IC‐P‐083: MRI Patch‐Based Imaging Biomarker for Automatic Detection of Parkinson Disease
Bibliographic record
Abstract
Parkinson’s disease (PD) is a common neurodegenerative disorder affecting the motor system due to loss of dopaminergic neurons in the substantia nigra in addition to non-dopaminergic system degeneration mainly in the basal ganglia region. Finding sensitive biomarkers of PD would facilitate the clinical management, particularly in the early stages of disease. Sensitive biomarkers could also effectively help advance drug development for the condition. Recently, we have developed a method for early detection of Alzheimer’s disease based on the automatic analysis of T1w MRI scans. Here we adapt this method for detection of PD. The method is based on nonlocal patch-based algorithm, estimating anatomical patterns from a given subject in a specific brain region and comparing them with a database composed of healthy subjects and patients. The output of the method is a grading value showing similarity towards NC (-1) or PD (+1).We tested the method using the baseline 3T high resolution T1-weighted MRI scans obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data), corresponding to newly diagnosed PD patients (n=198) and an age-matched control group (n=93). We performed 10-fold cross-validation experiments and measured grading values for the substantia nigra (SN) and subthalamic nucleus (STN). The classifier performance was tested by estimating the Area Under the receiver operating characteristic Curve (AUC). Our results showed statistically significant differences in grading values for left SN (p=0.013) and left STN (p=0.0002) between patients and controls. When used to classify individual subjects in the cross-validation experiment the performance of the classifier yields an AUC=0.662 for the left SN, and AUC=0.733 for the left STN. We have shown that our method could be applied for detecting PD with AUC similar to Alzheimer Disease vs NC detection shown previously (AUC=0.73 for hippocampal grading in AD, Coupe et al, 2015). Midbrain area of the population-specific anatomical average of T1w scans from PPMI dataset with colour labels showing substantia nigra navy and turquoise) and subthalamic nucleus (green and yellow) used to calculate grading scores. Grading distribution across STN and SN. Receiver operating characteristic (ROC) curve for classification between PD and NC based on grading of each structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".