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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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