The role of high‐field magnetic resonance imaging in parkinsonian disorders: Pushing the boundaries forward
Bibliographic record
Abstract
Historically, magnetic resonance imaging (MRI) has contributed little to the study of Parkinson's disease (PD), but modern MRI approaches have unveiled several complementary markers that are useful for research and clinical applications. Iron- and neuromelanin-sensitive MRI detect qualitative changes in the substantia nigra. Quantitative MRI markers can be derived from diffusion weighted and iron-sensitive imaging or volumetry. Functional brain alterations at rest or during task performance have been captured with functional and arterial spin labeling perfusion MRI. These markers are useful for the diagnosis of PD and atypical parkinsonism, to track disease progression from the premotor stages of these diseases and to better understand the neurobiological basis of clinical deficits. A current research goal using MRI is to generate time-dependent models of the evolution of PD biomarkers that can help understand neurodegeneration and provide reliable markers for therapeutic trials. This article reviews recent advances in MRI biomarker research at high-field (3T) and ultra high field-imaging (7T) in PD and atypical parkinsonism. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".