Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease

We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures. We consider Alzheimer’s disease (AD), mild cognitive impairment of individuals who converted to AD within 18 months (MCIc), and normal controls (NC). Our approach uses statistical learning and a feature space consisting of projection-based shape descriptors, allowing for canonical representation of brain regions. Our framework automatically identifies the structures most affected by the disease. We evaluate our results by comparing to other methods using a standardized data set of 375 adults available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our framework is sensitive to identifying the onset of Alzheimer’s disease, achieving up to 88.13% accuracy in classifying MCIc versus NC, outperforming previous methods.National Science Foundation (U.S.) (1502435

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches

The most frequent kind of dementia of the nervous system, Alzheimer's disease, weakens several brain processes (such as memory) and eventually results in death. The clinical study uses magnetic resonance imaging to diagnose AD. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. As early diagnosis and stage detection are the most crucial elements in enhancing patient care and treatment outcomes, deep learning algorithms for MRI images have recently allowed for diagnosing a medical condition at the beginning stage and identifying particular symptoms of Alzheimer's disease. As a result, we aimed to analyze five specific studies focused on AD diagnosis using MRI-based deep learning algorithms between 2021 and 2023 in this study. To completely illustrate the differences between these techniques and comprehend how deep learning algorithms function, we attempted to explore selected approaches in depth

NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.La versión final de este artículo fue publicada el 29 de junio de 2023 en Brain Topography (Springer). Se encuentra accesible desde Biblioteca Di Tella a través de Prim

Study of longitudinal neurodegeneration biomarkers to support the early diagnosis of Alzheimer’s disease

