Deconvolution of fMRI BOLD signal in time-domain using an exponential operator and Lasso optimization - Archive ouverte HAL Access content directly
Poster Communications Year :

Deconvolution of fMRI BOLD signal in time-domain using an exponential operator and Lasso optimization

(1, 2) , (1, 2) , (1, 2) , (1, 2) , (1, 2)
1
2

Abstract

Many techniques have been explored so far in the study of neural activations using the blood oxygenated level dependent (BOLD) signal. Among them, deconvolution methods have been developed in order to explore spontaneous brain activity when the brain is in resting-state. These techniques are powerful since they do not require a priori knowledge about timing and duration of activations [2]. In this work, we propose a regularized deconvolution technique which uses an exponential operator, whose shape and performance can be adjusted by tuning a parameter α, and the Least-Angle Regression (LARS) algorithm, by using the least absolute shrinkage and selection operator (LASSO) model.
Vignette du fichier
DEF_Poster_ICostantini (1).pdf (1.77 Mo) Télécharger le fichier

Dates and versions

hal-01713304 , version 1 (20-02-2018)

Identifiers

  • HAL Id : hal-01713304 , version 1

Cite

Isa Costantini, Patryk Filipiak, Kostiantyn Maksymenko, Rachid Deriche, Samuel Deslauriers-Gauthier. Deconvolution of fMRI BOLD signal in time-domain using an exponential operator and Lasso optimization. CoBCoM 2017 - Computational Brain Connectivity Mapping Winter School Workshop, Nov 2017, Juan Les Pins, France. ⟨hal-01713304⟩
92 View
14 Download

Share

Gmail Facebook Twitter LinkedIn More