54 research outputs found

    Alzheimer Disease Detection Techniques and Methods: A Review

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    Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

    REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

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    Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

    Machine learning approaches for the study of AD with brain MRI data

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    Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Directors: Roser Sala Llonch, Agnès Pérez MillanThe use of automated or semi-automated approaches based on imaging data has been suggested to support the diagnoses of some diseases. In this context, Machine Learning (ML) appears as a useful emerging tool for this purpose, allowing from feature extraction to automatic classification. Alzheimer Disease (AD) and Frontotemporal Dementia (FTD) are two common and prevalent forms of early-onset dementia with different, but partly overlapping, symptoms and brain patterns of atrophy. Because of the similarities, there is a need to establish an accurate diagnosis and to obtain good markers for prognosis. This work combines both supervised and unsupervised ML algorithms to classify AD and FTD. The data used consisted of gray matter volumes and cortical thicknesses (CTh) extracted from 3TT1 MRI of 44 healthy controls (HC, age: 57.8±5.4 years), 53 Early-Onset Alzheimer Disease patients (EOAD, age: 59.4±4.4 years) and 64 FTD patients (FTD, age: 64.4±8.8 years). A principal component analysis (PCA) of all volumes and thicknesses was performed and a number of principal components (PC) that accumulated at least 80% of the data variance were entered into a Support Vector Machine (SVM). Overall performance was assessed using a 5-fold crossvalidation..

    DETECTING BRAIN-WIDE INTRINSIC CONNECTIVITY NETWORKS USING fMRI IN MICE

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    Functional neuroimaging methods in mice are essential for unraveling complex neuronal networks that underlie maladaptive behavior in neurological disorder models. By using fMRI to detect intrinsic connectivity networks in mice, we can examine large scale alteration in brain activity and functional connectivity to establish causal associations in brain network changes. The work presented in this dissertation is organized into five chapters. Chapter 1 provides the necessary background required to understand how functional neuroimaging tools such as fMRI detect signal changes attributed to spontaneous neuronal activity of intrinsic connectivity networks in mice. Chapter 2 describes the development of our isotropic fMRI acquisition sequence in mice and semi-automated pipeline for mouse fMRI data. Naïve mouse fMRI scans were used to validated the pipeline by reliably and reproducibly extracting intrinsic connectivity networks. Chapter 3 establishes the development and validation of a novel superparamagenetic iron-oxide nanoparticle to enhance fMRI signal sensitivity. Chapter 4 studies the effects norepinephrine released by locus coeruleus neurons on the default mode network in mice. Norepinephrine release selectively enhanced neuronal activity and connectivity in the Frontal module of the default mode network by suppressing information flow from the Retrosplenial-Hippocampal to the Association modules. Chapter 5 addresses the implications of our findings and addresses the limitations and future studies that can be conducted to expand on this research.Doctor of Philosoph

    Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

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    Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

    Diagnosis of Autism Spectrum Disorder Based on Brain Network Clustering

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    Developments in magnetic resonance imaging (MRI) provide new non-invasive approach—functional MRI (fMRI)—to study functions of brain. With the help of fMRI, I can build functional brain networks (FBN) to model correlations of brain activities between cortical regions. Studies focused on brain diseases, including autism spectrum disorder (ASD), have been conducted based on analyzing alterations in FBNs of patients. New biomarkers are identified, and new theories and assumptions are proposed on pathology of brain diseases. Considering that traditional clinical ASD diagnosis instruments, which greatly rely on interviews and observations, can yield large variance, recent studies start to combine machine learning methods and FBN to perform auto-classification of ASD. Such studies have achieved relatively good accuracy. However, in most of these studies, features they use are extracted from the whole brain networks thus the dimension of the features can be high. High-dimensional features may yield overfitting issues and increase computational complexity. Therefore, I need a feature selection strategy that effectively reduces feature dimensions while keeping a good classification performance. In this study, I present a new feature selection strategy that extracting features from functional modules but not the whole brain networks. I will show that my strategy not only reduces feature dimensions, but also improve performances of auto-classifications of ASD. The whole study can be separated into 4 stages: building FBNs, identification of functional modules, statistical analysis of modular alterations and, finally, training classifiers with modular features for auto-classification of ASD. I firstly demonstrate the whole procedure to build FBNs from fMRI images. To identify functional module, I propose a new network clustering algorithm based on joint non-negative matrix factorization. Different from traditional brain network clustering algorithms that mostly perform on an average network of all subjects, I design my algorithm to factorize multiple brain networks simultaneously because the clustering results should be valid not only on the average network but also on each individual network. I show the modules I find are more valid in both views. Then I statistically analyze the alterations in functional modules between ASD and typically developed (TD) group to determine from which modules I extract features from. Several indices based on graph theory are calculated to measure modular properties. I find significant alterations in two modules. With features from these two modules, I train several widely-used classifiers and validate the classifiers on a real-world dataset. The performances of classifiers trained by modular features are better than those with whole-brain features, which demonstrates the effectiveness of my feature selection strategy

