A Multi-Scale Analysis to set the Default Mode Network in noisy fMRI data
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1
National University of Colombia, Biomedical Engineering, Colombia
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2
University of Liege, Cyclotron Research Centre and Neurology Department, Belgium
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3
University of Liege, CHU Sart Tilman Hospital, Belgium
The Default Mode Network (DMN) is presently defined by those brain zones involved in maintaining a baseline brain activation. This network is usually revealed using Independent Component Analysis upon the fMRI data. However, a number of factors can easily perturb the acquired data, in particular a large head motion. Yet this problem has been partially overcome by registering the acquired brain volume, registration is still very limited in case of large head movements, a frequent scenario in patients with disorders of consciousness.
This article presents a multiscale analysis of the fMRI data which improves the robustness with which the DMN is detected in subjects that move the head during the acquisition process. Initially, the method obtains multiple scales by filtering the original volumes out with a sequence of gaussian filters. Each fMRI scale is preprocessed by registering, normalizing, smoothing and coregistering, as described elsewhere, using the SPM software. These preprocessed data are then decomposed into the spatial-temporal components with the FastICA algorithm. The DMN is then selected with the Goodness of Fit approach (GoF), for each of the different scales. Finally, the components of the DMN at different scales are summed up, ruling out the original volume.
The approach was validated by perturbing the acquired sequence of five healthy subjects with the six rigid-body movement series obtained from 15 subjects (5 Control, 3 Minimally Conscious State, 2 Locked in Syndrome and 5 Vegetative State), with head motion from 1mm up to 15 mm.
In summary, the proposed method was applied to 18 disturbed data that lost the DMN, from which 11 fMRI data recovered the DMN. The mean of the Goodness of fit of the DMN component increased from 0.1 to 0.4. This multiscale analysis improves the robustness of the DMN detection in case of large head movements.
Keywords:
Computational Neurosciences,
Default Mode Network,
fMRI,
High head motions,
multiscale analysis
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Abstract
Topic:
neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords)
Citation:
Baquero
K,
Gómez
F,
Soddu
A,
Vanhaudenhuyse
A,
Demertzi
A,
Tshibanda
J,
Gosseries
O,
Noirhomme
Q,
Laureys
S and
Romero
E
(2011). A Multi-Scale Analysis to set the Default Mode Network in noisy fMRI data.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00067
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Received:
23 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Miss. Katherine Baquero, National University of Colombia, Biomedical Engineering, Bogotá, Bogota DC, 11001000, Colombia, kabaquero@uliege.be
Prof. Eduardo Romero, National University of Colombia, Biomedical Engineering, Bogotá, Bogota DC, 11001000, Colombia, edromero@unal.edu.co