Machine learning (ML) based analysis of neuroimages in neuropsychiatry context are advancing the understanding of neurobiological profiles and the pathological bases of neuropsychiatric disorders. Computational analysis and investigations on features derived from structural magnetic resonance imaging (sMRI) of the brain are used to quantify morphological or anatomical characteristics of the different regions of the brain that have role in several distinct brain functions. This helps in the realization of anatomical underpinnings of those disorders that cause brain atrophy. Structural neuroimaging data acquired from schizophrenia (SCZ), bipolar disorder (BD) patients and people who experienced psychosis for the first time, are used for the experiments presented in this thesis. The cerebral cortex (i.e., gray matter) of the brain is one of the most studied anatomical part using 'cortical-average-thickness' distribution feature in the literature. This helps in the realization of the anatomical underpinning of those mental illnesses that cause brain atrophy. To this regard, based on statistical background, 'cortical-skewness' feature, a novel digital imaging-derived neuroanatomical biomarker that could potentially assist in the differentiation of healthy control (HC) and patient groups is proposed and tested in this thesis. The core theme of machine intelligence relies in extracting and learning patterns of input data from experience. Classification is one of the task. In a basic set up, ML algorithms are trained using exemplary multivariate data features and its associated class labels, so that they could be able to create models and do predictive classification and other tasks. Considering the conundrum nature of psychiatric disorders, researchers in the field, could benefit from ML based analysis of complex brain patterns. Out of many, one task is computer aided classification (CAC). This is achieved by training the algorithms, these complex brain patterns and their corresponding diagnostic statistics manual (DSM) based clinical gold standard labels. Indeed, in the literature, supervised learning methods such as support vector machines (SVM) which follow inductive learning strategy are widely exploited and achieved interesting results. Observing this and due to the fact that the most widely available relevant anatomical features of the cortex such as thickness and volume values, could not be considered satisfactory features because of the heterogeneous nature of the human brain anatomy due to differences in age, gender etc., a contextual similarity based learning is proposed. This learning uses a transductive learning mechanism (i.e, learn a specific function for the problem at hand) instead of learning a general function to solve a specific problem. Based on this, it is adopted, a formulation of a semi supervised graph transduction (label propagation) algorithm based on the notions of game theory, where the consistent labeling is represented with Nash equilibrium, to tackle the problem of learning from neuroimages with subtle microscopic difference among different clinical groups. However, since such kind of algorithms heavily rely on the graph structure of the extracted features, we extended the classification procedure by introducing a pre-training phase based on a distance metric learning strategy with the aim of enhancing the contextual similarity of the images by providing a 'must belong in the same class' and 'must not belong in the same class' constraint from the available training data. This would result to increase intra-class similarity and decrease inter-class similarity. The proposed classification pipeline is used for searching anatomical biomarkers. With the goal of identifying potential neuroanatomical markers of a psychiatric disorder, it is aimed to develop a feature selection strategy taking into consideration the widely exploited cortical thickness and the proposed skewness feature, with the objective of searching a combination of features from all cortical regions of the brain that could maximize the possible differentiation among the different clinical groups Considering Research Domain Criteria (RDoC) framework developed by National Institute of Mental Health (NIMH) with the aim of developing biologically valid perspective of mental disorders by integrating multimodal sources, clinical interview scores and neuroimaging data are used with ML methods to tackle the challenging problem of differential classification of BD vs. SCZ. Finally, as deep learning methods are emerging with remarkable results in several application domains, we adopted this class of methods especially convolutional neural networks (CNNs) with a 3D approach, to extract volumetric neuroanatomical markers. CAC of first episode psychosis (FEP) is performed by exploiting the 3D complex spatial structure of the brain to identify key regions of the brain associated with the pathophysiology of FEP. Testing of individualized predictions with big dataset of 855 structural scans to identify possible markers of the disease is performed.
MACHINE LEARNING BASED ANALYSIS AND COMPUTER AIDED CLASSIFICATION OF NEUROPSYCHIATRIC DISORDERS USING NEUROIMAGING / T.m. Dagnew ; supervisor: R. Sassi ; coordinator: P. Boldi. - Milano : Università degli studi di Milano. DIPARTIMENTO DI INFORMATICA Giovanni Degli Antoni, 2019 Feb 01. ((31. ciclo, Anno Accademico 2018.
|Titolo:||MACHINE LEARNING BASED ANALYSIS AND COMPUTER AIDED CLASSIFICATION OF NEUROPSYCHIATRIC DISORDERS USING NEUROIMAGING|
|Supervisori e coordinatori interni:||SASSI, ROBERTO|
|Data di pubblicazione:||1-feb-2019|
|Parole Chiave:||Machine Learning ; Magnetic Resonance Imaging (MRI) ; Neuroimage analysis ; Neuropsychiatry ; Medical image analysis|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Citazione:||MACHINE LEARNING BASED ANALYSIS AND COMPUTER AIDED CLASSIFICATION OF NEUROPSYCHIATRIC DISORDERS USING NEUROIMAGING / T.m. Dagnew ; supervisor: R. Sassi ; coordinator: P. Boldi. - Milano : Università degli studi di Milano. DIPARTIMENTO DI INFORMATICA Giovanni Degli Antoni, 2019 Feb 01. ((31. ciclo, Anno Accademico 2018.|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.13130/dagnew-tewodros-mulugeta_phd2019-02-01|
|Appare nelle tipologie:||Tesi di dottorato|