Visual dysfunction in Parkinson's disease

Patients with Parkinson's disease have a number of specific visual disturbances. These include changes in colour vision and contrast sensitivity and difficulties with complex visual tasks such as mental rotation and emotion recognition. We review changes in visual function at each stage of visual processing from retinal deficits, including contrast sensitivity and colour vision deficits to higher cortical processing impairments such as object and motion processing and neglect. We consider changes in visual function in patients with common Parkinson's disease-associated genetic mutations including GBA and LRRK2 We discuss the association between visual deficits and clinical features of Parkinson's disease such as rapid eye movement sleep behavioural disorder and the postural instability and gait disorder phenotype. We review the link between abnormal visual function and visual hallucinations, considering current models for mechanisms of visual hallucinations. Finally, we discuss the role of visuo-perceptual testing as a biomarker of disease and predictor of dementia in Parkinson's disease

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

Neuroimaging studies of brain networks in Parkinson’s Disease

Parkinson’s disease (PD) is a common, disabling, neurodegenerative disease characterised by three core motor symptoms: tremor, rigidity and bradykinesia. These symptoms arise from degeneration of dopaminergic (DA) cells in the substantia nigra (SN) and the subsequent loss of dopaminergic terminals within the striatum, and circuits to cortical areas, critical in the control of movement. Other, non-DA systems are now known to be involved in the pathogenesis of PD, defective cognitive functions and side effects of DA medication treatments. Thus, the use of non-invasive in vivo techniques such as magnetic resonance imaging (MRI) has allowed a reliable, albeit in-direct method of assessing alterations in the PD brain. It is now widely considered that motor control is dependent upon the integrated operation of large-scale distributed brain networks. Recent methodological advances in MRI techniques allow both structural and functional connectivity between critical regions of motor control to be investigated and increase our understanding of the impact of PD pathology on motor networks and its subsequent effect on symptomatology. In this thesis, I present three studies that combine both structural and functional MRI techniques to assess the neural PD motor network and to test the general hypothesis that loss of effective motor control in PD arises from disrupted connectivity. I demonstrate in a sizable cross-sectional study that as disease burden increases, effective functioning in key motor areas and functional connectivity between regions in both the active and resting state is initially compromised but does show evidence of compensatory mechanisms. In addition, I show that compensatory mechanisms are likely to possess a neural reserve property rather than permit a period of normal functioning. Next, I present a follow-up study that assessed the active and resting neural motor network longitudinally. This study clearly shows that functional connectivity of the active and resting neural motor network is compromised as the disease progresses with evidence suggesting the initiation of compensatory mechanisms. Finally, structural properties of key regions related to PD pathology (substantia nigra and striatum) have been assessed to elucidate the effect of PD progression on diffusion indices and clinical symptoms. This work identifies the importance of multi-modal assessment of neural networks in PD to evaluate the effect of disease on neural motor control.Open Acces

Disrupted morphological grey matter networks in early-stage Parkinson’s disease

AbstractWhile previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.</jats:p

New Insights into Molecular Mechanisms Underlying Neurodegenerative Disorders

Neurodegenerative disorders encompass a broad range of sporadic and/or familial debilitating conditions characterized by the progressive dysfunction and loss of selective neuronal populations, determining different clinical phenotypes. Emerging research data indicate an interplay of genetic factors and epigenetic mechanisms underlying neurodegenerative processes, which lead to increased prevalence of neurodegenerative disorders. In concert with the constant increase in the aging population, neurodegenerative disorders currently represent a major challenge to public health worldwide. Despite recent advances in clinical and preclinical research, the pathogenesis of these disorders still remains poorly understood, without effective treatments being available to halt the neurodegenerative processes, but rather aiming at relieving symptoms. Therefore, a critical evaluation of current research data and in-depth understanding of the molecular mechanisms that lead to neurodegeneration are crucial in order to identify potential therapeutic targets that can pave the way to the development of novel and promising therapies. This Special Issue is focused on novel molecular data in the field of neurodegeneration that associate with the onset and progression of neurodegenerative diseases. We are particularly interested in original articles and reviews that provide new insights into the main molecular pathogenic mechanisms underlying neurodegenerative disorders, aiming to identify potential biomarkers and novel therapeutic strategies

