Multi‐contrast unbiased MRI template of a Parkinson’s disease population
Bibliographic record
Abstract
Abstract Background Parkinson’s disease (PD) is a complex neurodegenerative disorder affecting regions such as the substantia nigra (SN), red nucleus (RN) and locus coeruleus (LC). Processing MRI data from patients with PD requires anatomical structural references for spatial normalization and structural segmentation. Extending our previous work (Xiao et al., 2015/17), we present multi‐contrast unbiased MRI templates of a larger population of 172 subjects using more modalities: T1w, T2*w, T1‐T2* fusion, R2*map, T2w, PDw, fluid‐attenuated inversion recovery (FLAIR), improved susceptibility‐weighted imaging (CLEAR‐SWI) (Eckstein et al., 2021), and neuromelanin‐sensitive MRI (NM). Methods Subjects were recruited through the Quebec Parkinson’s Network since December 2018 and are included in a database of PD patients, RBD patients, and age‐ and sex‐ matched healthy controls, complete with MRI data, motor, and cognitive evaluation scores. All raw MRI was QC’d, eliminating scans with artefacts, motion, or noise, resulting in 172 subjects (69 female; ages=39‐87). Using our previous methods (Fonov et al., 2011), a non‐linear unbiased average T1‐T2* fusion template was created in stereotaxic space. For the other contrasts, the subject’s linear contrast‐to‐T1‐T2* transformation was concatenated with the subject’s T1‐T2* non‐linear stereotaxic transformation to avoid multiple resamplings before voxel‐wise averaging of all subject data in the common template space. Results Figure 1 depicts the whole brain 1mm template. Figure 2 depicts a closeup of the 0.3 mm template of the midbrain. From both figures, the first eight contrasts were created from 172 subjects: 145 PD, 1 RBD, and 26 controls. The NM contrast was created from 121 subjects (59 female; ages=39‐87): 99 PD, 1 RBD, and 21 controls. The T1‐T2* fusion maps provide improved deep grey matter contrast from T2*w images, while maintaining cortical structure information from T1w images. R2* maps, T2w, PD2, FLAIR, and CLEAR‐SWI contrasts highlight deep grey structures including SN, RN, subthalamic nucleus (STN), putamen, and globus pallidum, while the NM contrast highlights NM‐rich regions of SN and LC. Conclusion Anatomical structural references are useful for structural segmentation and morphometric analyses between healthy controls, PD, and RBD patients in regions such as SN, RN, and LC to aid in early diagnosis and better understand disease prognosis.
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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 | 0.001 |
| 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) | 0.003 | 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".