@inproceedings{5a3f758fc00342e188ae1c9b0980d8c3,
title = "Group-wise sparse representation of brain states reveal network abnormalities in mild traumatic brain injury",
abstract = "Mild traumatic brain injury (mTBI) is a leading public health care burden. Recent research has shown that the functional impairment in mTBI patients could be captured by resting state fMRI (rsfMRI) at network level. Moreover exploring brain response to mTBI over time at large scale network level can help physicians better diagnose brain injury and order appropriate rehabilitation plan. Therefore, there is a need for methodological innovation that could assess brain impairment in rsfMRI data and further define biomarkers for network changes. In this paper, we propose a novel group-wise sparse representation of brain states (GSRBS) approach, based on rsfMRI data, to explore the effect of mTBI on functional networks across different groups and longitudinal stages. Specifically, a dictionary of brain networks is learned from the volumes of rsfMRI data, and at each time point these networks are linearly and sparsely combined to realize a brain state. Our results showed that group-wise statistical difference on the network composition of brain states could be found between healthy controls and mTBI patients at two different temporal stages.",
keywords = "brain network, fMRI, MTBI, sparse coding",
author = "Jinglei Lv and Armin Iraji and Hanbo Chen and Fangfei Ge and Lei Guo and Xin Zhang and Zhifeng Kou and Tianming Liu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 ; Conference date: 13-04-2016 Through 16-04-2016",
year = "2016",
month = jun,
day = "15",
doi = "10.1109/ISBI.2016.7493210",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "58--61",
booktitle = "2016 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}