Physiology and neuroanatomy of emotional reactivity in frontotemporal dementia

ABSTRACT AND SUMMARY OF EXPERIMENTAL FINDINGS The frontotemporal dementias (FTD) are a heterogeneous group of neurodegenerative diseases that cause variable profiles of fronto-insulo-temporal network disintegration. Loss of empathy and dysfunctional social interaction are a leading features of FTD and major determinants of care burden, but remain poorly understood and difficult to measure with conventional neuropsychological instruments. Building on a large body of work in the healthy brain showing that embodied responses are important components of emotional responses and empathy, I performed a series of experiments to examine the extent to which the induction and decoding of somatic physiological responses to the emotions of others are degraded in FTD, and to define the underlying neuroanatomical changes responsible for these deficits. I systematically studied a range of modalities across the entire syndromic spectrum of FTD, including daily life emotional sensitivity, the cognitive categorisation of emotions, interoceptive accuracy, automatic facial mimicry, autonomic responses, and structural and functional neuroanatomy to deconstruct aberrant emotional reactivity in these diseases. My results provide proof of principle for the utility of physiological measures in deconstructing complex socioemotional symptoms and suggest that these warrant further investigation as clinical biomarkers in FTD. Chapter 3: Using a heartbeat counting task, I found that interoceptive accuracy is impaired in semantic variant primary progressive aphasia, but correlates with sensitivity to the emotions of others across FTD syndromes. Voxel based morphometry demonstrated that impaired interoceptive accuracy correlates with grey matter volume in anterior cingulate, insula and amygdala. Chapter 4: Using facial electromyography to index automatic imitation, I showed that mimicry of emotional facial expressions is impaired in the behavioural and right temporal variants of FTD. Automatic imitation predicted correct identification of facial emotions in healthy controls and syndromes focussed on the frontal lobes and insula, but not in syndromes focussed on the temporal lobes, suggesting that automatic imitation aids emotion recognition only when social concepts and semantic stores are intact. Voxel based morphometry replicated previously identified neuroanatomical correlates of emotion identification ability, while automatic imitation was associated with grey matter volume in a visuomotor network including primary visual and motor cortices, visual motion area (MT/V5) and supplementary motor cortex. Chapter 5: By recording heart rate during viewing of facial emotions, I showed that the normal cardiac reactivity to emotion is impaired in FTD syndromes with fronto-insular atrophy (behavioural variant FTD and nonfluent variant primary progressive aphasia) but not in syndromes focussed on the temporal lobes (right temporal variant FTD and semantic variant primary progressive aphasia). Unlike automatic imitation, cardiac reactivity dissociated from emotion identification ability. Voxel based morphometry revealed grey matter correlates of cardiac reactivity in anterior cingulate, insula and orbitofrontal cortex. Chapter 6: Subjects viewed videos of facial emotions during fMRI scanning, with concomitant recording of heart rate and pupil size. I identified syndromic profiles of reduced activity in posterior face responsive regions including posterior superior temporal sulcus and fusiform face area. Emotion identification ability was predicted by activity in more anterior areas including anterior cingulate, insula, inferior frontal gyrus and temporal pole. Autonomic reactivity related to activity in both components of the central autonomic control network and regions responsible for processing the sensory properties of the stimuli

