125 research outputs found
Parkinson's Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data
Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the l2,p norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson's Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches
Proteomics and network analysis identify common and specific pathways of neurodegeneration
Neurodegenerative disorders, such as Parkinson's disease (PD), Alzheimer's disease (AD) and amyotrophic lateral sclerosis (ALS) are multi-factorial in nature, involving several genetic mutations (in coding or regulatory regions) and epigenetic and environmental factors. The main clinical manifestation (movement disorders, cognitive impairment and/or psychiatric disturbances) depends on the neuron population being primarily affected. Complex and multifactorial neurodegenerative diseases can be investigated using a holistic approach that can give a global view about the pathogenetic process and shed light on specific and generic pathways of neurodegeneration. Proteomics offers a global molecular snapshot of proteins and consequently of processes that may influence neuronal death. The proteome in fact provides a dynamic view of what is happening in the system under investigation, because the expression of proteins, their abundance, their localization in tissues or cells, the type and amount of their post-translational changes depend from the environment and from the cellular physiological state. Therefore, all the projects presented in this thesis, by combining bioinformatics tools with proteomics, aimed at highlighting biochemical processes shared by different neurodegenerative diseases and diseasespecific pathways, which may justify the degeneration of dopaminergic neurons in PD. Finally, a focus on the mitochondrial interactome and proteome intended to elucidate important specific steps of the degenerative process in PD
Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease
Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression.
The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy.
This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant.
With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis
Pathways to dementia: genetic predictors of cognitive and brain imaging endophenotypes in Alzheimer's disease
Indiana University-Purdue University Indianapolis (IUPUI)Alzheimer's disease (AD) is a national priority, with nearly six million Americans affected at an annual cost of $200 billion and no available cure. A better understanding of the mechanisms underlying AD is crucial to combat its high and rising incidence and burdens. Most cases of AD are thought to have a complex etiology with numerous genetic and environmental factors influencing susceptibility. Recent genome-wide association studies (GWAS) have confirmed roles for several hypothesized genes and have discovered novel loci associated with disease risk. However, most GWAS-implicated genetic variants have displayed modest individual effects on disease risk and together leave substantial heritability and pathophysiology unexplained. As a result, new paradigms focusing on biological pathways have emerged, drawing on the hypothesis that complex diseases may be influenced by collective effects of multiple variants – of a variety of effect sizes, directions, and frequencies – within key biological pathways. A variety of tools have been developed for pathway-based statistical analysis of GWAS data, but consensus approaches have not been systematically determined. We critically review strategies for genetic pathway analysis, synthesizing extant concepts and methodologies to guide application and future development. We then apply pathway-based approaches to complement GWAS of key AD-related endophenotypes, focusing on two early, hallmark features of disease, episodic memory impairment and brain deposition of amyloid-β. Using GWAS and pathway analysis, we confirmed the association of APOE (apolipoprotein E) and discovered additional genetic modulators of memory functioning and amyloid-β deposition in AD, including pathways related to long-term potentiation, cell adhesion, inflammation, and NOTCH signaling. We also identified genetic associations to amyloid-β deposition that have classically been understood to mediate learning and memory, including the BCHE gene and signaling through the epidermal growth factor receptor. These findings validate the use of pathway analysis in complex diseases and illuminate novel genetic mechanisms of AD, including several pathways at the intersection of disease-related pathology and cognitive decline which represent targets for future studies. The complexity of the AD genetic architecture also suggests that biomarker and treatment strategies may require simultaneous targeting of multiple pathways to effectively combat disease onset and progression
FUNCTIONAL NETWORK CONNECTIVITY IN HUMAN BRAIN AND ITS APPLICATIONS IN AUTOMATIC DIAGNOSIS OF BRAIN DISORDERS
