Differential Effects of Brain Disorders on Structural and Functional Connectivity

Different measures of brain connectivity can be defined based on neuroimaging read-outs, including structural and functional connectivity. Neurological and psychiatric conditions are often associated with abnormal connectivity, but comparing the effects of the disease on different types of connectivity remains a challenge. In this paper, we address the problem of quantifying the relative effects of brain disease on structural and functional connectivity at a group level. Within the framework of a graph representation of connectivity, we introduce a kernel two-sample test as an effective method to assess the difference between the patients and control group. Moreover, we propose a common representation space for structural and functional connectivity networks, and a novel test statistics to quantitatively assess differential effects of the disease on different types of connectivity. We apply this approach to a dataset from BTBR mice, a murine model of Agenesis of the Corpus Callosum (ACC), a congenital disorder characterized by the absence of the main bundle of fibers connecting the two hemispheres. We used normo-callosal mice (B6) as a comparator. The application of the proposed methods to this data-set shows that the two types of connectivity can be successfully used to discriminate between BTBR and B6, meaning that both types of connectivity are affected by ACC. However, our novel test statistics shows that structural connectivity is significantly more affected than functional connectivity, consistent with the idea that functional connectivity has a robust topology that can tolerate substantial alterations in its structural connectivity substrate

Recent advances in supervised learning for brain graph classification

Modelling brain networks as graphs has become a dominant approach in neuroimaging. Substantial recent efforts in this area has led to a large number of new methods for analysing such brain graphs. In this paper, we review recent methods for estimating brain graphs and highlight some recent advances in predictive modelling on graphs. We divide the existing methods into three main categories, namely machine learning approaches, statistical hypothesis testing approaches, and network science ap- proaches, and discuss techniques associated with each approach as well as links between the approaches. Graph-based methods have strong roots in pattern recognition, computer vision, social sciences, and statistical physics, and many methods developed for brain graphs are readily transferable to other fields. We thus foresee this methodological upsurge in brain graph analysis will have a wide impact on applications beyond neuroimaging in years to come