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis

Connectivity biomarkers in neurodegenerative tauopathies

The primary tauopathies are a group of neurodegenerative diseases affecting movement and cognition. In this thesis I study Progressive Supranuclear Palsy (PSP) and the Corticobasal Syndrome (CBS), two parkinsonian disorders associated with accumulation of hyperphos- phorylated and abnormally folded tau protein. I contrast these two disorders with Parkinson’s disease (PD), which is associated with the accumulation of alpha-synuclein but has a genetic association with the MAPT gene encoding tau. Understanding the tauopathies to develop effective treatments will require a better grasp of the relationships between clinical syndromes and cognitive measures and how the anatomical and neurochemical networks that underlie clinical features might be altered by disease. I investigate simple clinical biomarkers, showing that a two-minute test of verbal fluency is a potential diagnostic biomarker to distinguish between PD and PSP and that the ACE-R and its subscores could play a role in monitoring cognition over time in PD, PSP and CBS. I assess the implementation of network analysis in Functional Mag- netic Resonance Imaging (fMRI) data, introduce Maybrain software for graphical network analysis and visualisation. I go on to show an overlap between graph theory network measures and I identify three main factors underlying graph network measures of: efficiency and distance, hub characteristics, network community measures. I apply these measures in PD, PSP and the CBS. All three diseases caused a loss of functional connectivity in com- parison to the control group that was concentrated in more highly connected brain regions and in longer distance connections. In ad- dition, widely localised cognitive function of verbal fluency co-varied with the connection strength in highly connected regions across PD, PSP and CBS. To take this further, I investigated specific functional covariance networks. All three disease groups showed reduced connectivity between the basal ganglia network and other networks, and between the anterior salience network and other networks. Localised areas of increased co- variance suggest a breakdown of network boundaries which correlated with motor severity in PSP and CBS, and duration of disease in CBS. I explore the link between gene expression of the tau gene MAPT and its effects on functional connectivity showing that the expression of MAPT correlated with connection strength in highly connected hub regions that were more susceptible to a loss of connection strength in PD and PSP. I conclude by discussing how tau protein aggregates and soluble tau oligomers may explain the changes in functional brain networks. The primary tauopathies are a group of neurodegenerative diseases affecting movement and cognition. In this thesis I study Progressive Supranuclear Palsy (PSP) and the Corticobasal Syndrome (CBS), two parkinsonian disorders associated with accumulation of hyperphos- phorylated and abnormally folded tau protein. I contrast these two disorders with Parkinson’s disease (PD), which is associated with the accumulation of alpha-synuclein but has a genetic association with the MAPT gene encoding tau. Understanding the tauopathies to develop effective treatments will require a better grasp of the relationships between clinical syndromes and cognitive measures and how the anatomical and neurochemical networks that underlie clinical features might be altered by disease. I investigate simple clinical biomarkers, showing that a two-minute test of verbal fluency is a potential diagnostic biomarker to distinguish between PD and PSP and that the ACE-R and its subscores could play a role in monitoring cognition over time in PD, PSP and CBS. I assess the implementation of network analysis in Functional Mag- netic Resonance Imaging (fMRI) data, introduce Maybrain software for graphical network analysis and visualisation. I go on to show an overlap between graph theory network measures and I identify three main factors underlying graph network measures of: efficiency and distance, hub characteristics, network community measures. I apply these measures in PD, PSP and the CBS. All three diseases caused a loss of functional connectivity in com- parison to the control group that was concentrated in more highly connected brain regions and in longer distance connections. In ad- dition, widely localised cognitive function of verbal fluency co-varied with the connection strength in highly connected regions across PD, PSP and CBS. To take this further, I investigated specific functional covariance networks. All three disease groups showed reduced connectivity between the basal ganglia network and other networks, and between the anterior salience network and other networks. Localised areas of increased co- variance suggest a breakdown of network boundaries which correlated with motor severity in PSP and CBS, and duration of disease in CBS. I explore the link between gene expression of the tau gene MAPT and its effects on functional connectivity showing that the expression of MAPT correlated with connection strength in highly connected hub regions that were more susceptible to a loss of connection strength in PD and PSP. I conclude by discussing how tau protein aggregates and soluble tau oligomers may explain the changes in functional brain networks.This work was supported by the Medical Research Council (grant number G1100464) and a Sackler Scholarshi

Perception and cognition in Parkinson's disease: a neural network perspective

Parkinson’s disease (PD) is a neurodegenerative disorder commonly presenting with perceptual and cognitive dysfunction. Whereas previous work in PD suggests that abnormal basal ganglia activity has profound effects on integrated functioning of widespread cortical networks, the relation of specific network functions to the perceptual and cognitive impairments is still poorly understood. Here, I present a series of fMRI investigations of network-level functioning in non-demented individuals with PD with the aim of elucidating these associations. Study 1 examined the neural correlates of optic flow processing in 23 individuals with PD and 17 age-matched control participants (MC). An optic flow network comprising visual motion areas V6, V3A, MT+ and visuo-vestibular areas PIVC and CSv is known to be important for parsing egomotion depth cues in humans. The hypothesis was that individuals with PD would show less activation in these regions than MC when processing optic flow. While MC participants showed robust activation in this network, PD participants showed diminished activity within MT+ and CSv. Diminished CSv activity also correlated with greater disease severity. Study 2 investigated intrinsic network organization in PD with a focus on the functional coupling among three neurocognitive networks: the default-mode network (DMN), the salience network (SN), and the central executive network (CEN). Twenty-four individuals with PD and 20 MC participants were scanned at rest. The hypothesis was that PD participants would demonstrate dysfunctional SN coupling with the DMN and CEN. Relative to MC, in PD the CEN was less positively coupled with the SN and less anti-correlated with the DMN. Study 3 investigated the association between functional coupling and cognition in the same group that participated in Study 2. As hypothesized, anti-correlated functional coupling between the SN and DMN was related to successful performance on tests of executive function, psychomotor speed, and memory retrieval in MC but not in PD, suggesting that dysfunction within these networks could underlie early cognitive deficits in PD. Together, the results from the three studies suggest that dysfunctional activity in cortical networks important for visual motion processing and neurocognitive efficiency may underlie aspects of perceptual and cognitive impairment in PD.2017-12-06T00:00:00