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Toolbox for enhanced fMRI activation mapping using anatomically adapted graph wavelets

Behjat, Hamid LU (2016) 22nd Annual Meeting of the Organization for Human Brain Mapping (OHBM 2016)
Abstract
In fMRI studies with evoked activity, brain activity is detected by voxel-wise GLM tting, followed by statistical hypothesis testing. Statistical parametric mapping (SPM), one of the most popular classical methods, relies upon Gaussian smoothing to deal with the multiple-comparison correction. As an alternative, we have recently introduced a graph-based framework for fMRI brain activation mapping (Behjat, et al., 2015). The graph is designed such that it encodes the topological structure of the gray matter (GM). The approach exploits the spectral graph wavelet transform for the purpose of defining an advanced multi-scale spatial transformation for fMRI data. The use of spatial wavelet transforms has the benefit of providing a compact... (More)
In fMRI studies with evoked activity, brain activity is detected by voxel-wise GLM tting, followed by statistical hypothesis testing. Statistical parametric mapping (SPM), one of the most popular classical methods, relies upon Gaussian smoothing to deal with the multiple-comparison correction. As an alternative, we have recently introduced a graph-based framework for fMRI brain activation mapping (Behjat, et al., 2015). The graph is designed such that it encodes the topological structure of the gray matter (GM). The approach exploits the spectral graph wavelet transform for the purpose of defining an advanced multi-scale spatial transformation for fMRI data. The use of spatial wavelet transforms has the benefit of providing a compact representation of activation patterns. The framework extends wavelet-based SPM (WSPM), which is a framework that combines wavelet processing of non-smoothed data with voxel-wise statistical testing while guaranteeing strong FP control. Here, we present an implementation of the proposed framework as a user-friendly, SPM-compatible toolbox that deals with multi-subject studies. (Less)
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author
organization
publishing date
type
Contribution to conference
publication status
published
keywords
fMRI, signal processing
conference name
22nd Annual Meeting of the Organization for Human Brain Mapping (OHBM 2016)
language
English
LU publication?
yes
id
0d5121b0-0608-449c-8018-07474334530a
date added to LUP
2016-07-25 13:53:27
date last changed
2016-08-08 13:45:46
@misc{0d5121b0-0608-449c-8018-07474334530a,
  abstract     = {In fMRI studies with evoked activity, brain activity is detected by voxel-wise GLM  tting, followed by statistical hypothesis testing. Statistical parametric mapping (SPM), one of the most popular classical methods, relies upon Gaussian smoothing to deal with the multiple-comparison correction. As an alternative, we have recently introduced a graph-based framework for fMRI brain activation mapping (Behjat, et al., 2015). The graph is designed such that it encodes the topological structure of the gray matter (GM). The approach exploits the spectral graph wavelet transform for the purpose of defining an advanced multi-scale spatial transformation for fMRI data. The use of spatial wavelet transforms has the benefit of providing a compact representation of activation patterns. The framework extends wavelet-based SPM (WSPM), which is a framework that combines wavelet processing of non-smoothed data with voxel-wise statistical testing while guaranteeing strong FP control. Here, we present an implementation of the proposed framework as a user-friendly, SPM-compatible toolbox that deals with multi-subject studies.},
  author       = {Behjat, Hamid},
  keyword      = { fMRI, signal processing},
  language     = {eng},
  title        = {Toolbox for enhanced fMRI activation mapping using anatomically adapted graph wavelets},
  year         = {2016},
}