Exact Topological Inference for Paired Brain Networks Persistent Homology.

Chung MK, Vilalta-Gil V, Lee H, Rathouz PJ, Lahey BB, Zald DH
Inf Process Med Imaging. 2017 2017: 299-310

PMID: 29075089 · PMCID: PMC5654491 · DOI:10.1007/978-3-319-59050-9_24

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.

MeSH Terms (9)

Algorithms Brain Brain Mapping Humans Image Enhancement Image Interpretation, Computer-Assisted Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity

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