Identifying altered resting state network connectivity in Parkinson’s disease with mild cognitive impairment
Bibliographic record
Abstract
Objective: To investigate brain connectivity associated with Parkinson’s disease patient with mild cognitive impairment during resting-state fMRI Background: Cognitive disturbances in Parkinson’s disease (PD) is one of the factors that detrimentally impacts the patient’s quality of life and contributes to a high disease burden. A quarter of PD patients experience cognitive disturbances when diagnosed with PD and ultimately develop dementia (Hely, Reid et al. 2008). Mild cognitive impairment (MCI) is common in PD and is a prodromal state of dementia. This study aimed to investigate brain connectivity associated with PD-MCI during resting-state fMRI. This may extend understanding of the neurobiology of pre-clinical dementia in PD. Method: 14 PD-MCI, 25 PD-NC (normal cognition) and 22 age and gender-matched healthy controls (HC) were scanned (3T Siemens PRISMA). PD-MCI was diagnosed using a comprehensive cognitive battery according to the recommended diagnostic criteria (Litvan, Goldman et al. 2012). Seed-based functional connectivity analysis was performed to identify altered connectivity between seeds in the default mode networks (DMN), frontoparietal network (FPN), and dorsal attention network (DAN) to other regions between groups. Results: PD-MCI and PD-NC showed no significant difference in PD related measures such as PD duration, UPDRS-III, HY stage, and LEDD. MPFC in DMN to bilateral cerebellar vermis (VI) showed a significant increase in connectivity in PD-MCI compared to PD-NC and HC, with PD-NC having a significant decrease in connectivity compared to both PD-MCI and HC. Compared to PD-NC, PD-MCI showed up-regulated functional connectivity between Left FPF and bilateral IPS seeds in DAN and IFG, STG and parietal regions. However, there was no difference between PD-MCI and HC. Conclusion: Increased functional connectivity between MPFC and bilateral cerebellar vermis in PD-MCI emphasise the important role of the cerebellum in cognition. The previous study also reported the possibility of the compensatory prefrontal cortical-cerebellar loop in PD-MCI (Zhan, Lin et al. 2018). Furthermore, increased functional connectivity in PD-MCI between DAN and other regions could be a compensatory effect. Despite prominent cognitive deficit in PD-MCI, PD-MCI is still not in a state of dementia. Longitudinal studies will provide in-depth knowledge of altered functional connectivity in cognitively impaired PD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".