Brain functional connectivity and neuropsychiatric symptoms in patients with mild cognitive impairment, cerebrovascular disease and Parkinson disease
Bibliographic record
Abstract
BACKGROUND: Mild Behavioral Impairment (MBI) is a condition characterized by neuropsychiatric symptoms (NPS) in older adults without dementia, serving as a precursor to various forms of dementia. This study explores the association between NPS and functional connectivity (FC) within the default mode network (DMN), executive control network (ECN), and salience network (SN) across three high-risk cohorts: mild cognitive impairment (due to Alzheimer's) (MCI, n = 79), cerebrovascular disease (CVD, n = 144), and Parkinson's disease (PD, n = 132). METHOD: A total of 367 participants were recruited from the Ontario Neurodegenerative Disease Research Initiative (ONDRI). The assessment of NPS utilized the Neuropsychiatric Inventory Questionnaire (NPI-Q), with symptom severity rated on a scale from 1 to 3 (mild, moderate, severe). Resting-state FC was analyzed for the DMN, ECN, and SN, using dual regression analysis to generate subject-specific whole-brain FC maps for each network. The association between FC maps and NPS scores was examined using FSL's randomise with 5,000 permutations, while controlling for age, sex, and education. Results are presented following cluster False Discovery Rate (FDR) correction. RESULT: The study revealed significant associations between NPS and FC specific to each cohort. In the MCI group, disturbed appetite and nighttime behaviors were correlated with increased FC of the dorsal DMN (p<0.05, R = 0.47, and p = 0.01, R = 0.47). The CVD group exhibited correlations between higher levels of anxiety and decreased FC of the dorsal DMN (p<0.05, R = -0.4), ventral DMN (p<0.05, R = -0.33), and bilateral ECN (p<0.05, R = -0.35 and R = -0.33). The PD group showed disturbed nighttime behavior associated with increased FC in ventral DMN (p<0.05, R = 0.35) and bilateral ECN (p<0.05, R = 0.43 and R = 0.37). CONCLUSION: This research underscores disorder-specific correlations between specific NPS domains and FC in MCI, CVD, and PD, emphasizing the unique neural underpinnings of symptomatology in each group. Furthermore, it is essential to note the inherent heterogeneity in all groups. Overall, the pathological substrates of neurodegenerative disorders likely play a pivotal role in shaping the neural correlates of MBI within each disorder. These findings provide valuable insights into targeted interventions and avenues for future research in neurodegenerative disorders.
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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".