iron deposition was observed in the cortical and subcortical structures of the PD-MCI and PD-NC groups. The susceptibility values of vulnerable brain subregions shown significant correlation with MoCA-B and UPDRS-III. Together with the texture index, magnetic susceptibility values could provide robust performance in distinguishing PD-MCI patients from PD-NC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.799 | 0.294 |
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".