Scalable Machine Learning Methods for Massive Biomedical Data Analysis.

Modern data acquisition techniques have enabled biomedical researchers to collect and analyze datasets of substantial size and complexity. The massive size of these datasets allows us to comprehensively study the biological system of interest at an unprecedented level of detail, which may lead to the discovery of clinically relevant biomarkers. Nonetheless, the dimensionality of these datasets presents critical computational and statistical challenges, as traditional statistical methods break down when the number of predictors dominates the number of observations, a setting frequently encountered in biomedical data analysis. This difficulty is compounded by the fact that biological data tend to be noisy and often possess complex correlation patterns among the predictors. The central goal of this dissertation is to develop a computationally tractable machine learning framework that allows us to extract scientifically meaningful information from these massive and highly complex biomedical datasets. We motivate the scope of our study by considering two important problems with clinical relevance: (1) uncertainty analysis for biomedical image registration, and (2) psychiatric disease prediction based on functional connectomes, which are high dimensional correlation maps generated from resting state functional MRI.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111354/1/takanori_1.pd

An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals

In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.We acknowledge support by the Agencia Estatal de Investigación of Spain (CAIMAN, reference TEC2017-86921-C2-1-R and CLARA, reference RTI2018-099655-B-I00) and by the grant of the Ministry of Education and Science of the Russian Federation Agreement No. 074-02-2018-330

Extracting Generalizable Hierarchical Patterns Of Functional Connectivity In The Brain

The study of the functional organization of the human brain using resting-state functional MRI (rsfMRI) has been of significant interest in cognitive neuroscience for over two decades. The functional organization is characterized by patterns that are believed to be hierarchical in nature. From a clinical context, studying these patterns has become important for understanding various disorders such as Major Depressive Disorder, Autism, Schizophrenia, etc. However, extraction of these interpretable patterns might face challenges in multi-site rsfMRI studies due to variability introduced due to confounding variability introduced by different sites and scanners. This can reduce the predictive power and reproducibility of the patterns, affecting the confidence in using these patterns as biomarkers for assessing and predicting disease. In this thesis, we focus on the problem of robustly extracting hierarchical patterns that can be used as biomarkers for diseases. We propose a matrix factorization based method to extract interpretable hierarchical decomposition of the rsfRMI data. We couple the method with adversarial learning to improve inter-site robustness in multi-site studies, removing non-biological variability that can result in less interpretable and discriminative biomarkers. Finally, a generative-discriminative model is built on top of the proposed framework to extract robust patterns/biomarkers characterizing Major Depressive Disorder. Results on large multi-site rsfMRI studies show the effectiveness of our method in uncovering reproducible connectivity patterns across individuals with high predictive power while maintaining clinical interpretability. Our framework robustly identifies brain patterns characterizing MDD and provides an understanding of the manifestation of the disorder from a functional networks perspective which can be crucial for effective diagnosis, treatment and prevention. The results demonstrate the method\u27s utility and facilitate a broader understanding of the human brain from a functional perspective

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación (España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250 and A-TIC-080-UGR18 project

Diagnosing Autism Spectrum Disorder through Brain Functional Magnetic Resonance Imaging

Autism spectrum disorder (ASD) is a neurodevelopmental condition that can be debilitating to social functioning. Previous functional Magnetic Resonance Imaging (fMRI) classification studies have included only small subject sample sizes (n 50) and have seen high classification accuracy. The recent release of the Autism Brain Imaging Data Exchange (ABIDE) provides fMRI data for over 1,100 subjects. In our research, we derive a subject\u27s functional network connectivity (FNC) from their fMRI data and develop a regularized logistic classifier to determine whether a subject has autism. We obtained up to 65% classification accuracy, similar to other studies using the ABIDE dataset, suggesting that generalizing a classifier over a large number of subjects is much more difficult than smaller studies. The connectivity among several brain regions of ASD subjects were highlighted in the model as abnormal compared to the control subjects which potentially warrants future investigations about how these regions affect ASD. Although the classification accuracy was lower than what could be considered as clinically applicable, this research contributes to the continuing development of an automated classifier for diagnosing autism

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy

Blending generative models with deep learning for multidimensional phenotypic prediction from brain connectivity data

