Abstract #2977
Multi-Echo Simultaneous Multi-Slice fMRI: Reliable High-Dimensional Decomposition and Unbiased Component Classification
Prantik Kundu 1 , Valur Olafsson 2 , Souheil Inati 3 , Peter Bandettini 1,3 , and Thomas Liu 4
1
Section on Functional Imaging Methods, NIMH,
Bethesda, MD, United States,
2
UCSD,
San Diego, CA, United States,
3
fMRI
Core Facility, NIMH, Bethesda, MD, United States,
4
Center
for Functional MRI, UCSD, San Diego, CA, United States
We demonstrate that a multi-echo (ME) approach to
simultaneous multi-slice (SMS) fMRI acquisition (TR<1s)
enables robust solutions to current challenges in SMS
data analysis using spatial ICA for connectivity
analysis and denoising. Unlike single-echo SMS
acquisition, which currently requires arbitrary
dimensionality estimation and denoising that is
dependent on a group-level templates, the ME approach
instead uses direct BOLD/non-BOLD dimensionality
detection and component classification. We show here
that: ME-ICA on ME-SMS data enables stable high
dimensionality estimates for resting and video
paradigms; BOLD components of cortex and subcortex show
clear TE-dependence; and importantly, SMS related
artifacts show clear [non-BOLD] TE-independence.
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