The human brain is one of the most complex systems known to the mankind. Over the past 3500 years, mankind has constantly investigated this remarkable system in order to understand its structure and function. Emerging of neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have opened a non-invasive in-vivo window into brain function. Moreover, fMRI has made it possible to study brain disorders such as schizophrenia from a different angle unknown to researchers before. Human brain function can be divided into two categories: functional segregation and integration. It is well-understood that each region in the brain is specialized in certain cognitive or motor tasks. The information processed in these specialized regions in different temporal and spatial scales must be integrated in order to form a unified cognition or behavior. One way to assess functional integration is by measuring functional connectivity (FC) among specialized regions in the brain. Recently, there is growing interest in studying the FC among brain functional networks. This type of connectivity, which can be considered as a higher level of FC, is termed functional network connectivity (FNC) and measures the statistical dependencies among brain functional networks. Each functional network may consist of multiple remote brain regions. Four studies related to FNC are presented in this work. First FNC is compared during the resting-state and auditory oddball task (AOD). Most previous FNC studies have been focused on either resting-state or task-based data but have not directly compared these two. Secondly we propose an automatic diagnosis framework based on resting-state FNC features for mental disorders such as schizophrenia. Then, we investigate the proper preprocessing for fMRI time-series in order to conduct FNC studies. Specifically the impact of autocorrelated time-series on FNC will be comprehensively assessed in theory, simulation and real fMRI data. At the end, the notion of autoconnectivity as a new perspective on human brain functionality will be proposed. It will be shown that autoconnectivity is cognitive-state and mental-state dependent and we discuss how this source of information, previously believed to originate from physical and physiological noise, can be used to discriminate schizophrenia patients with high accuracy
The Phenomenology, Pathophysiology and Progression of the Core Features of Lewy Body Dementia
Lewy body dementias – Dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) - are disabling neurodegenerative conditions defined pathologically by the presence of intraneuronal α-synuclein rich aggregates (‘Lewy bodies’ and ‘Lewy neurites’). These disorders are characterized by a set of ‘core’ clinical features, namely cognitive fluctuations, visual hallucinations, motor parkinsonism, and most recently added, REM sleep behaviour disorder. These features are central to the diagnosis of Lewy bodies dementias (especially DLB) and discriminate them from other neurodegenerative disorders. Despite decades of research, the etiopathogenesis underlying Lewy body disorders is poorly understood. This accounts for the relative lack of objective biomarkers and both symptomatic and disease modifying therapies. The present thesis comprises a series of investigations that seeks to understand the phenomenology, pathophysiology, and clinical progression of Lewy body dementias through focus on each of the core clinical features. Systematic review and empiric studies are organized under the respective headings of cognitive fluctuations, visual hallucinations, REM sleep behaviour disorder, motor features, interrelationships, and clinical progression of the core features. Novel clinical and pathophysiological insights are obtained which have implications for the prediction and diagnosis of core features, the development of new objective biomarkers, and clinical endpoints of disease progression. From these studies, a shared pathophysiological basis for the core features is postulated and potential avenues for future directions are highlighted, focusing on replication and validation of new biomarkers and clinical measures, discovery of new biomarkers and mechanisms, and translation to prodromal and patient cohorts
Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study
Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not
Brain Dynamics as Confirmatory Biomarker of Dementia with Lewy Bodies Versus Alzheimer’s Disease - an Electrophysiological Study
PhD ThesisIntroduction
Dementia with Lewy bodies (DLB), Parkinson’s disease dementia (PDD) and Alzheimer’s
disease dementia (AD) are associated with different pathologies. Nevertheless, symptomatic overlap between these conditions may lead to misdiagnosis. Resting-state functional connectivity features in DLB as assessed with electroencephalography (EEG) are emerging as diagnostic biomarkers. However, their pathological significance is still questioned. This study aims to further investigate this aspect and to infer functional and structural sources of EEG abnormalities in DLB.
Methods
Graph theory analysis was first performed to assess EEG network differences between healthy controls (HC) and dementia groups. Source localisation and Network Based Statistics (NBS) were used to infer EEG cortical network and dominant frequency (DF) alterations in DLB compared with AD. Further analysis aimed to assess the subnetwork associated with visual hallucination (VH) symptom in DLB and PDD, i.e. LBD, compared with not-hallucinating (NVH) patients. Finally, probabilistic tractography was performed on diffusion tensor imaging (DTI) data between cortical regions, thalamus, and basal forebrain (NBM). Correlation between structural and functional connectivity was tested.
Results
EEG α-band (7-13.5 Hz) network features were affected in LBD compared with HC, whilst DLB β-band network (14-20.5 Hz) was weaker and more segregated when compared with AD. This scenario replicated in the source domain. DF was significantly lower in DLB compared with AD, and positively correlated with structural connectivity strength between NBM and the cortex. Functional visual ventral network connectivity and cholinergic projections towards the cortex were affected in VH compared with NVH, and significantly correlated in NVH.
