Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy in Parkinson’s Disease
Bibliographic record
Abstract
At present, conventional magnetic resonance imaging (MRI) shows no convincing structural changes in Parkinson’s disease (PD) itself, but it may be useful in helping to distinguish PD from other neurodegenerative parkinsonian syndromes. Magnetic resonance spectroscopy (MRS) also may provide useful information in distinguishing PD from disorders such as multiple system atrophy. The general field of MRI and MRS is evolving rapidly, and a number of new developments may provide relevant information. Novel pulse sequences, for instance, may provide more information regarding substantia nigra pathology in PD. The use of MR technologies to measure regional concentrations of brain iron should provide more information regarding the relationship between iron accumulation and parkinsonian symptoms. MRS provides a sensitive tool to investigate the possible contribution of abnormal brain energy metabolism to the pathogenesis of PD. MRS also allows the assessment of other metabolite changes in PD, for example, providing for the evaluation of associated changes in regional brain glutamate content. Last, functional MRI provides the potential to evaluate, in a noninvasive fashion, the role played by the basal ganglia in motor control and cognition in normal individuals as well as in PD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".