Longitudinal Structural Voxel based Morphometry of 1.5 tesla MRI in early Parkinson’s disease
Bibliographic record
Abstract
Parkinson’s disease is a multisystemic, progressive neurodegenerative disorder characterized by the gradual loss of dopaminergic neurons in the substantia nigra, resulting in a wide range of motor and non-motor symptoms that extend beyond the classical features of rigidity, tremor, bradykinesia and postural instability. The incidence of Parkinson's disease is steadily increasing with demographic changes and requires biomarkers to detect the disease at an early stage in order to develop strategies for management with the disease and a form of therapy to be applied as early as possible. The aim of this thesis was to analyze a continuous reduction in grey matter volume in patients with newly diagnosed Parkinson's disease (DeNoVo) and healthy controls with structural voxel-based morphometry in vivo at baseline and after 2- and 4-year follow-up. Additionally, correlations between grey matter atrophy and clinical assessments such as the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Clock Drawing Test (CDT) and the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) were examined across all three time points (BL, FU1, FU2). As part of the longitudinal DeNoPa study, structural MRI examinations of the participants were conducted using a 1.5-Tesla MRI at the radiology clinic in Baunatal-Kassel. Using voxel-based morphometry based on 3D-T1 images, volumetric changes in grey matter were assessed and statistically analyzed in 216 participants, including 118 patients with Parkinson’s disease and 98 healthy controls. Over the course of the measurement time points, both Parkinson’s patients and healthy controls showed a significant reduction in grey matter and hippocampal volume. The findings suggest that the volume loss in Parkinson’s patients was more pronounced and consistently significant across a greater number of regions, including four areas of the frontal lobe, five areas of the parietal lobe, four areas of the temporal lobe, three areas of the occipital lobe as well as the left caudate nucleus. The significant correlations between grey matter volume reductions, particularly in the frontal lobe and hippocampus, and cognitive performance in the aforementioned tests in Parkinson's patients suggest that cognitive decline and neuropsychiatric symptoms occur in the early stages of the disease. The detection of non-motor symptoms is of particular importance as they significantly impair the quality of life of those affected, make every day functioning more difficult and contribute significantly to the burden on caregivers. The fact that the control group also showed a significant reduction of grey matter but no correlations with motor and cognitive tests, suggests that brain atrophy is subject to a physiological aging process. The results in the present study suggest that although continuous brain atrophy in grey matter is common in PD patients and a heterogeneous atrophy pattern seems to exist, it cannot yet be considered a specific disease pattern for PD. Overall, the data highlight the potential of imaging as a biomarker in combination with cognitive tests in the early stages of the Parkinson’s disease and a risk stratification tool for the emergence of cognitive decline as NMS and dementia in de novo Parkinson’s patients. These findings underscore the importance of a multimodal diagnostic approach in Parkinson's disease, particularly in the early stages, integrating complementary assessments to capture the heterogeneous nature of the disorder, with special attention to non-motor symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".