The Neural Correlates of Visual Hallucinations in Parkinson's Disease

Visual hallucinations are common in Parkinson’s disease (PD) and linked to worse outcomes: increased mortality, higher carer burden, cognitive decline, and worse quality of life. Recent functional studies have aided our understanding, showing large-scale brain network imbalance in PD hallucinations. Imbalance of different influences on visual perception also occurs, with impaired accumulation of feedforward signals from the eyes and visual parts of the brain. Whether feedback signals from higher brain control centres are also affected is unknown and the mechanisms driving network imbalance in PD hallucinations remain unclear. In this thesis I will clarify the computational and structural changes underlying PD hallucinations and link these to functional abnormalities and regional changes at the cellular level. To achieve this, I will employ behavioural testing, diffusion weighted imaging, structural and functional MRI in PD patients with and without hallucinations. I will quantify the use of prior knowledge during a visual learning task and show that PD with hallucinations over-rely on feedback signals. I will examine underlying structural connectivity changes at baseline and longitudinally and will show that posterior thalamic connections are affected early, with frontal connections remaining relatively preserved. I will show that PD hallucinations are associated with a subnetwork of reduced structural connectivity strength, affecting areas crucial for switching the brain between functional states. I will assess the role of the thalamus as a potential driver of network-level changes and show preferential medial thalamus involvement. I will utilise data from the Allen Institute transcription atlas and show how differences in regional gene expression in health contributes to the selective vulnerability of specific white matter connections in PD hallucinations. These findings reveal the structural correlates of visual hallucinations in PD, link these to functional and behavioural changes and provide insights into the cellular mechanisms that drive regional vulnerability, ultimately leading to hallucinations

Structural and Functional Imaging Correlates of Visual Hallucinations in Parkinson's Disease

PURPOSE OF REVIEW: To review recent structural and functional MRI studies of visual hallucinations in Parkinson's disease. RECENT FINDINGS: Previously, neuroimaging had shown inconsistent findings in patients with Parkinson's hallucinations, especially in studies examining grey matter volume. However, recent advances in structural and functional MRI techniques allow better estimates of structural connections, as well as the direction of connectivity in functional MRI. These provide more sensitive measures of changes in structural connectivity and allow models of the changes in directional functional connectivity to be tested. We identified 27 relevant studies and found that grey matter imaging continues to show heterogeneous findings in Parkinson's patients with visual hallucinations. Newer approaches in diffusion imaging and functional MRI are consistent with emerging models of Parkinson's hallucinations, suggesting shifts in attentional networks. In particular, reduced bottom-up, incoming sensory information, and over-weighting of top-down signals appear to be important drivers of visual hallucinations in Parkinson's disease

Flickering Stimuli Do Not Reliably Induce Visual Hallucinations in Parkinson's Disease.

Visual hallucinations are a common and often distressing feature of Parkinson's disease; they are ephemeral and capricious, making them difficult to study but tend to be more prominent in dim illumination. Flickering stimuli can induce simple hallucinations even in healthy individuals. We tested a stroboscope and an equivalent full-screen flickering stimulus in 16 participants: 7 patients with Parkinson's and habitual visual hallucinations, 6 Parkinson's patients without hallucinations and 3 controls. Both flicker sources induced varied geometrical hallucinations in 4 participants (25%) and complex hallucinations in 1 but neither induced typical Parkinson's-associated hallucinations

Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis

Quantitative magnetic resonance imaging (qMRI) allows extraction of reproducible and robust parameter maps. However, the connection to underlying biological substrates remains murky, especially in the complex, densely packed cortex. We investigated associations in human neocortex between qMRI parameters and neocortical cell types by comparing the spatial distribution of the qMRI parameters longitudinal relaxation rate (⁠R1⁠), effective transverse relaxation rate (⁠R2∗⁠), and magnetization transfer saturation (MTsat) to gene expression from the Allen Human Brain Atlas, then combining this with lists of genes enriched in specific cell types found in the human brain. As qMRI parameters are magnetic field strength-dependent, the analysis was performed on MRI data at 3T and 7T. All qMRI parameters significantly covaried with genes enriched in GABA- and glutamatergic neurons, i.e. they were associated with cytoarchitecture. The qMRI parameters also significantly covaried with the distribution of genes enriched in astrocytes (⁠R2∗ at 3T, R1 at 7T), endothelial cells (⁠R1 and MTsat at 3T), microglia (⁠R1 and MTsat at 3T, R1 at 7T), and oligodendrocytes and oligodendrocyte precursor cells (⁠R1 at 7T). These results advance the potential use of qMRI parameters as biomarkers for specific cell types

Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease

BACKGROUND: Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence. METHODS: We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods. RESULTS: Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12). CONCLUSIONS: In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials

Genetic topography and cortical cell loss in Huntington's disease link development and neurodegeneration

Cortical cell loss is a core feature of Huntington Disease (HD), beginning many years before clinical motor diagnosis, during the premanifest stage. However, it is unclear how genetic topography relates to cortical cell loss. Here, we explore the biological processes and cell types underlying this relationship and validate this using cell-specific post-mortem data. Eighty premanifest participants on average 15 years from disease onset and 71 controls were included. Using volumetric and diffusion MRI we extracted HD-specific whole brain maps where lower grey matter volume and higher grey matter mean diffusivity, relative to controls, were used as proxies of cortical cell loss. These maps were combined with gene expression data from the Allen Human Brain Atlas (AHBA) to investigate the biological processes relating genetic topography and cortical cell loss. Cortical cell loss was positively correlated with the expression of developmental genes (i.e. higher expression correlated with greater atrophy and increased diffusivity) and negatively correlated with the expression of synaptic and metabolic genes that have been implicated in neurodegeneration. These findings were consistent for diffusion MRI and volumetric HD-specific brain maps. As wild type Huntingtin is known to play a role in neurodevelopment, we explored the association between wild type Huntingtin (HTT) expression and developmental gene expression across the AHBA. Co-expression network analyses in 134 human brains free of neurodegenerative disorders was also performed. HTT expression was correlated with the expression of genes involved in neurodevelopment while co-expression network analyses also revealed that HTT expression was associated with developmental biological processes. Expression weighted cell-type enrichment (EWCE) analyses were used to explore which specific cell-types were associated with HD cortical cell loss and these associations were validated using cell specific single nucleus RNAseq (snRNAseq) data from post-mortem HD brains. The developmental transcriptomic profile of cortical cell loss in preHD was enriched in astrocytes and endothelial cells, while the neurodegenerative transcriptomic profile was enriched for neuronal and microglial cells. Astrocyte-specific genes differentially expressed in HD post-mortem brains relative to controls using snRNAseq were enriched in the developmental transcriptomic profile, while neuronal and microglial-specific genes were enriched in the neurodegenerative transcriptomic profile Our findings suggest that cortical cell loss in preHD may arise from dual pathological processes, emerging as a consequence of neurodevelopmental changes, at the beginning of life, followed by neurodegeneration in adulthood, targeting areas with reduced expression of synaptic and metabolic genes. These events result in age-related cell death across multiple brain cell types

Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease.

BACKGROUND: Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence. METHODS: We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods. RESULTS: Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12). CONCLUSIONS: In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials

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

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

Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis

Quantitative magnetic resonance imaging (qMRI) allows extraction of reproducible and robust parameter maps. However, the connection to underlying biological substrates remains murky, especially in the complex, densely packed cortex. We investigated associations in human neocortex between qMRI parameters and neocortical cell types by comparing the spatial distribution of the qMRI parameters longitudinal relaxation rate (equation ImEquation1), effective transverse relaxation rate (equation ImEquation2), and magnetization transfer saturation (MTsat) to gene expression from the Allen Human Brain Atlas, then combining this with lists of genes enriched in specific cell types found in the human brain. As qMRI parameters are magnetic field strength-dependent, the analysis was performed on MRI data at 3T and 7T. All qMRI parameters significantly covaried with genes enriched in GABA- and glutamatergic neurons, i.e. they were associated with cytoarchitecture. The qMRI parameters also significantly covaried with the distribution of genes enriched in astrocytes (equation ImEquation3 at 3T, equation ImEquation4 at 7T), endothelial cells (equation ImEquation5 and MTsat at 3T), microglia (equation ImEquation6 and MTsat at 3T, equation ImEquation7 at 7T), and oligodendrocytes and oligodendrocyte precursor cells (equation ImEquation8 at 7T). These results advance the potential use of qMRI parameters as biomarkers for specific cell types