@inproceedings{9888e2b707894e9792ea9577c7a35f63,
title = "The model order limit: Deep sparse factorization for resting brain",
abstract = "The decomposition of resting state Functional Magnetic Resonance Imaging (rs-fMRI) data by Dictionary Learning (DL) using sparsity constraint have been recently shown to be an alternative to traditional consideration of independence criteria to obtain Resting State Networks. However, a single level decomposition is not suitable when applied for group rs-fMRI analysis, as it requires a large model order or an equivalently large number of dictionary atoms to appropriately identify distinct brain networks. This is computationally expensive for group rs-fMRI analysis in resource constraint environments. In this work, we have proposed a deep sparse factorization method for multi-level decomposition of rs-fMRI data. Preliminary results from this study show that a multi-layer framework with very small model order can reveal better spatial maps of resting brain that share equivalent temporal dynamics. In addition, the proposed approach outperforms the existing DL approaches with single level decomposition in the metric of quality as well as computational complexity.",
keywords = "Deep Sparse Factorization (DSF), Dictionary Learning (DL), Functional Connectivity, Rs-fMRI",
author = "Debadatta Dash and Vinayak Abrol and Sao, {Anil Kumar} and Bharat Biswal",
year = "2018",
month = may,
day = "23",
doi = "10.1109/ISBI.2018.8363796",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1244--1247",
booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",
address = "United States",
note = "15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",
}