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

Machine Learning Methods for Structural Brain MRIs: Applications for Alzheimer’s Disease and Autism Spectrum Disorder

This thesis deals with the development of novel machine learning applications to automatically detect brain disorders based on magnetic resonance imaging (MRI) data, with a particular focus on Alzheimer’s disease and the autism spectrum disorder. Machine learning approaches are used extensively in neuroimaging studies of brain disorders to investigate abnormalities in various brain regions. However, there are many technical challenges in the analysis of neuroimaging data, for example, high dimensionality, the limited amount of data, and high variance in that data due to many confounding factors. These limitations make the development of appropriate computational approaches more challenging. To deal with these existing challenges, we target multiple machine learning approaches, including supervised and semi-supervised learning, domain adaptation, and dimensionality reduction methods.In the current study, we aim to construct effective biomarkers with sufficient sensitivity and specificity that can help physicians better understand the diseases and make improved diagnoses or treatment choices. The main contributions are 1) development of a novel biomarker for predicting Alzheimer’s disease in mild cognitive impairment patients by integrating structural MRI data and neuropsychological test results and 2) the development of a new computational approach for predicting disease severity in autistic patients in agglomerative data by automatically combining structural information obtained from different brain regions.In addition, we investigate various data-driven feature selection and classification methods for whole brain, voxel-based classification analysis of structural MRI and the use of semi-supervised learning approaches to predict Alzheimer’s disease. We also analyze the relationship between disease-related structural changes and cognitive states of patients with Alzheimer’s disease.The positive results of this effort provide insights into how to construct better biomarkers based on multisource data analysis of patient and healthy cohorts that may enable early diagnosis of brain disorders, detection of brain abnormalities and understanding effective processing in patient and healthy groups. Further, the methodologies and basic principles presented in this thesis are not only suited to the studied cases, but also are applicable to other similar problems

Integrated Structural And Functional Biomarkers For Neurodegeneration

Alzheimer\u27s Disease consists of a complex cascade of pathological processes, leading to the death of cortical neurons and development of dementia. Because it is impossible to regenerate neurons that have already died, a thorough understanding of the earlier stages of the disease, before significant neuronal death has occurred, is critical for developing disease-modifying therapies. The various components of Alzheimer\u27s Disease pathophysiology necessitate a variety of measurement techniques. Image-based measurements known as biomarkers can be used to assess cortical thinning and cerebral blood flow, but non-imaging characteristics such as performance on cognitive tests and age are also important determinants of risk of Alzheimer\u27s Disease. Incorporating the various imaging and non-imaging sources of information into a scientifically interpretable and statistically sound model is challenging. In this thesis, I present a method to include imaging data in standard regression analyses in a data-driven and anatomically interpretable manner. I also introduce a technique for disentangling the effect of cortical structure from blood flow, enabling a clearer picture of the signal carried by cerebral blood flow beyond the confounding effects of anatomical structure. In addition to these technical developments in multi-modal image analysis, I show the results of two clinically-oriented studies focusing on the relative importance of various biomarkers for predicting presence of Alzheimer\u27s Disease pathology in the earliest stages of disease. In the first, I present evidence that white matter hyperintensities, a marker of small vessel disease, are more highly associated with Alzheimer\u27s Disease pathology than current mainstream imaging biomarkers in elderly control patients. In the second, I show that once Alzheimer\u27s Disease has progressed to the point of noticeable cognitive decline, cognitive tests are as predictive of presence of Alzheimer\u27s pathology as standard imaging biomarkers. Taken together, these studies demonstrate that the relative importance of biomarkers and imaging modalities changes over the course of disease progression, and sophisticated data-driven methods for combining a variety of modalities is likely to lead to greater biological insight into the disease process than a single modality

Investigation of Multi-dimensional Tensor Multi-task Learning for Modeling Alzheimer's Disease Progression

