@inproceedings{9206949ad72f4dfbac7817335645f297,
title = "Integration of network topological features and graph Fourier transform for fMRI data analysis",
abstract = "Motivated by the recent progress in both graph signal processing and brain imaging, we integrate both techniques for complex brain network analysis. In particular, we address the challenge of evaluating the difference of functional connectivity networks between different age groups from resting state functional magnetic resonance imaging (RS-fMRI) observations. We proposed an approach to combine commonly used topological features from complex network analysis with the Graph Fourier Transform (GFT). Since GFT contributes to find the significant subspace of the original signal while topological features reveal the morphological structure of the brain network, they provide complementary information for characterizing brain networks. The method was validated on resting-state fMRI imaging data from Philadelphia Neurodevelopmental Cohort (PNC) dataset, comprised of normally developing adolescents from 8 to 22. The result shows that the model works well in distinguishing different age groups with an accuracy of 86.64% for 389 subjects.",
keywords = "Graph Fourier transform, Lasso, Network, SVM, Topological features",
author = "Junqi Wang and Calhoun, {Vince D.} and Stephen, {Julia M.} and Wilson, {Tony W.} and Wang, {Yu Ping}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",
year = "2018",
month = may,
day = "23",
doi = "10.1109/ISBI.2018.8363530",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "92--96",
booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",
}