Alzheimer’s Disease (AD) is a progressive and neurodegenerative disorder characterized by pathological brain changes starting several years before clinical symptoms appear. Earlier and accurate identification of those brain structures changes can help to improve diagnosis and monitoring, allowing that future treatments target the disease in its earliest stages, before irreversible brain damage or mental decline takes place. The brain of AD subjects shrinks significantly as the disease progress. Furthermore, ageing is the major risk factor for sporadic AD, older brains being more susceptible than young or middle-aged ones. However, seemingly healthy elderly brains lose matter in regions related to AD. Likewise, similar changes can also be found in subjects having mild cognitive impairment (MCI), which is a symptomatic pre-dementia phase of AD. This work proposes two methods based on statistical learning methods, which are focused on characterising the ageing-related changes in brain structures of healthy elderly controls (HC), MCI and AD subjects, and addressing the estimation of the current diagnosis (ECD) of HC, MCI and AD, as well as the prediction of future diagnosis (PFD) of these groups mainly focused on the early diagnosis of conversion from MCI to AD. Data correspond to longitudinal neurodegeneration measurements from Magnetic Resonance Imaging (MRI) images. These biomarkers were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS). ADNI data includes MRI biomarkers available at a 5-year follow up on HC, MCI and AD subjects, while OASIS data only includes biomarkers measured at baseline on HC and AD. In the first method, called M-res, variant (vr) and quasi-variant (qvr) biomarkers were identified on HC subjects by using a Linear Mixed Effects (LME) approach on males and females, separately. Then, we built an ageing-based null model, which would characterise the normal atrophy and growth patterns of vr and qvr biomarkers, as well as the correlation between them. By using the null model on those subjects who had been clinically diagnosed as HC, MCI or AD, normal age-related changes were estimated, and then, their deviation scores (residuals) from the observed MRI-based biomarkers were computed. In contrast to M-res, the second method, called M-raw, is focused on directly analyzing the raw MRI-based biomarkers values stratified by five-year age groups. M-raw includes a differential diagnosis-specific feature selection (FS) method, which is applied before classification. In both methods, the differential diagnosis problem was addressed by building Support Vector Machines (SVM) models to carry out three main experiments—AD vs. HC, MCI vs. HC, and AD vs. MCI. In M-res, the SVM models were trained by using as input the residuals computed for the vr biomarkers plus the age, whereas in M-raw, we used the pool of selected features plus age, gender and years of education. The advancement of early disease prediction was calculated as the average number of years advanced in the PFD of the subjects concerning the last known clinical diagnosis. Finally, the ability of both methods to correctly discriminate AD vs. HC subjects was evaluated and compared by testing them on OASIS subjects observed at baseline. Results confirm accelerated or reduced estimates of decline in all cortical biomarkers with increasing age and a frontotemporal pattern of atrophy in HC subjects, as well as in MCI and AD. Regarding the ECD problem, all SVM models obtained better results than comparable methods in the literature for most classification quality indicators, especially on AD vs. HC. Both methods also improve the PFD given the current clinical tests, both in prediction quality indicators and the amount of time by which the diagnosis is advanced.La enfermedad de Alzheimer (AD) es un trastorno progresivo y neurodegenerativo caracterizado por cambios patológicos en el cerebro que comienzan varios años antes de aparecer los primeros síntomas clínicos. La identificación temprana y precisa de estos cambios ayuda a mejorar el diagnóstico y la monitorización, permitiendo que la enfermedad sea abordada en sus primeras etapas, antes de producirse un deterioro morfológico y mental irreversible. El cerebro de los sujetos con AD se reduce significativamente a medida que avanza la enfermedad, siendo el envejecimiento el principal factor de riesgo para la AD esporádica, donde los cerebros de la gente mayor son más susceptibles que los más jóvenes. Sin embargo, ha sido observado que los cerebros de los adultos mayores y de los sujetos en una fase anterior con deterioro cognitivo leve (MCI) pierden materia en regiones relacionadas con AD. Esta tesis propone dos métodos basados en métodos de aprendizaje estadísticos, que se centran en caracterizar los cambios relacionados con el envejecimiento en estructuras cerebrales de controles sanos de edad avanzada (HC), MCI y AD, y en abordar la estimación del diagnóstico actual (ECD) de estos grupos, así como la predicción de su diagnóstico futuro (PFD), principalmente en el diagnóstico precoz de la conversión de MCI a AD. Los datos utilizados corresponden a biomarcadores de neurodegeneración longitudinal obtenidas de imágenes de Resonancia Magnética (MRI). Estos biomarcadores se obtuvieron a partir de los estudios Alzheimer?s Disease Neuroimaging Initiative (ADNI) y Open Access Series of Imaging Studies (OASIS). Los datos de ADNI incluyeron biomarcadores de MRI disponibles en un seguimiento de 5 años en sujetos HC, MCI y AD, mientras que los datos de OASIS solo incluyeron biomarcadores medidos al inicio del estudio en HC y AD. En el primer método, denominado M-res, los biomarcadores que cambiaron significativamente (vr) y los que cambiaron en una reducida escala (qvr) fueron identificados en sujetos HC utilizando modelos lineales de efectos mixtos (LME). Asimismo, modelos nulos basados en el normal envejecimiento del cerebro fueron construidos para cada género. A través de estos ellos se buscó caracterizar la atrofia normal y los patrones de crecimiento de los biomarcadores vr y qvr, así como la correlación entre ellos. Estos modelos fueron utilizados en los sujetos HC, MCI y AD restantes para inferir los valores normales de los biomarcadores vr y luego calcular sus desviaciones (residuos) respecto a los biomarcadores observados. A diferencia de M-res, el segundo método denominado M-raw, se centra en el análisis de los valores directos de los biomarcadores MRI, estratificados por grupos de edad de cinco años. M-raw incluye un método de selección de características específicas del diagnóstico diferencial aplicado antes de la clasificación. En ambos métodos, se entrenaron máquinas soporte vectorial (SVM) para abordar tres experimentos: AD vs. HC, MCI vs. HC y AD vs. MCI. En M-res, los modelos SVM fueron entrenados a partir de los residuos calculados para los biomarcadores vr más la edad, mientras que en M-raw, se utilizó el grupo de características seleccionadas más la edad, el sexo y los años de educación. El avance de la predicción temprana de la enfermedad fue calculada como el promedio de años avanzados en el PFD con respecto al último diagnóstico clínico conocido. Los resultados confirman una reducción en todos los biomarcadores corticales a medida que la edad avanza, siendo el cambio de algunas regiones más acelerados que otras. Asimismo, se observó un patrón de atrofia frontotemporal en los tres grupos de sujetos. Con respecto al problema ECD, todos los modelos SVM obtuvieron mejor desempeño en la clasificación que los métodos comparables en la literatura, especialmente en AD vs. HC. Ambos métodos también mejoraron la PFD, tanto en los indicadores de calidad de predicción como en el tiempo de avance en el diagnóstico (hasta 1.87 años antes en sujetos de 80-84 años).Postprint (published version