Machine learning (ML) techniques for predicting Alzheimer's disease (AD) progression can significantly assist clinicians and researchers in constructing effective AD prevention and treatment strategies. The main constraints on the performance of current ML approaches are prediction accuracy and stability problems in medical small dataset scenarios, monotonic data formats (loss of multi-dimensional knowledge of the data and loss of correlation knowledge between biomarkers) and biomarker interpretability limitations. This thesis investigates how multi-dimensional information and knowledge from biomarker data integrated with multi-task learning approaches to predict AD progression. Firstly, a novel similarity-based quantification approach is proposed with two components: multi-dimensional knowledge vector construction and amalgamated magnitude-direction quantification of brain structural variation, which considers both the magnitude and directional correlations of structural variation between brain biomarkers and encodes the quantified data as a third-order tensor to address the problem of monotonic data form. Secondly, multi-task learning regression algorithms with the ability to integrate multi-dimensional tensor data and mine MRI data for spatio-temporal structural variation information and knowledge were designed and constructed to improve the accuracy, stability and interpretability of AD progression prediction in medical small dataset scenarios. The algorithm consists of three components: supervised symmetric tensor decomposition for extracting biomarker latent factors, tensor multi-task learning regression and algorithmic regularisation terms. The proposed algorithm aims to extract a set of first-order latent factors from the raw data, each represented by its first biomarker, second biomarker and patient sample dimensions, to elucidate potential factors affecting the variability of the data in an interpretable manner and can be utilised as predictor variables for training the prediction model that regards the prediction of each patient as a task, with each task sharing a set of biomarker latent factors obtained from tensor decomposition. Knowledge sharing between tasks improves the generalisation ability of the model and addresses the problem of sparse medical data. The experimental results demonstrate that the proposed approach achieves superior accuracy and stability in predicting various cognitive scores of AD progression compared to single-task learning, benchmarks and state-of-the-art multi-task regression methods. The proposed approach identifies brain structural variations in patients and the important brain biomarker correlations revealed by the experiments can be utilised as potential indicators for AD early identification

Multidimensional Feature Engineering for Post-Translational Modification Prediction Problems

Protein sequence data has been produced at an astounding speed. This creates an opportunity to characterize these proteins for the treatment of illness. A crucial characterization of proteins is their post translational modifications (PTM). There are 20 amino acids coded by DNA after coding (translation) nearly every protein is modified at an amino acid level. We focus on three specific PTMs. First is the bonding formed between two cysteine amino acids, thus introducing a loop to the straight chain of a protein. Second, we predict which cysteines can generally be modified (oxidized). Finally, we predict which lysine amino acids are modified by the active form of Vitamin B6 (PLP/pyridoxal-5-phosphate.) Our work aims to predict the PTM\u27s from protein sequencing data. When available, we integrate other data sources to improve prediction. Data mining finds patterns in data and uses these patterns to give a confidence score to unknown PTMs. There are many steps to data mining; however, our focus is on the feature engineering step i.e. the transforming of raw data into an intelligible form for a prediction algorithm. Our primary innovation is as follows: First, we created the Local Similarity Matrix (LSM), a description of the evolutionarily relatedness of a cysteine and its neighboring amino acids. This feature is taken two at a time and template matched to other cysteine pairs. If they are similar, then we give a high probability of it sharing the same bonding state. LSM is a three step algorithm, 1) a matrix of amino acid probabilities is created for each cysteine and its neighbors from an alignment. 2) We multiply the iv square of the BLOSUM62 matrix diagonal to each of the corresponding amino acids. 3) We z-score normalize the matrix by row. Next, we innovated the Residue Adjacency Matrix (RAM) for sequential and 3-D space (integration of protein coordinate data). This matrix describes cysteine\u27s neighbors but at much greater distances than most algorithms. It is particularly effective at finding conserved residues that are further away while still remaining a compact description. More data than necessary incurs the curse of dimensionality. RAM runs in O(n) time, making it very useful for large datasets. Finally, we produced the Windowed Alignment Scoring algorithm (WAS). This is a vector of protein window alignment bit scores. The alignments are one to all. Then we apply dimensionality reduction for gains in speed and performance. WAS uses the BLAST algorithm to align sequences within a window surrounding potential PTMs, in this case PLP attached to Lysine. In the case of WAS, we tried many alignment algorithms and used the approximation that BLAST provides to reduce computational time from months to days. The performances of different alignment algorithms did not vary significantly. The applications of this work are many. It has been shown that cysteine bonding configurations play a critical role in the folding of proteins. Solving the protein folding problem will help us to find the solution to Alzheimer\u27s disease that is due to a misfolding of the amyloid-beta protein. Cysteine oxidation has been shown to play a role in oxidative stress, a situation when free radicals become too abundant in the body. Oxidative stress leads to chronic illness such as diabetes, cancer, heart disease and Parkinson\u27s. Lysine in concert with PLP catalyzes the aminotransferase reaction. Research suggests that anti-cancer drugs will potentially selectively inhibit this reaction. Others have targeted this reaction for the treatment of epilepsy and addictions