Conclusions
Functional connectivity as assessed with EEG is more affected in DLB compared with AD. Moreover, the visual ventral network is functionally altered in VH compared with NVH. Results from structural analysis provide empirical evidence on the role of cholinergic dysfunctions in DLB and PDD pathology and corresponding functional correlates
The role of the gut and the gastrointestinal microbiome in Parkinson’s disease
INTRODUCTION: Parkinson’s disease (PD) is a disabling and progressive neurodegenerative disorder that is increasing in prevalence with the aging and urbanisation of the global population. The mechanisms underlying PD pathogenesis and progression are incompletely understood. Improved clinical recognition of early and prodromal non-motor symptoms (NMS), namely gastrointestinal (GI) dysfunction, has focused research over the last two decades on the roles of the gut. More recently, the influences of the microbiota-gut-brain-axis (MGBA) in the development and progression of PD have become an intensive area of research. Studies have demonstrated an association between the GM and a variety of PD-related characteristics, identifying important impacts on levodopa metabolism by certain microbiota. Importantly, the effect of device-assisted therapies (DATs) on the GM and the robustness of microbiota compositional differences between PD patients and household controls (HCs) has not been well defined.
The aims of this thesis were to 1) investigate GI dysfunction and nutritional patterns in PD, 2) determine if the GM is a biomarker of PD, and 3) investigate the temporal stability of the GM in PD patients receiving standard therapies and those initiating DATs.
METHODS: 103 PD patients and 81 HCs were recruited and participants with PD were considered in two sub-cohorts; 1) PD patients initiating DAT; either Deep Brain Stimulation (DBS) (n=10), or levodopa-carbidopa intestinal gel (LCIG) (n=11), who had GM sampling from stool at -2, 0, 2 and 4 weeks around initiation of DAT and baseline, 6 and 12 months following DAT initiation, 2) 82 PD patients receiving standard PD therapies, who had GM sampling from stool at baseline, 6 and 12 months. Validated PD questionnaire metadata ascertaining motor characteristics and NMS, as well as nutritional data in the form of a Food Frequency Questionnaire, were collected for all participants at baseline, 6 and 12 months. Total DNA was isolated from stool before sequencing the V3-V4 region of 16S rRNA. Relative bacterial abundances, diversity measures, compositional differences and clinical-microbiome associations were determined, as well as developing predictive modelling to identify PD patients and assess disease progression.
RESULTS: PD patients reported more prevalent and severe GI dysfunction, especially constipation, which was almost three-times more common compared to HC subjects, (78.6% vs 28.4%, p<0.001). PD patients had a higher intake of total carbohydrates (279 g/day vs 232 g/day; p=0.034), which was largely attributable to an increased daily sugar intake (153 g/day vs 119 g/day; p=0.003), particularly of free sugars (61 g/day vs 41 g/day; p=0.001). Significant GM compositional differences across several taxonomic levels were apparent between PD patients and HCs and associated with a number of PD motor and NMS features, as well as certain therapies. Predictive models to distinguish PD from HCs were developed considering global GM profiles, achieving an area under the curve (AUC) of 0.71, which was improved by addition of data on carbohydrate intake (AUC 0.74).
Longitudinal analysis demonstrated persistent underrepresentation of known short-chain fatty acid producing bacteria in PD patients, particularly those concerned with butyrate production; Butyricicoccus, Fusicatenibacter, Lachnospiraceae ND3007 group and Erysipelotrichaceae UCG−003. Taxa differences observed over the short-term (four week) sampling period around DAT (DBS and LCIG) initiation, were not sustained at 6 and 12 months. Despite this, persistent longer-term overrepresentation of Prevotella was observed after DBS initiation, and a trend was found that was suggestive of overrepresentation of Roseburia after LCIG initiation. These results suggest that there may be variable shorter and longer-term DBS and LCIG influences on the GM, which are complex and multifactorial. PD progression analysis did not identify distinct persisting GM compositional differences between faster and slower progressing patients, although predictive modelling was strengthened by the consideration of nutritional data, specifically protein intake, and improved the predictive capacity for PD progression.
CONCLUSION: This thesis demonstrates that there are numerous clinically significant associations between the gut, GM and PD. GI dysfunction is common, and carbohydrate nutritional intake appears to be different from the general population in PD. Persistent alterations of GM composition in PD compared to HCs were found. These findings provide support for the existence of disturbances of gut homeostatic pathways, which may disrupt intestinal barrier permeability and lead to gut leakiness, in the pathogenesis of PD. This thesis also highlights the potential to use the GM in the identification of PD and the characterisation of disease progression
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