Network science as a discipline has provided us with foundational machinery to study complex relational entities such as social networks, genomics, econometrics etc. The human brain is a complex network that has recently garnered immense interest within the data science community. Connectomics or the study of the underlying connectivity patterns in the brain has become an important field of study for the characterization of various neurological disorders such as Autism, Schizophrenia etc. Such connectomic studies have provided several fundamental insights into its intrinsic organisation and implications on our behavior and health. This thesis proposes a collection of mathematical models that are capable of fusing information from functional and structural connectivity with phenotypic information. Here, functional connectivity is measured by resting state functional MRI (rs-fMRI), while anatomical connectivity is captured using Diffusion Tensor Imaging (DTI). The phenotypic information of interest could refer to continuous measures of behavior or cognition, or may capture levels of impairment in the case of neuropsychiatric disorders. We first develop a joint network optimization framework to predict clinical severity from rs-fMRI connectivity matrices. This model couples two key terms into a unified optimization framework: a generative matrix factorization and a discriminative linear regression model. We demonstrate that the proposed joint inference strategy is successful in generalizing to prediction of impairments in Autism Spectrum Disorder (ASD) when compared with several machine learning, graph theoretic and statistical baselines. At the same time, the model is capable of extracting functional brain biomarkers that are informative of individual measures of clinical severity. We then present two modeling extensions to non-parametric and neural network regression models that are coupled with the same generative framework. Building on these general principles, we extend our framework to incorporate multimodal information from Diffusion Tensor Imaging (DTI) and dynamic functional connectivity. At a high level, our generative matrix factorization now estimates a time-varying functional decomposition. At the same time, it is guided by anatomical connectivity priors in a graph-based regularization setup. This connectivity model is coupled with a deep network that predicts multidimensional clinical characterizations and models the temporal dynamics of the functional scan. This framework allows us to simultaneously explain multiple impairments, isolate stable multi-modal connectivity signatures, and study the evolution of various brain states at rest. Lastly, we shift our focus to end-to-end geometric frameworks. These are designed to characterize the complementarity between functional and structural connectivity data spaces, while using clinical information as a secondary guide. As an alternative to the previous generative framework for functional connectivity, our representation learning scheme of choice is a matrix autoencoder that is crafted to reflect the underlying data geometry. This is coupled with a manifold alignment model that maps from function to structure and a deep network that maps to phenotypic information. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive yet interpretable brain biomarkers. Finally, we also present a preliminary analytical and experimental exposition on the theoretical aspects of the matrix autoencoder representation

Predicting clinical variables from neuroimages using fused sparse group lasso

Predictive models in which neuroimage features serve as predictors and a clinical variable is modeled as the outcome are good candidates for clinical application because (1) they can exploit dependencies between predictor variables and thus potentially explain more variability in the outcome than a mass univariate approach, and (2) they allow inference at the individual level, such that a prediction can be obtained for a new individual whose data was not used to train the model. This dissertation proposes methods for neuroimaging prediction models that not only aim for predictive accuracy, but also seek interpretability and potential insight into the underlying pathophysiology of neuropsychiatric disorders. In the first part of this dissertation we propose the fused sparse group lasso penalty, which encourages structured, sparse, interpretable solutions by incorporating prior information about spatial and group structure among voxels. We derive optimization steps for fused sparse group lasso penalized regression using the alternating direction method of multipliers algorithm. With simulation studies, we demonstrate conditions under which fusion and group penalties together outperform either of them alone. We then use fused sparse group lasso to predict continuous measures from resting state magnetic resonance imaging data using the Autism Brain Imaging Data Exchange dataset. In the second part of this dissertation we use fused sparse group lasso to predict age from multimodal neuroimaging data in a sample of cognitively normal adults aged 65 and older. In general, we show how the incorporation of prior information via the fused sparse group lasso penalty can enhance the interpretability of neuroimaging predictive models while also yielding good predictive performance. Public health significance: Psychiatric disorders and neurological diseases such as Alzheimer's present a large public health burden. As of yet, there have been relatively few translations of basic neuroscience findings to clinical applications in psychiatry. Prediction models using neuroimaging data can potentially help clinicians with diagnosis and prediction of prognosis and treatment response. Establishing interpretable neuroimaging-based biomarkers can improve our understanding of the neurobiological mechanisms underlying neuropsychiatric disorders and suggest approaches for prevention and treatment