Diffusion Tensor Imaging in Idiopathic Parkinson's Disease and Multisystem Atrophy (Parkinsonian Type)
Bibliographic record
Abstract
BACKGROUND: Differentiation between Parkinson's disease (PD) and atypical Parkinson syndromes (AP) is usually based on clinical examination, but can be challenging especially at early stages of the diseases. Diffusion tensor imaging (DTI) allows for differentiation between PD and AP with good specificity. It is a promising tool for clinical application, but has not been elaborated completely with respect to methodology and validity. OBJECTIVE: In this study we evaluated differences of the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) within white brain matter between patients with PD and multisystem atrophy of the parkinsonian type (MSAp). MATERIALS AND METHODS: DTI data of 9 PD and 9 MSAp patients were compared by means of a hypothesis-free whole-brain analysis algorithm (TBSS) focusing on changes within white matter. RESULTS: We found significantly higher values of the ADC in the MSAp group in the anterior limb of the inner capsule, superior parts of the corona radiata, and lateral periputaminal white matter. Group differences in FA values were not significant. CONCLUSION: Changes of the ADC close to the putamen proved most consistent and seem to be promising for the ongoing clinical implementation of DTI for the differentiation of hypokinetic-rigid movement disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".