A Novel Hybrid Ordinal Learning Model with Health Care Application

Ordinal learning (OL) is a type of machine learning models with broad utility in health care applications such as diagnosis of different grades of a disease (e.g., mild, modest, severe) and prediction of the speed of disease progression (e.g., very fast, fast, moderate, slow). This paper aims to tackle a situation when precisely labeled samples are limited in the training set due to cost or availability constraints, whereas there could be an abundance of samples with imprecise labels. We focus on imprecise labels that are intervals, i.e., one can know that a sample belongs to an interval of labels but cannot know which unique label it has. This situation is quite common in health care datasets due to limitations of the diagnostic instrument, sparse clinical visits, or/and patient dropout. Limited research has been done to develop OL models with imprecise/interval labels. We propose a new Hybrid Ordinal Learner (HOL) to integrate samples with both precise and interval labels to train a robust OL model. We also develop a tractable and efficient optimization algorithm to solve the HOL formulation. We compare HOL with several recently developed OL methods on four benchmarking datasets, which demonstrate the superior performance of HOL. Finally, we apply HOL to a real-world dataset for predicting the speed of progressing to Alzheimer's Disease (AD) for individuals with Mild Cognitive Impairment (MCI) based on a combination of multi-modality neuroimaging and demographic/clinical datasets. HOL achieves high accuracy in the prediction and outperforms existing methods. The capability of accurately predicting the speed of progression to AD for each individual with MCI has the potential for helping facilitate more individually-optimized interventional strategies.Comment: 16 pages, 3 figures, 2 table

How Valuable are Clinical Neuropsychological Assessments? A Meta-analysis of Neuropsychological Tests with Comparison to Common Medical Tests and Treatments

There has been a general decrease in neuropsychological assessments at a time when medical diagnostic technology and treatments have expanded, leading to a faulty assumption that medical tests and healthcare treatments provide more reliable or valid data than psychological assessments. A landmark report from the American Psychological Association’s (APA) Psychological Assessment Work Group (PAWG) found that validity coefficients for many psychological tests were indistinguishable from those of medical tests (Meyer et al., 2001). An updated systematic review of the advancement in neuropsychological testing is essential to the continued advancement of the value of neuropsychological assessment in healthcare. This meta-analysis sought to (1) summarize effect sizes of neuroimaging to diagnose dementia, medications to treat chronic diseases, and neuropsychological tests to diagnose dementia and TBI, (2) determine the differences (if any) in effect sizes between medical domains, and (3) determine the differences (if any) in effect sizes between medical domains and neuropsychological tests. EBSCO networks were searched for original research examining the efficacy of neuroimaging for Alzheimer’s Disease (AD), xi neuropsychological tests for AD and traumatic brain injury (TBI), and medication to treat memory impairment and cardiovascular events between clinical and control samples. Studies were coded using a complex multi-comparison, outcome, and subgroup schema. Data were analyzed under random-effects modeling. Of 6,668 studies identified, 78 were retained for primary and ancillary meta-analyses (715 effect sizes extracted; 35,810 clinical and 42,964 control participants represented). Primary results indicated a significant difference between domains, such that neuroimaging (g = -1.603) and neuropsychological tests (g = -1.591) both yielded greater effect sizes than medication studies (g = -0.009]. Secondary results indicated the AD neuropsychological test effect size [g = -2.213) was significantly different than the TBI neuropsychological test efficacy [g = -0.649; Q(1) = 42.821, p = 0.000]. Additionally, results indicated nonsignificant effect sizes for both memory impairment medications (g = -.052) and aspirin for cardiovascular events (g = .017). CONCLUSIONS: The diagnostic efficacy of neuroimaging and neuropsychological tests were both substantial and non-significantly different from one another. These findings provide clinicians and consumers with convincing evidence that neuropsychological tests are a reliable diagnostic tool for people with acquired and neurodegenerative brain disorders