    Visualisation of multi-dimensional medical images with application to brain electrical impedance tomography

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    Medical imaging plays an important role in modem medicine. With the increasing complexity and information presented by medical images, visualisation is vital for medical research and clinical applications to interpret the information presented in these images. The aim of this research is to investigate improvements to medical image visualisation, particularly for multi-dimensional medical image datasets. A recently developed medical imaging technique known as Electrical Impedance Tomography (EIT) is presented as a demonstration. To fulfil the aim, three main efforts are included in this work. First, a novel scheme for the processmg of brain EIT data with SPM (Statistical Parametric Mapping) to detect ROI (Regions of Interest) in the data is proposed based on a theoretical analysis. To evaluate the feasibility of this scheme, two types of experiments are carried out: one is implemented with simulated EIT data, and the other is performed with human brain EIT data under visual stimulation. The experimental results demonstrate that: SPM is able to localise the expected ROI in EIT data correctly; and it is reasonable to use the balloon hemodynamic change model to simulate the impedance change during brain function activity. Secondly, to deal with the absence of human morphology information in EIT visualisation, an innovative landmark-based registration scheme is developed to register brain EIT image with a standard anatomical brain atlas. Finally, a new task typology model is derived for task exploration in medical image visualisation, and a task-based system development methodology is proposed for the visualisation of multi-dimensional medical images. As a case study, a prototype visualisation system, named EIT5DVis, has been developed, following this methodology. to visualise five-dimensional brain EIT data. The EIT5DVis system is able to accept visualisation tasks through a graphical user interface; apply appropriate methods to analyse tasks, which include the ROI detection approach and registration scheme mentioned in the preceding paragraphs; and produce various visualisations

    Neuroimaging biomarkers associated with clinical dysfunction in Parkinson disease

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    Parkinson disease (PD) is the second most common neurodegenerative disorder in the world, directly affecting 2-3% of the population over the age of 65. People diagnosed with the disorder can experience motor, autonomic, cognitive, sensory and neuropsychiatric symptoms that can significantly impact quality of life. Uncertainty still exists about the pathophysiological mechanisms that underlie a range of clinical features of the disorder, linked to structural as well as functional brain changes. This thesis thus aimed to uncover neuroimaging biomarkers associated with clinical dysfunction in PD. A 'hubs-and-spokes' neural circuit-based approach can contribute to this aim, by analysing the component elements and also the interconnections of important brain networks. This thesis focusses on structures within basal ganglia-thalamocortical neuronal circuits that are linked to a range functions impacted in the disorder, and that are vulnerable to the consequences of PD pathology. This thesis investigated neuronal 'hubs' by studying the morphology of the caudate nucleus, putamen, thalamus and neocortex. The caudate nucleus, putamen and thalamus are all vital subcortical 'hubs' that play important roles in a number of functional domains that are compromised in PD. The neocortex, on the other hand, has a range of 'hubs' spread across it, regions of the brain that are crucial for neuronal signalling and communication. The interconnections, or 'spokes', between these hubs and other brain regions were investigated using seed-based resting-state functional connectivity analyses. Finally, a morphological analysis was used to investigate possible structural changes to the corpus callosum, the major inter-hemispheric white matter tract of the brain, crucial to effective higher-order brain processes. This thesis demonstrates that the caudate nucleus, putamen, thalamus, corpus callosum and neocortex are all atrophied in PD participants with dementia. PD participants also demonstrated a significant correlation between volumes of the caudate nuclei and general cognitive functioning and speed, while putamina volumes were correlated with general motor function. Cognitively unimpaired PD participants demonstrated minimal morphological alterations compared to control participants, however they demonstrated significant increases in functional connectivity of the caudate nucleus, putamen and thalamus with areas across the frontal lobe, and decreases in functional connectivity with parietal and cerebellar regions. PD participants with mild cognitive impairment and dementia show decreased functional connectivity of the thalamus with paracingulate and posterior cingulate cortices, respectively. This thesis contributes a deeper understanding of the relationship between structures of basal ganglia-thalamocortical neuronal circuits, corpus callosal and neocortical morphology, and the clinical dysfunction associated with PD. This thesis suggests that functional connectivity changes are more common in early stages of the disorder, while morphological alterations are more pronounced in advanced disease stages
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