Abstract #3089
Differentiating neuronal and non-neuronal contributions in BOLD signal using multimodal recordings and multi-echo EPI
Han Yuan 1 , Callen Johnson 1,2 , Raquel Phillips 1 , Vadim Zotev 1 , Masaya Misaki 1 , and Jerzy Bodurka 1,3
1
Laureate Institute for Brain Research,
Tulsa, OK, United States,
2
Department
of Physics, University of Tulsa, Tulsa, OK, United
States,
3
College
of Engineering, University of Oklahoma, Norman, OK,
United States
We investigated the resting state brain dynamics using
single-shot multi-echo EPI sequence with simultaneously
acquired electroencephalography (EEG) and respiratory
data. Multi-echo EPI images were decomposed into
linearly weighted components based on differentiated
TE-dependent signals using spatial independent component
analysis (ICA). The BOLD signal of neuronal or
non-neuronal/physiological origin was differentiated by
comparing the time course of EPI independent components
with the variations of EEG alpha power and respiratory
volumes. Results show that the multi-echo ICA approach
based on the multimodal data is able to decompose the
BOLD signals into components of neuronal and
non-neuronal origin, and thus can be used to remove the
physiological noise of BOLD signals.
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