Statistical Learning for Biomedical Data under Various Forms of Heterogeneity

In modern biomedical research, an emerging challenge is data heterogeneity. Ignoring such heterogeneity can lead to poor modeling results. In cancer research, clustering methods are applied to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis. A notable drawback of existing clustering methods is that they ignore the possibility that the variance of gene expression profile measurements can be heterogeneous across subgroups, leading to inaccurate subgroup prediction. In Chapter 2, we present a statistical approach that can capture both mean and variance structure in gene expression data. We demonstrate the strength of our method in both synthetic data and two cancer data sets. For a binary classification problem, there can be potential subclasses within the two classes of interest. These subclasses are latent and usually heterogeneous. We propose the Composite Large Margin Classifier (CLM) to address the issue of classification with latent subclasses in Chapter 3. The CLM aims to find three linear functions simultaneously: one linear function to split the data into two parts, with each part being classified by a different linear classifier. Our method has comparable prediction accuracy to a general nonlinear kernel classifier without overfitting the training data while at the same time maintaining the interpretability of traditional linear classifiers. There is a growing recognition of the importance of considering individual level heterogeneity when searching for optimal treatment doses. Such optimal individualized treatment rules (ITRs) for dosing should maximize the expected clinical benefit. In Chapter 4, we consider a randomized trial design where the candidate dose levels are continuous. To find the optimal ITR under such a design, we propose an outcome weighted learning method which directly maximizes the expected beneficial clinical outcome. This method converts the individualized dose selection problem into a nonstandard weighted regression problem. A difference of convex functions (DC) algorithm is adopted to efficiently solve the associated non-convex optimization problem. The consistency and convergence rates for the estimated ITR are derived and small-sample performance is evaluated via simulation studies. We illustrate the method using data from a clinical trial for Warfarin dosing.Doctor of Philosoph

How Valuable are Clinical Neuropsychological Assessments? A Meta-analysis of Neuropsychological Tests with Comparison to Common Medical Tests and Treatments

There has been a general decrease in neuropsychological assessments at a time when medical diagnostic technology and treatments have expanded, leading to a faulty assumption that medical tests and healthcare treatments provide more reliable or valid data than psychological assessments. A landmark report from the American Psychological Association’s (APA) Psychological Assessment Work Group (PAWG) found that validity coefficients for many psychological tests were indistinguishable from those of medical tests (Meyer et al., 2001). An updated systematic review of the advancement in neuropsychological testing is essential to the continued advancement of the value of neuropsychological assessment in healthcare. This meta-analysis sought to (1) summarize effect sizes of neuroimaging to diagnose dementia, medications to treat chronic diseases, and neuropsychological tests to diagnose dementia and TBI, (2) determine the differences (if any) in effect sizes between medical domains, and (3) determine the differences (if any) in effect sizes between medical domains and neuropsychological tests. EBSCO networks were searched for original research examining the efficacy of neuroimaging for Alzheimer’s Disease (AD),neuropsychological tests for AD and traumatic brain injury (TBI), and medication to treat memory impairment and cardiovascular events between clinical and control samples. Studies were coded using a complex multi-comparison, outcome, and subgroup schema. Data were analyzed under random-effects modeling. Of 6,668 studies identified, 78 were retained for primary and ancillary meta-analyses (715 effect sizes extracted; 35,810 clinical and 42,964 control participants represented). Primary results indicated a significant difference between domains, such that neuroimaging (g = -1.603) and neuropsychological tests (g = -1.591) both yielded greater effect sizes than medication studies (g = -0.009]. Secondary results indicated the AD neuropsychological test effect size [g = -2.213) was significantly different than the TBI neuropsychological test efficacy [g = -0.649; Q(1) = 42.821, p = 0.000]. Additionally, results indicated nonsignificant effect sizes for both memory impairment medications (g = -.052) and aspirin for cardiovascular events (g = .017). CONCLUSIONS: The diagnostic efficacy of neuroimaging and neuropsychological tests were both substantial and non-significantly different from one another. These findings provide clinicians and consumers with convincing evidence that neuropsychological tests are a reliable diagnostic tool for people with acquired and neurodegenerative brain disorders

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