APPLICATIONS OF FUNCTIONAL MAGNETIC RESONANCE IMAGING IN THE EARLY DIAGNOSIS OF PARKINSON DISEASE: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Introduction: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms that typically emerge after extensive dopaminergic neuronal loss. Functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for identifying early neural dysfunctions preceding overt clinical manifestations. Objective: To systematically evaluate the evidence regarding the diagnostic utility of fMRI in detecting early or prodromal Parkinson disease, highlighting the main paradigms, analytical methods, and biomarkers associated with altered brain connectivity and activity patterns. Methods: Searches were conducted in PubMed, Scopus, Web of Science, Cochrane Library, LILACS, ClinicalTrials.gov, and ICTRP. Studies published from 2019 to 2025 investigating the use of task-based or resting-state fMRI in early, prodromal, or de novo PD were included. Data extraction followed PRISMA guidelines. Methodological quality was assessed with the Newcastle-Ottawa Scale for observational studies and QUADAS-2 for diagnostic accuracy. Results and Discussion: 24 studies met the eligibility criteria. Altered functional connectivity was consistently observed within the basal ganglia–thalamocortical circuit, default mode network, and cerebellar regions. Machine-learning models using fMRI-based biomarkers achieved diagnostic accuracies between 82 % and 95 % in distinguishing early PD from healthy controls. However, heterogeneity of acquisition parameters and analytic pipelines limited direct comparability across studies. Conclusion: fMRI provides sensitive markers of early neuronal dysfunction in Parkinson disease and holds promise as a non-invasive adjunct to clinical and molecular biomarkers. Standardization of protocols and longitudinal validation are required before routine clinical implementation.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".