Meeting Banner
Abstract #2668

Exploring Tensor Decomposition as an Alternative to ICA for Denoising Multi-Echo fMRI data

Eneko Uruñuela1, Miguel Ánguel Veganzones2, and César Caballero-Gaudes1
1Basque Center on Cognition, Brain and Language, Donostia - San Sebastián, Spain, 2University of Deusto, Donostia - San Sebastián, Spain

Synopsis

Keywords: Brain Connectivity, fMRI, Tensor DecompositionDenoising of the blood oxygen level-dependent signal is critical for the study of brain dynamics with functional MRI data. However, disentangling neurobiological signals from non-neurobiological ones such as head motion-related artifacts, and cardiac-related and respiration-related fluctuations. Multi-echo ICA approaches are often used to denoise the data by exploiting the echo-time dependence of the BOLD signal. Nevertheless, these rely on the optimally combined data and do not employ the information contained in the different echo-time signals. Here, we explore the potential of tensor decomposition techniques, which can simultaneously consider all the information available, as a way to process multi-echo fMRI data.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords