Quantitative research of diffusion tensor imaging in cognitive impairment of Parkinson's disease
Bibliographic record
Abstract
Objectives: To analyze the microstructural white matter changes of Parkinson's disease(PD) patients, study the value of diffusion tensor imaging(DTI) in the diagnosis of PD with cognitive impairment. Materials and Methods: Thirty PD patients were brought into research, including 18 patients with cognitive impairment(PD-CI) and 12 patients with cognitive normal(PD-CN), at the same time, 30 healthy volunteers were selected as the control group with gender, age, education matched. Conventional MRI and DTI examination were performed between two groups, HoehnYahr(H-Y) and montreal cognitive assessment(Mo CA) were evaluated before the MRI exam. On T2WI(b=0) image combined with DTI color coding graph, manually drawing the round region of interest to respectively measure the value of FA and ADC on substantia nigra, red nucleus, globus pallidus, putamen nucleus, the head of caudate nucleus, thalamus, frontal white matter, temporal white matter, parietal white matter, occipital white matter, and statistical analysis were conducted. Results: Compared with the healthy control group, the FA value of substantia nigra, putamen nucleus, the head of caudate nucleus, frontal white matter in PD patients obviously decrease(P0.05), the ADC value of frontal and temporal white matter is obviously increase(P0.05). There is statistical difference about the ADC value of frontal white matter between groups PD-CI and PD-CN(P0.05). The FA and ADC value on each part of patients group have no obvious correlation with H-Y and Mo CA scores(P0.05). Conclusions: The substantia nigra- striatum loop neurons of PD patients have widespread damage, the injury of frontal white matter is probably key factor of combined cognitive impairment of PD. The appraisal of the microstructural white matter changes by using DTI may be one of the important clues to early diagnosis and illness monitoring about Parkinson's disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".