Enhanced identification of BOLD-like components with multi-echo simultaneous multi-slice (MESMS) fMRI and multi-echo ICA

• DOI: 10.1016/j.neuroimage.2015.02.052
• Publication Date: 2015-03-02T07:01:55Z
• Authors: Valur Olafsson; Prantik Kundu; Eric C. Wong; Peter A. Bandettini; Thomas T. Liu
• Keywords: Adult; Male; Principal Component Analysis; Neurology & Neurosurgery; Echo-Planar Imaging; Image Processing; Psychology and Cognitive Sciences; Neurosciences; Bioengineering; Medical and Health Sciences; Magnetic Resonance Imaging; Oxygen; 03 medical and health sciences; Computer-Assisted; 0302 clinical medicine; Clinical Research; Image Processing, Computer-Assisted; Humans; Female; Artifacts

The recent introduction of simultaneous multi-slice (SMS) acquisitions has enabled the acquisition of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data with significantly higher temporal sampling rates. In a parallel development, the use of multi-echo fMRI acquisitions in conjunction with a multi-echo independent component analysis (ME-ICA) approach has been introduced as a means to automatically distinguish functionally-related BOLD signal components from signal artifacts, with significant gains in sensitivity, statistical power, and specificity. In this work, we examine the gains that can be achieved with a combined approach in which data obtained with a multi-echo simultaneous multi-slice (MESMS) acquisition are analyzed with ME-ICA. We find that ME-ICA identifies significantly more BOLD-like components in the MESMS data as compared to data acquired with a conventional multi-echo single-slice acquisition. We demonstrate that the improved performance of MESMS derives from both an increase in the number of temporal samples and the enhanced ability to filter out high-frequency artifacts.