Disrupted functional brain network associated with presence of hallucinations in Parkinson’s disease
Bibliographic record
Abstract
Abstract Background Hallucinations negatively impact quality of life in Parkinson’s disease, yet their neural mechanisms remain poorly understood, particularly in early disease stages. This study aimed to identify functional connectivity differences associated with visual hallucinations in early Parkinson’s Disease and to validate these findings across independent datasets. Methods Resting state fMRI data from two prior independent studies was used (total N=185; N=84 hallucinators and N=101 non-hallucinators). Group differences in functional connectivity were assessed within predefined cytoarchitectonic cortical classes and functional networks, followed by whole-brain analysis using Network-Based Statistics (NBS). Associations with clinical measures, including hallucination severity, motor symptoms, cognition, and attention, were also evaluated. Results NBS identified a subnetwork of reduced functional connectivity in hallucinators, connecting regions involved in the default mode, somatomotor and attentional networks. This subnetwork was replicated in a matched sample from our independent cohort (N=50; p<0.01). Functional connectivity within the identified network was significantly associated with hallucination severity (R² = 0.35, p = 0.01), and with baseline and future motor symptoms, cognition, and attention in hallucinators. Conclusions The identified functional subnetwork shows promise as a potential biomarker and therapeutic target for Parkinson’s disease psychosis, warranting further investigation and validation in future studies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".