Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson’s disease

Abstract

Hallucinations are a core feature of psychosis and common in Parkinson’s. Their transient, unexpected nature suggests a change in dynamic brain states, but underlying causes are unknown. Here, we examine temporal dynamics and underlying structural connectivity in Parkinson’s-hallucinations using a combination of functional and structural MRI, network control theory, neurotransmitter density and genetic analyses. We show that Parkinson’s-hallucinators spent more time in a predominantly Segregated functional state with fewer between-state transitions. The transition from integrated-to-segregated state had lower energy cost in Parkinson’s-hallucinators; and was therefore potentially preferable. The regional energy needed for this transition was correlated with regional neurotransmitter density and gene expression for serotoninergic, GABAergic, noradrenergic and cholinergic, but not dopaminergic, receptors. We show how the combination of neurochemistry and brain structure jointly shape functional brain dynamics leading to hallucinations and highlight potential therapeutic targets by linking these changes to neurotransmitter systems involved in early sensory and complex visual processing.

Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease

“…In the context of the brain, such input can intuitively take the form of task modulation [14, 16, 51] or other perturbations from the environment, but potentially also pharmacological or direct electromagnetic stimulation [72, 91, 124, 134], or endogenous signals from elsewhere in the brain [92]. This approach is widely applicable across the breadth of neuroscience, from C. elegans and drosophila [73, 158] to rodents and primates [52, 73], and across human development [78, 106, 140], health, and disease [11, 14, 16, 58, 107, 134, 165, 166].…”

Functional Brain Networks of Minor and Well‐Structured Major Hallucinations in Parkinson's Disease

“…This sample size exceeds that of the majority of previous fMRI studies in PD with hallucination. 8,[38][39][40][41][42] Moreover, some of these studies used relatively short acquisition time, ranging from 6 to 7 minutes. 38,[40][41][42] Furthermore, another study highlights that even short resting-state fMRI scans lasting only 2 to 5 minutes demonstrate strong clinical utility, without significant loss of individual functional network connectivity information of longer scans.…”

“…8,[38][39][40][41][42] Moreover, some of these studies used relatively short acquisition time, ranging from 6 to 7 minutes. 38,[40][41][42] Furthermore, another study highlights that even short resting-state fMRI scans lasting only 2 to 5 minutes demonstrate strong clinical utility, without significant loss of individual functional network connectivity information of longer scans. 43 A longer acquisition time has advantages for analysis sensitivity and power, but there are some trade-offs that prolonged time inside the scanner increases the risk of patients experiencing fatigue, head movement, and various subjective physiological noises, 43 which is particularly relevant in the context of our study population, an aging group affected by PD.…”

Indirect evidence for altered dopaminergic neurotransmission in very premature‐born adults

“…A detailed description of the evaluated prospective whole population‐based Bavarian Longitudinal Study (BLS) cohort can be found in Lubsen et al (2011); Sakreida et al, 2022; Zarkali et al (2022). Briefly, in the BLS neonatal at‐risk children and healthy controls from southern Bavaria were included between January 1985 and March 1986 (Wolke et al, 1994; Wolke & Meyer, 1999).…”

“…Indeed, a novel analysis toolbox, developed by Dukart et al, implemented this idea of the indirect study of potential neurotransmission changes, providing an elegant analysis pipeline for calculating cross-sample spatial regression coefficients (spatial-RC) reflecting the relationship between brain metric individual difference maps and molecular imaging-derived normative neurotransmitter maps (Dukart et al, 2021). Previous studies using the toolbox provided reliable results and were able to validate the methodology (Chen et al, 2021;Dugré & Potvin, 2022;Han et al, 2022;Martins et al, 2022;Oldehinkel et al, 2022;Park et al, 2022;Sakreida et al, 2022;Tang et al, 2022;Xu et al, 2022;Zarkali et al, 2022). There are similar methodological approaches, for example, the Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) toolbox (Dipasquale et al, 2019), whose analytic approach combines pharmacological rs-fMRI analyses and specific neurotransmitter receptor distributions by PET imaging in healthy brains, or, in general, toolboxes from the field of imaging transcriptomics that work in a similar way (e.g., Imaging Transcriptomics [Martins et al, 2021] or Gene Category Enrichment Analysis [Fulcher et al, 2021]).…”