Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson’s disease
Bibliographic record
Abstract
Abstract Background Visual hallucinations in Parkinsons disease (PD) are transient, suggesting a change in dynamic brain states. However, the causes underlying these dynamic brain changes are not known. Methods Focusing on fundamental network properties of integration and segregation, we used rsfMRI to examine alterations in temporal dynamics in PD patients with hallucinations (n=16) compared to those without hallucinations (n=75) and a group of normal controls (n=32). We used network control theory to examine how structural connectivity guides transitions between functional states. We then studied the brain regions most involved in these state transitions, and examined corresponding neurotransmitter density profiles and receptor gene expression in health. Results There were significantly altered temporal dynamics in PD with hallucinations, with an increased proportion of time spent in the Segregated state compared to non-hallucinators and controls; less between-state transitions; and increased dwell time in the Segregated state. The energy cost needed to transition from integrated-to-segregated state was lower in PD-hallucinators compared to non-hallucinators. This was primarily driven by subcortical and transmodal cortical brain regions, including the thalamus and default mode network regions. The regional energy needed to transition from integrated-to-segregated state was significantly correlated with regional neurotransmitter density and gene expression profiles for serotoninergic (including 5HT2A), GABAergic, noradrenergic and cholinergic but not dopaminergic density profiles. Conclusions We describe the patterns of temporal functional dynamics in PD-hallucinations, and link these with neurotransmitter systems involved in early sensory and complex visual processing. Our findings provide mechanistic insights into visual hallucinations in PD and highlighting potential therapeutic targets.
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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".