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Abstract #0912

Deconvolution of multi-echo functional MRI data with Multivariate Multi-Echo Sparse Paradigm Free Mapping

Eneko Uruñuela-Tremiño1, Stefano Moia1, Javier Gonzalez-Castillo2, and César Caballero-Gaudes1

1Basque Center on Cognition, Brain and Language, Donostia - San Sebastián, Spain, 2Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States

This work introduces a novel multivariate deconvolution method to blindly estimate neuronal-related -signal changes in multi-echo fMRI data without prior knowledge of their timings. In contrast to current voxel-based approaches, this algorithm simultaneously deconvolves all brain’s voxel time series and uses structured spatio-temporal regularization to improve the quality of the estimates. Besides, it employs stability selection procedures to overcome the selection of regularization parameters of the deconvolution problem. We evaluate its performance in multiple realistic simulations, showing its potential to blindly detect BOLD events in paradigms when no prior information can be obtained.

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