Brain Connectivity Based Models For Identifying Autism Spectrum Disorder Using Functional Magnetic Resonance Imaging

Machine learning,especially deep learning,has emerged as an up-and-coming tool for understanding the neural basis of brain disorders by analyzing and extracting hidden connectivity patterns from high dimensional neuroimaging data.These data-intensive approaches are increasingly being considered as having the potential to improve our understanding of brain disorders,as well as their accurate diagnosis and prognosis,especially if more massive neuroimaging databases are made available and improved feature extraction and training methods are developed based on existing data.This thesis focuses on the development of novel features extraction techniques and new machine learning models to automatically identify autism spectrum disorders using resting-state functional magnetic resonance imaging(Rsf MRI)data.In recent years,a considerable number of shallow and deep machine learning models have been proposed in neuroimaging studies of neurological disorders such as autism,epilepsy,Alzheimer's disease,and attention deficit disorder.Moreover,these models have been instrumental in the discovery of aberrant brain regions.However,analyzing neuroimaging data entail many technical challenges,including a considerable number of preprocessing steps,dealing with many confounding factors,and high dimensionality.These problems and constraints make the development of suitable computational approaches more challenging.In order to tackle existing challenges and increase the classification accuracy,we resort to several methodologies,including adopting ‘optimal' neuroimaging processing pipelines,graph-theoretic models of the human brain,complex network-based feature selection,and supervised(shallow and deep)machine learning.In the current study,we aim at constructing valid connectivity-based predictors of autism spectrum disorder combined with appropriate classifiers that can yield high accuracy,precision,and recall.Moreover,such models can help clinicians to better understand atypical brain regions and connectivity patterns under this debilitating pathology and make improved diagnosis and prognosis.The main contributions of our efforts are: 1)we develop a novel framework for identifying autism disorder using community features and linear discriminant analysis model;2)we design a novel framework for identifying autism disorder based on egocentric connectivity features and extreme gradient boosting classifier(XGBoost);3)we propose novel multi-atlas deep learning models for identifying autism disorder that significantly outperform existing approaches;4)we localize and report aberrant connectivity patterns in the autistic brain that can be of great importance to the clinician.The encouraging results of this work provide insight into how to construct better predictors or biomarkers based on multisite and multi-atlas analysis of relatively large datasets made of autistic patients and healthy participants.Interestingly,the frameworks developed in this study are potentially applicable to identify additional neuropsychiatric disorders.