White matter damage in patients with mild cognitive impairment in Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND: This study investigates the correlation between white matter structural damage and cognitive impairment in patients with Parkinson's disease (PD) and mild cognitive impairment (MCI). METHODS: A total of 40 patients with PD were divided into two groups, i.e., a mild cognitive impairment (PD-MCI) and a normal cognitive (PDN) group, and 20 healthy patients were enrolled as the control group. Changes in the white matter structure of patients with PD were evaluated using diffusion tensor imaging (DTI), and cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) scale and Mini-Mental State Examination (MMSE). Finally, the correlations between the two groups were analyzed. RESULTS: The Unified Parkinson's Disease Rating Scale score was significantly higher in the PD-MCI than in the PDN group (P=0.008). The total MoCA and MMSE scores in the PD-MCI group were significantly lower than in the PDN and control groups (P<0.01). Patients in PDMCI group were tested by MMSE scale, and the abnormal score rate was 60.0%. Among them, 8 PD patients with normal MMSE total score were found to have mild cognitive impairment by MoCA evaluation. When compared with the PDN and control groups, the MoCA scores for visual space, which is assessed as part of the MoCA scale and generally represents bilateral parietal function; naming; memory; and attention were significantly lower in the PD-MCI group (P<0.001). When compared with the PDN group, the fractional anisotropy (FA) values for the right parietal and left occipital lobes were significantly lower in the PD-MCI group (P=0.005; P=0.018). The relationship between MoCA value and right parietal white matter and left occipital white matter was 0.555, 0.474, respectively. A Pearson's correlation test was conducted to compare the FA values and MoCA scores of the various brain areas in the PD-MCI group and revealed a significant positive correlation between the MoCA score and the white matter of the right parietal and left occipital lobes (P<0.01). CONCLUSIONS: Patients with PD experience early cognitive impairment, and the MoCA scale can be used for early screening. In addition, the DTI of white matter can highlight early white matter damage. In the current study, the damaged brain areas displayed by DTI were consistent with areas showing decreased MoCA scores and were positively correlated with the severity of PD.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".