fMRI Deconvolution via Temporal Regularization using a LASSO model and the LARS algorithm

Abstract : In the context of functional MRI (fMRI), methods based on the deconvolution of the blood oxygenated level dependent (BOLD) signal have been developed to investigate the brain activity, without a need of a priori knowledge about activations occurrence [2]. In this work, we propose a novel temporal regularized deconvolution of the BOLD signal using the Least Absolute Shrinkage and Selection Operator (LASSO) model, solved by means of the Least-Angle Regression (LARS) algorithm. In this way, we were able to recover the underlying neurons activations and their dynamics.
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Isa Costantini, Patryk Filipiak, Kostiantyn Maksymenko, Samuel Deslauriers-Gauthier, Rachid Deriche. fMRI Deconvolution via Temporal Regularization using a LASSO model and the LARS algorithm. EMBC'18 - 40th International Engineering in Medicine and Biology Conference, Jul 2018, Honolulu, United States. ⟨hal-01855467⟩

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