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This paper proposes a novel approach of integrating different neuroimaging techniques to characterize an autistic brain. Different techniques like EEG, fMRI and DTI have traditionally been used to find biomarkers for autism, but there have been very few attempts for a combined or multimodal approach of EEG, fMRI and DTI to understand the neurobiological basis of autism spectrum disorder (ASD). Here, we explore how the structural brain network correlate with the functional brain network, such that the information encompassed by these two could be uncovered only by using the latter. In this paper, source localization from EEG using independent component analysis (ICA) and dipole fitting has been applied first, followed by selecting those dipoles that are closest to the active regions identified with fMRI. This allows translating the high temporal resolution of EEG to estimate time varying connectivity at the spatial source level. Our analysis shows that the estimated functional connectivity between two active regions can be correlated with the physical properties of the structure obtained from DTI analysis. This constitutes a first step towards opening the possibility of using pervasive EEG to monitor the long-term impact of ASD treatment without the need for frequent expensive fMRI or DTI investigations., Comment: 14 pages, 14 figures, IEEE Transactions on Cognitive and Developmental Systems, 2017

In this paper we utilized the concept of stable phase synchronization topography - synchrostates - over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data., Comment: 4 pages, 11 figures, 1 table