Exact Topological Inference For Paired Brain Networks Via Persistent Homology

摘要: We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology. The is general enough dealing with any type of images such as twins, multimodal longitudinal images. exact nonparametric statistical inference procedure derived on testing monotonic graph theory features that do not rely time consuming permutation tests. proposed method computes the probability quadratic while tests require exponential time. As illustrations, we apply to simulated twin fMRI study. In case latter, determine significance heritability index large-scale reward network where every voxel node.