Brain structural network changes in Parkinson's disease with mild cognitive impairment: a diffusion tensor imaging study
Bibliographic record
Abstract
Objective To explore the changes of brain structure network in patients with Parkinson's disease with mild cognitive impairment(PD-MCI), and to provide novel markers for the early recognition of PD-MCI. Methods Total 47 patients with primary PD were continuously enrolled at the Department of Neurology, Affiliated Hospital of Xuzhou Medical University from May 2017 to May 2018.Twenty-four healthy volunteers were selected as the healthy control (HC) group.General demographic data were collected from all subjects. The overall cognitive function and the five cognitive domains (attention and working memory, executive function, language, memory and visual spatial function) were comprehensively evaluated, then the patients with PD were further divided into PD-MCI group(n=22) and PD-NC group(n=25) based on their cognitive evaluation results.All subjects completed the diffusion tensor imaging(DTI) scan and the individual structural brain connection was obtained by deterministic diffusion-tensor tractography.By graph theory analysis technology, the network properties (global properties and node properties) and edge-wise distributions were compared to assess structural connectivity differences among the three groups. Results Compared with the PD-NC and HC groups, the PD-MCI group had a larger characteristic path length(PD-NC: (8.33±0.95), HC: (8.18±1.35), PD-MCI: (9.20±1.52), F=4.14, P<0.05) and reduced global efficiency (PD-NC: (0.13±0.05), HC: (0.13±0.04), PD-MCI: (0.10±0.04), F=3.73, P<0.05) in addition to a lower nodal efficiency in frontoparietal areas, thalamus, cingulate gyrus, and insular(P=0 .003-0.040, FDR correction). Compared with the HC group, the PD-MCI group had a large frontotemporoparietal areas, basal ganglia, and insular network with decreased connection strength (P<0.05, FDR correction). Compared with the PD-NC group, the PD-MCI group had a lower network connection strength in the frontoparietal areas (P<0.05, FDR correction). Conclusion A disruption of structural connections that make up the brain network can lead to changes in information integration and delivery, leading to cognitive dysfunction in patients with PD.The distribution pattern of brain network structure connection changes is expected to become a new marker for identifying PD-MCI. Key words: Parkinson's disease; Mild cognitive impairment; Structural networks; Graph theory; Diffusion tensor image
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".