On characterizing population commonalities and subject variations in brain networks

Multiplex temporal measures reflecting neural underpinnings of brain functional connectivity under cognitive load in Autism Spectrum Disorder

Methods: Thirty individuals with ASD (11-18years) and thirty Typically Developing (TD) individuals (11-16years) were recruited to perform the mental task. The participants were instructed to flip the shown geometric images mentally and mark their response on a scale. The task-related multivariate EEG activations were analyzed using multiplex temporal Visibility Graphs (VGs) to compute local and global brain network functional connectivity dynamics.Results: With cognitive load (0-back to the 2-back task), the behavioral performance (d' index and Reaction Time) has reduced in ASD. The brain network has become more segregated and less integrated, reflecting more involvement of intra-regions over inter-regions in ASD. The frequent rerouting in hubs measured by Eigenvector Centrality (EC) indicated the progression of brain trajectory towards ASD. Overall, the neural mechanisms involving hyperactive response in frontal regions, frequent rewiring, and strengthened brain connectivity as a result of learning-induced performance reflected the adaptation to cognitive demands in ASD.Discussion: The correlation between complex graph measures and behavioral domain further reflected that neural metrics could predict the behavioral performance of the individuals in the task. In the future, modeling functional connectivity-based markers have the potential to reflect the brain trajectory alterations, which can detect ASD even before the behavioral manifestations become apparent.

Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification

High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance.