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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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