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Activelets: Wavelets for sparse representation of hemodynamic responses

Title: Activelets: Wavelets for sparse representation of hemodynamic responses
Authors: Ildar Khalidov; Dimitri Van; De Ville; Jalal Fadili; Michael Unser; I. Introduction
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://miplab.epfl.ch/exchange/activelets.pdf.
Publication Year: 2011
Collection: CiteSeerX
Subject Terms: Index Terms
Description: We propose a new framework to extract the activity-related component in the BOLD functional Magnetic Resonance Imaging (fMRI) signal. As opposed to traditional fMRI signal analysis tech-niques, we do not impose any prior knowledge of the event timing. Instead, our basic assumption is that the activation pattern is a sequence of short and sparsely-distributed stimuli, as is the case in slow event-related fMRI. We introduce new wavelet bases, termed “activelets”, which sparsify the activity-related BOLD signal. These wavelets mimic the behavior of the differential operator underlying the hemodynamic system. To recover the sparse representation, we deploy a sparse-solution search algorithm. The feasibility of the method is evaluated using both synthetic and experimental fMRI data. The importance of the activelet basis and the non-linear sparse recovery algorithm is demonstrated by comparison against classical B-spline wavelets and linear regularization, respectively.
Document Type: text
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.451.9524; http://miplab.epfl.ch/exchange/activelets.pdf
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.451.9524; http://miplab.epfl.ch/exchange/activelets.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number: edsbas.FCF231E4
Database: BASE