Neuro-Hotnet: A Graph Theoretic Approach for Brain FC Estimation

Functional connectivity (FC) for quantifying interactions between regions of the brain is commonly estimated from functional magnetic resonance imaging (fMRI). There has been increasing interest in the potential of multimodal imaging to obtain more robust estimates of FC in high-dimensional settings. Recent work has found uses for graphical algorithms in combining fMRI signals with structural connectivity estimated from diffusion tensor imaging (DTI) for FC estimation. At the same time new algorithms focused on de novo identification of graphical subnetworks with significant levels of connectivity are finding other biological applications with great success. Such algorithms develop notions of graphical influence that aid in revealing subnetworks of interest while maintaining rigorous statistical control on discoveries. We develop a novel algorithm adapting some of these methods to FC estimation with computational efficiency and scalability. Our proposed algorithm leverages a graphical random walk on DTI data to define a new measure of structural influence that highlights connected components of maximal interest. The subnetwork topology is then compared to a suitable null hypothesis using permutation testing. Finally, individual discovered components are tested for significance. Extensive simulations show our method is comparable in power to those currently in use while being fast, robust, and simple to implement. We also analyze task-fMRI data from the Human Connectome Project database and find novel insights into brain interactions during the performance of a motor task. It is anticipated that the transparency and flexibility of our approach will prove valuable as further understanding of the structure-function relationship informs the future of network estimation. Scalability will also only become more important as neurological data become more granular and grow in dimension.