Skip navigation
Skip navigation

A model-free de-drifting approach for detecting BOLD activities in fMRI data

Shah, Adnan

Description

A model-free method for efficiently capturing drifts in functional magnetic resonance imaging (fMRI) data is presented. The proposed algorithm applies a first order differencing to the fMRI time series samples in order to remove the drift effect. Initially, a consistent hemodynamic response function (HRF) of the fMRI voxel is estimated using linear least-squares. An optimal estimate of the drift is then obtained based on a wavelet thresholding technique applied to the generated...[Show more]

dc.contributor.authorShah, Adnan
dc.date.accessioned2015-03-11T00:37:04Z
dc.date.available2015-03-11T00:37:04Z
dc.identifier.issn1939-8018
dc.identifier.urihttp://hdl.handle.net/1885/12871
dc.description.abstractA model-free method for efficiently capturing drifts in functional magnetic resonance imaging (fMRI) data is presented. The proposed algorithm applies a first order differencing to the fMRI time series samples in order to remove the drift effect. Initially, a consistent hemodynamic response function (HRF) of the fMRI voxel is estimated using linear least-squares. An optimal estimate of the drift is then obtained based on a wavelet thresholding technique applied to the generated residuals after eliminating the induced activation response. Finally, the de-drifted fMRI voxel response is acquired by removing the estimated drift from the fMRI time-series. Its performance is assessed using simulated and motor-task real fMRI data sets obtained from both block and event-related designs. The application results reveal that the proposed method, which avoids the selection of a model to remove the drift component unlike traditional methods, is efficient in de-drifting the fMRI time-series and offers blood oxygen level-dependent (BOLD)-fMRI signal improvement and enhanced activation detection.
dc.format11 pages
dc.publisherSpringer Verlag
dc.rights© Springer Science+Business Media New York 2014
dc.sourceJournal of Signal Processing Systems
dc.subjectFunctional MRI
dc.subjectConsistent estimation
dc.subjectOptimal de-drifting
dc.subjectActivation detection
dc.titleA model-free de-drifting approach for detecting BOLD activities in fMRI data
dc.typeJournal article
local.identifier.citationvolume79
dcterms.dateAccepted2014-07-08
dc.date.issued2014
local.identifier.absfor080106 - Image Processing
local.identifier.ariespublicationa383154xPUB1838
local.publisher.urlhttp://link.springer.com/
local.type.statusPublished version
local.contributor.affiliationShah, Adnan, College of Engineering and Computer Science, Australian National University
local.identifier.essn1939-8115
local.bibliographicCitation.issue2
local.bibliographicCitation.startpage133
local.bibliographicCitation.lastpage143
local.identifier.doi10.1007/s11265-014-0926-8
dc.date.updated2015-12-10T11:19:27Z
local.identifier.scopusID2-s2.0-84904529730
CollectionsANU Research Publications

Download

There are no files associated with this item.


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator