@inproceedings{c06e1d290bb1486789aea332850b7b7e,
title = "Automated signal drift and global fluctuation removal from 4D fMRI data based on principal component analysis as a major preprocessing step for fMRI data analysis",
abstract = "Temporal signal drift is one of the significant artifacts in functional Magnetic Resonance Imaging (fMRI) data that is not given as much attention as motion or physiological artifacts. However, signal drift if not accounted for, can introduce spurious correlation between different regions in resting state fMRI data. Hence detection and removal of signal drift is an important preprocessing step in fMRI data analysis. Here we propose an automated data driven approach that makes use of Principal Component Analysis (PCA) to eliminate not only low frequency signal drift but also spontaneous high frequency global signal fluctuations. This approach is also able to identify the most dominant component for each voxel separately. For task fMRI, this can help us identify regions that respond in a time locked manner to the experiment paradigm. Such regions can be thought of as activation regions. The dominant principal components corresponding to such regions can also be used to investigate intra-region Hemodynamic Response (HR) variability within subjects and across subjects.",
keywords = "PCA, fMRI, global signal fluctuations, preprocessing, signal drift",
author = "Parmar, {Harshit S.} and Brian Nutter and Rodney Long and Sameer Antani and Sunanda Mitra",
note = "Publisher Copyright: {\textcopyright} 2019 SPIE.; Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 19-02-2019 Through 21-02-2019",
year = "2019",
doi = "10.1117/12.2512968",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Barjor Gimi and Andrzej Krol",
booktitle = "Medical Imaging 2019",
}