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

Harshit S. Parmar; Brian Nutter; Rodney Long; Sameer Antani; Sunanda Mitra

doi:10.1117/12.2512968

15 March 2019 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

Harshit S. Parmar, Brian Nutter, Rodney Long, Sameer Antani, Sunanda Mitra

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109531E (2019) https://doi.org/10.1117/12.2512968
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

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Citation Download Citation

Harshit S. Parmar, Brian Nutter, Rodney Long, Sameer Antani, and Sunanda Mitra "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", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531E (15 March 2019); https://doi.org/10.1117/12.2512968

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