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Weil, R. S.

Publications and source records attributed to Weil, R. S..

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Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease

Visual hallucinations are common in Parkinsons disease and are associated with poorer quality of life and higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinsons disease and other Lewy-body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinsons hallucinations. This is in part because previous resting-state studies focus on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity - defined as the directed (causal) influence that one region exerts on another region - from resting-state functional MRI data. In the current study, we utilise spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinsons disease visual hallucinators, and 75 Parkinsons disease non-hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from left prefrontal cortex to primary visual cortex and medial thalamus, as compared to non-hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinsons disease.

neuroscience↗

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

BackgroundVisual 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. MethodsFocusing 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. ResultsThere 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. ConclusionsWe 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.

neuroscience↗

Visual dysfunction predicts cognitive impairment and white matter degeneration in Parkinson's disease

Visual dysfunction predicts dementia in Parkinsons disease (PD), but whether this translates to structural change is not known. We aimed to identify longitudinal white matter changes in patients with Parkinsons disease and low visual function and also in those who developed mild cognitive impairment (MCI). We used fixel-based analysis to examine longitudinal white matter change in PD. Diffusion MRI and clinical assessments were performed in 77 patients at baseline (22 low visual function /55 intact vision; and 13 MCI, 13 MCI converters /51 normal cognition) and 25 controls and again after 18 months. We compared micro-structural changes in fibre density, macro-structural changes in fibre bundle cross-section (FC) and combined fibre density and cross-section across white matter, adjusting for age, gender and intracranial volume. Patients with Parkinsons and visual dysfunction showed worse cognitive performance at follow up and were more likely to develop MCI compared with those with normal vision (p=0.008). Parkinsons with poor visual function showed diffuse micro-structural and macro-structural changes at baseline, whereas those with MCI showed fewer baseline changes. At follow-up, Parkinsons with low visual function showed widespread macrostructural changes, involving the fronto-occipital fasciculi, external capsules, and middle cerebellar peduncles bilaterally. No longitudinal change was seen in baseline MCI or in MCI converters, even when the two groups were combined. Parkinsons patients with poor visual function show increased white matter damage over time, providing further evidence for visual function as a marker of imminent cognitive decline.

neuroscience↗