3D - Patch Based Machine Learning Systems for Alzheimer’s Disease classification via 18F-FDG PET Analysis

abstract: Alzheimer’s disease (AD), is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. It is the cause of 60% to 70% of cases of dementia. There is growing interest in identifying brain image biomarkers that help evaluate AD risk pre-symptomatically. High-dimensional non-linear pattern classification methods have been applied to structural magnetic resonance images (MRI’s) and used to discriminate between clinical groups in Alzheimers progression. Using Fluorodeoxyglucose (FDG) positron emission tomography (PET) as the pre- ferred imaging modality, this thesis develops two independent machine learning based patch analysis methods and uses them to perform six binary classification experiments across different (AD) diagnostic categories. Specifically, features were extracted and learned using dimensionality reduction and dictionary learning & sparse coding by taking overlapping patches in and around the cerebral cortex and using them as fea- tures. Using AdaBoost as the preferred choice of classifier both methods try to utilize 18F-FDG PET as a biological marker in the early diagnosis of Alzheimer’s . Addi- tional we investigate the involvement of rich demographic features (ApoeE3, ApoeE4 and Functional Activities Questionnaires (FAQ)) in classification. The experimental results on Alzheimer’s Disease Neuroimaging initiative (ADNI) dataset demonstrate the effectiveness of both the proposed systems. The use of 18F-FDG PET may offer a new sensitive biomarker and enrich the brain imaging analysis toolset for studying the diagnosis and prognosis of AD.Dissertation/ThesisThesis Defense PresentationMasters Thesis Computer Science 201

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

MODELING AND QUANTITATIVE ANALYSIS OF WHITE MATTER FIBER TRACTS IN DIFFUSION TENSOR IMAGING

Diffusion tensor imaging (DTI) is a structural magnetic resonance imaging (MRI) technique to record incoherent motion of water molecules and has been used to detect micro structural white matter alterations in clinical studies to explore certain brain disorders. A variety of DTI based techniques for detecting brain disorders and facilitating clinical group analysis have been developed in the past few years. However, there are two crucial issues that have great impacts on the performance of those algorithms. One is that brain neural pathways appear in complicated 3D structures which are inappropriate and inaccurate to be approximated by simple 2D structures, while the other involves the computational efficiency in classifying white matter tracts. The first key area that this dissertation focuses on is to implement a novel computing scheme for estimating regional white matter alterations along neural pathways in 3D space. The mechanism of the proposed method relies on white matter tractography and geodesic distance mapping. We propose a mask scheme to overcome the difficulty to reconstruct thin tract bundles. Real DTI data are employed to demonstrate the performance of the pro- posed technique. Experimental results show that the proposed method bears great potential to provide a sensitive approach for determining the white matter integrity in human brain. Another core objective of this work is to develop a class of new modeling and clustering techniques with improved performance and noise resistance for separating reconstructed white matter tracts to facilitate clinical group analysis. Different strategies are presented to handle different scenarios. For whole brain tractography reconstructed white matter tracts, a Fourier descriptor model and a clustering algorithm based on multivariate Gaussian mixture model and expectation maximization are proposed. Outliers are easily handled in this framework. Real DTI data experimental results show that the proposed algorithm is relatively effective and may offer an alternative for existing white matter fiber clustering methods. For a small amount of white matter fibers, a modeling and clustering algorithm with the capability of handling white matter fibers with unequal length and sharing no common starting region is also proposed and evaluated with real DTI data