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Investigating Heritability Across Resting State Brain Networks via Heat Kernel Smoothing on Persistence Diagrams
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Investigating Heritability Across Resting State Brain Networks via Heat Kernel Smoothing on Persistence Diagrams
The brain's topological differences in resting state functional connectivity (rsfc) via resting state fMRI (rsfMRI) may provide important insight into brain function and dysfunction if heritable. Topological data analysis (TDA) is one such tool, robust to noise, for analyzing how the data's topological properties vary, and typically operates via filtration of the data and construction of a persistence diagram (PD). Therefore, the purpose of this study was to compute PDs to determine TDA-based heritability of static brain network topological features.
The brain's topological differences in resting state functional connectivity (rsfc) via resting state fMRI (rsfMRI) may provide important insight into brain function and dysfunction if heritable. Topological data analysis (TDA) is one such tool, robust to noise, for analyzing how the data's topological properties vary, and typically operates via filtration of the data and construction of a persistence diagram (PD). Therefore, the purpose of this study was to compute PDs to determine TDA-based heritability of static brain network topological features.
