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Anatomically-adapted Graph Wavelets for Improved Group-level fMRI Activation Mapping

Behjat, Hamid LU ; Leonardi, Nora; Sörnmo, Leif LU and Van De Ville, Dimitri (2015) In NeuroImage 123(Online 07 June 2015). p.185-199
Abstract
A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar grey matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries... (More)
A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar grey matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
wavelet thresholding, graph wavelets, spectral graph theory, functional MRI, statistical parametric mapping (SPM)
in
NeuroImage
volume
123
issue
Online 07 June 2015
pages
185 - 199
publisher
Elsevier
external identifiers
  • pmid:26057594
  • wos:000363763900018
  • scopus:84945451977
ISSN
1095-9572
DOI
10.1016/j.neuroimage.2015.06.010
language
English
LU publication?
yes
id
931eab7f-243d-4917-915b-d597006cd746 (old id 7357130)
alternative location
http://miplab.epfl.ch/pub/behjat1501.pdf
date added to LUP
2015-06-16 12:11:59
date last changed
2017-10-22 03:21:15
@article{931eab7f-243d-4917-915b-d597006cd746,
  abstract     = {A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar grey matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity.},
  author       = {Behjat, Hamid and Leonardi, Nora and Sörnmo, Leif and Van De Ville, Dimitri},
  issn         = {1095-9572},
  keyword      = {wavelet thresholding,graph wavelets,spectral graph theory,functional MRI,statistical parametric mapping (SPM)},
  language     = {eng},
  number       = {Online 07 June 2015},
  pages        = {185--199},
  publisher    = {Elsevier},
  series       = {NeuroImage},
  title        = {Anatomically-adapted Graph Wavelets for Improved Group-level fMRI Activation Mapping},
  url          = {http://dx.doi.org/10.1016/j.neuroimage.2015.06.010},
  volume       = {123},
  year         = {2015},
}