PT  - JOURNAL ARTICLE
AU  - Moo K. Chung
AU  - Victoria Villalta-Gil
AU  - Hyekyoung Lee
AU  - Paul J. Rathouz
AU  - Benjamin B. Lahey
AU  - David H. Zald
TI  - Exact Topological Inference for Paired Brain Networks <em>via</em> Persistent Homology
AID  - 10.1101/140533
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 140533
4099  - http://biorxiv.org/content/early/2017/05/22/140533.short
4100  - http://biorxiv.org/content/early/2017/05/22/140533.full
AB  - We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology. The framework is general enough for dealing with any type of paired images such as twins, multimodal and longitudinal images. The exact nonparametric statistical inference procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests. The proposed method computes the exact probability in quadratic time while the permutation tests require exponential time. As illustrations, we apply the method to simulated networks and a twin fMRI study. In case of the latter, we determine the statistical significance of the heritability index of the large-scale reward network where every voxel is a network node.