Cortical complexity alterations in motor subtypes of Parkinson's disease: A surface‐based morphometry analysis of fractal dimension
Bibliographic record
Abstract
Based on motor symptoms, Parkinson's disease (PD) can be classified into tremor dominant (TD) and postural instability gait difficulty (PIGD) subtypes. Few studies have examined cortical complexity differences in PD motor subtypes. This study aimed to investigate differences in cortical complexity and grey matter volume (GMV) between TD and PIGD. We enrolled 36 TD patients, 27 PIGD patients and 66 healthy controls (HC) from the PPMI (Parkinson's Progression Markers Initiative) database. Voxel-based morphometry (VBM) and surface-based morphometry (SBM) were utilized to assess differences in GMV, cortical thickness and cortical complexity. The structural MRI data of participants was analysed using CAT12/SPM12 (p < 0.05, FDR corrected). Additionally, correlations between clinical data and structural changes were examined (p < 0.05, Holm-Bonferroni corrected). In comparison to both HC and TD groups, PIGD patients exhibited a significant fractal dimension (FD) decrease in many cortical regions. A significant negative correlation between age and FD was observed in the left insula for the PIGD patients and in the bilateral insula for the TD patients. However, no significant differences were found in GMV, cortical thickness or other complexity indices. Altered FD in the bilateral insula indicates that postural instability and gait disturbances may result from a failure to integrate information from various structures, whereas parkinsonian rest tremor is not associated with this integration. Also, widespread decreases in cortical FD demonstrate that FD is more sensitive than other complexity measures and can serve as a novel biomarker for identifying subtle changes in cortical morphology in the PIGD subtype.
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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 | 0.002 | 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) | 0.001 | 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".