Evaluating Spatial Filtering on Diffusion MRI Data Harmonization in Parkinsonism
Bibliographic record
Abstract
Background: Parkinsonism is an umbrella term encompassing several disease pathologies that share common motor symptoms. The most prevalent diagnosis is Parkinson’s disease, followed by multiple system atrophy, and progressive supranuclear palsy. Early detection and differentiation between types of Parkinsonism remain an issue in clinical practice.Objective: MRI has the potential to aid the diagnosis of Parkinsonisms. A major hurdle is combining and harmonizing the data across different MRI vendors. The objective of this study was to determine if a full width half maximum gaussian spatial filter helps harmonize data sets collected from different scanners.Methods: Using 17 different MRI scanners, data was collected from 1,002 subjects. First, the data were spatially filtered using different sizes (no filter, 2mm, 4mm, 6mm). Data were then preprocessed and transformed into Montreal Neurological Institute (MNI) space. Next, support vector machine learning tested the training and validation accuracy of predicting diagnosis at each spatial filter setting.Results: The training and validation data for weighted sensitivity, specificity, and accuracy were similar for all filter conditions. Differences between the weighted sensitivity, specificity, and accuracy of the training groups for all filter sizes were less than 0.1 and less than 0.2 for validation groups.Conclusions: Training and validation predictions did not differ across spatial filters, suggesting the accuracy of the algorithm is robust at different spatial filter sizes. In conclusion, the size of the spatial filter applied to diffusion MRI data does not result in a change in the outcome of the machine learning approach.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".