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Functional connectomics to study Autism Spectrum DisorderAuthor: VATIKA HARLALKA Date: 2019-05-16 Report no: IIIT/TH/2019/33 Advisor:Vinod P K AbstractAutism Spectrum Disorder (ASD) is a clinical umbrella term for neuro-developmental disorders that are characterized by atypical social behavior, including deficits in receptive and expressive language, theory of mind (TOM), and cognitive flexibility deficit. It encompasses autism, Asperger’s syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), and childhood dis-integrative disorder. Accumulating evidence indicates that ASD is associated with alterations of neural circuitry compared to typically developing (TD) individuals. Despite numerous studies, there is a lack of consensus on the nature of atypical functional connectivity in ASD. We hypothesize that studying the effects of age, disease and their interaction together using resting-state functional MRI (rs-fMRI) data can reveal crucial insights about the autistic brain. fMRI measures blood oxygen level dependent (BOLD) signals that indirectly maps the neuronal activity across different regions of the brain. In this thesis, we studied these effects using two paradigms: Firstly, we studied the effects of age, disease and their interaction on static functional connectivity by treating the brain as a complex graph in children and adolescents (9-16 years). Graph-theoretic analyses were performed on the functional brain network. We characterized the regions of interest (ROIs), connections and functional networks that are altered in development and in ASD. The multiple network measures representing the modular organization (integration and segregation) in the whole-brain were quantified. The results showed that ASD exhibits increased functional integration at the expense of decreased functional segregation. In ASD adolescents, there is significant decrease in modularity suggesting a less robust modular organization and an increase in participation coefficient suggesting more random integration and widely distributed connection edges. There is significant hypo-connectivity observed in the adolescent group especially in the Default Mode Network (DMN) while the children group shows both hyper- and hypo-connectivity. This lends support to a model of global atypical connections and further identifies functional networks and regions that are independently affected by age, disease and their interaction. Secondly, we have also explored flexibility of the brain as a novel approach to characterize ASD using the temporal dynamics of BOLD signals. The dynamic variability in the connection strength and the modular organization in terms of the measures flexiblity, cohesion strength and disjointness were explored for each subject to characterize the effects of age, disease and their interaction. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We found that the whole-brain flex-ibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD and overcomes the limitations of the analyses that consider ASD as a single group. Overall, this thesis adopts a systematic approach to study pathology and development using static as well as dynamic functional connectivity. These are very useful in characterizing the topology of the brain along with the patterns in transient connectivity. Full thesis: pdf Centre for Computational Natural Sciences and Bioinformatics |
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