@inproceedings{568e053affcb475099d3742806146834,
title = "Recovering HRFs from overlapping ROIs in fMRI data using thresholding correlations for sparse dictionary learning",
abstract = "Recovering region-specific hemodynamic response function (HRF) in noisy fMRI data is essential to characterize the temporal dynamics of functionally coherent brain regions during activation. Data-driven techniques not based on sparsity fails to recover sub-region HRFs from overlapping regions of interest (ROIs) in task-related activations. This paper exploits spatial sparsity for recovering distinct HRFs from un-delineated overlapping ROIs in fMRI data. Spatial sparsity is realized using thresholding correlation for dictionary learning. The effectiveness of the proposed procedure is illustrated on both simulated and an experimental fMRI data obtained during a visual-task.",
keywords = "functional MRI, hemodynamic response function, overlapping ROIs, sparse dictionary learning",
author = "Adnan Shah and Khalid, {Muhammad Usman} and Seghouane, {Abd Krim}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 ; Conference date: 25-08-2015 Through 29-08-2015",
year = "2015",
month = nov,
day = "4",
doi = "10.1109/EMBC.2015.7319700",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5756--5759",
booktitle = "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015",
address = "United States",
}