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Statistical parametric mapping of functional MRI data using wavelets adapted to the cerebral cortex

Behjat, Hamid LU ; Leonardi, Nora and Van De Ville, Dimitri (2013) 10th IEEE International Symposium on Biomedical Imaging - From Nano to Macro (ISBI) p.1070-1073
Abstract
Wavelet approaches have been successfully applied to the detection of brain activity in fMRI data. Spatial activation patterns have a compact representation in the wavelet domain. However, classical wavelets designed for regular Euclidean spaces are not optimal for the topologically complicated gray-matter (GM) domain where activation is expected. We hypothesized that wavelet bases that are adapted to the structure of the GM, would be more powerful in detecting brain activity. We therefore combine (1) a GM-based graph wavelet transform as an advanced spatial transformation for fMRI data with (2) the wavelet-based statistical parametric mapping framework (WSPM). We introduce suitable design choices for the graph wavelet transform and... (More)
Wavelet approaches have been successfully applied to the detection of brain activity in fMRI data. Spatial activation patterns have a compact representation in the wavelet domain. However, classical wavelets designed for regular Euclidean spaces are not optimal for the topologically complicated gray-matter (GM) domain where activation is expected. We hypothesized that wavelet bases that are adapted to the structure of the GM, would be more powerful in detecting brain activity. We therefore combine (1) a GM-based graph wavelet transform as an advanced spatial transformation for fMRI data with (2) the wavelet-based statistical parametric mapping framework (WSPM). We introduce suitable design choices for the graph wavelet transform and evaluate the performance of the proposed approach both on simulated and real fMRI data. Compared to SPM and conventional WSPM, the graph-based WSPM shows improved detection of finely 3D-structured brain activity. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Statistical testing, functional MRI, spectral graph theory, graph, wavelet transform, wavelet thresholding
host publication
[Host publication title missing]
pages
1070 - 1073
conference name
10th IEEE International Symposium on Biomedical Imaging - From Nano to Macro (ISBI)
conference location
San Francisco, CA, United States
conference dates
2013-04-07 - 2013-04-11
external identifiers
  • wos:000326900100268
  • scopus:84881642140
ISSN
1945-8452
1945-7928
DOI
10.1109/ISBI.2013.6556663
language
English
LU publication?
yes
id
c1104e16-1acd-4268-a163-7edfbed7d03d (old id 4272561)
alternative location
http://miplab.epfl.ch/pub/behjat1301.pdf
date added to LUP
2014-02-12 14:05:42
date last changed
2019-05-14 03:20:17
@inproceedings{c1104e16-1acd-4268-a163-7edfbed7d03d,
  abstract     = {Wavelet approaches have been successfully applied to the detection of brain activity in fMRI data. Spatial activation patterns have a compact representation in the wavelet domain. However, classical wavelets designed for regular Euclidean spaces are not optimal for the topologically complicated gray-matter (GM) domain where activation is expected. We hypothesized that wavelet bases that are adapted to the structure of the GM, would be more powerful in detecting brain activity. We therefore combine (1) a GM-based graph wavelet transform as an advanced spatial transformation for fMRI data with (2) the wavelet-based statistical parametric mapping framework (WSPM). We introduce suitable design choices for the graph wavelet transform and evaluate the performance of the proposed approach both on simulated and real fMRI data. Compared to SPM and conventional WSPM, the graph-based WSPM shows improved detection of finely 3D-structured brain activity.},
  author       = {Behjat, Hamid and Leonardi, Nora and Van De Ville, Dimitri},
  issn         = {1945-8452},
  keyword      = {Statistical testing,functional MRI,spectral graph theory,graph,wavelet transform,wavelet thresholding},
  language     = {eng},
  location     = {San Francisco, CA, United States},
  pages        = {1070--1073},
  title        = {Statistical parametric mapping of functional MRI data using wavelets adapted to the cerebral cortex},
  url          = {http://dx.doi.org/10.1109/ISBI.2013.6556663},
  year         = {2013},
}