Static and Dynamic Functional Connectivity analysis in individuals with Autism Spectrum Disorder

Abstract

Autism Spectrum Disorder (ASD) is a lifelong heterogeneous developmental disorder that is characterized by abnormal development of the brain. Deficits in social skills, incapability to articulate language, abnormal sensory-motor movements, and stereotyped behaviors are mainly observed in children with ASD. The underlying biological markers for the diagnosis and treatment of ASD are not known yet. The current behavior-based diagnosis of ASD is arduous and requires expertise. It has been demonstrated that the non-invasive method such as resting-state functional magnetic resonance imaging (rs-fMRI) provides a means to examine functional connectivity patterns in the brain, which can help diagnose various neurodegenerative and psychiatric disorders, including ASD. Most previous studies associated ASD with atypical functional connectivity (FC) between different pairs of regions. However, brain connectivity is dynamic and varies extensively among brain states. In this thesis, we explored both static and dynamic functional connectivity based features to understand the fundamental group differences between ASD patients and typically developing (TD) subjects. Firstly, we proposed a Multilayer Perceptron (MLP) based classification model with autoencoder pretraining for classifying ASD from TD based on static functional connectivity (sFNC) extracted from rs-fMRI scans of the ABIDE-1 (a publicly available dataset from Autism Brain Imaging Data Exchange consortium). Our model achieves new state-of-the-art performance on the ABIDE-1 dataset with a 10- fold cross-validation accuracy of 74.82%. Further, we use the Integrated Gradients (IG) and DeepLIFT techniques to identify the correlations between brain regions that contribute most to the classification task. Our analysis identifies the following regions associated with ASD: Left Lingual Gyrus, Right Insula, Right Cuneus, Right Middle Frontal Gyrus, and Left Superior Temporal Gyrus. Interestingly, these regions in the brain are primarily responsible for social cognition, language, attention, decisionmaking, and visual processing, which are known to be altered in ASD. Secondly, we investigated the dynamic functional connectivity (dFNC) between 53 independent components among 188 ASD and 195 TD subjects sampled from the ABIDE-I consortium. We estimated dFNC using sliding window-based approaches and identified four distinct dynamic states through hard-clustering analysis. Hyper-connectivity within the cognitive control domain, between cognitive control and default mode network have been identified among ASD subjects. Hyper-connectivity within the default mode network has been found among TD individuals. Further, we estimated the dynamic temporal properties such as fractional time spent, mean dwell time per state and observed significant differences between ASD and TD groups. ASD subjects are found to have significantly longer dwell time in one of the states (4) when compared to TD individuals. We also found a significantly increased occurrence of the same state (4) in ASD subjects, whereas other states (1 and 3) are more frequent in TD subjects. Overall, both static and dynamic-based methods have been explored to find out the ASD biomarkers. While there is broad consensus in the brain network profiles between sFNC and dFNC, the temporal profile of brain state dynamics is additionally available with dFNC analysis and may potentially contribute to disease biomarkers. In addition, we have done extensive replication studies of both sFNC and dFNC based models for ASD classification or characterization in the literature. We observe great discrepancy between what is reported and what could be replicated or reproduced. Based on these analyses, we propose a set of recommendations for future studies that encompass factors such as dataset selection criteria, preprocessing pipepline, proper reporting of selected samples, atlas selection, and hyperparameter choices and reporting for the proposed models.