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Canonical cerebellar graph wavelets and their application to fMRI activation mapping

Behjat, Hamid LU ; Leonardi, Nora; Sörnmo, Leif LU and Van De Ville, Dimitri (2014) 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014 In [Host publication title missing] p.1039-1042
Abstract
Wavelet-based statistical parametric mapping (WSPM) is an extension of the classical approach in fMRI activation mapping that combines wavelet processing with voxel-wise statistical testing. We recently showed how WSPM, using graph wavelets tailored to the full gray-matter (GM) structure of each individual’s brain, can improve brain activity detection compared to using the classical wavelets that are only suited for the Euclidian grid. However, in order to perform analysis on a subject-invariant graph, canonical graph wavelets should be designed in normalized brain space. We here introduce an approach to define a fixed template graph of the cerebellum, an essential component of the brain, using the SUIT cerebellar template. We construct a... (More)
Wavelet-based statistical parametric mapping (WSPM) is an extension of the classical approach in fMRI activation mapping that combines wavelet processing with voxel-wise statistical testing. We recently showed how WSPM, using graph wavelets tailored to the full gray-matter (GM) structure of each individual’s brain, can improve brain activity detection compared to using the classical wavelets that are only suited for the Euclidian grid. However, in order to perform analysis on a subject-invariant graph, canonical graph wavelets should be designed in normalized brain space. We here introduce an approach to define a fixed template graph of the cerebellum, an essential component of the brain, using the SUIT cerebellar template. We construct a corresponding set of canonical cerebellar graph wavelets, and adopt them in the analysis of both synthetic and real data. Compared to classical SPM, WSPM using cerebellar graph wavelets shows superior type-I error control, an empirical higher sensitivity on real data, as well as the potential to capture subtle patterns of cerebellar activity. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
[Host publication title missing]
pages
1039 - 1042
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
external identifiers
  • wos:000350044701010
  • scopus:84929484906
ISSN
1557-170X
DOI
10.1109/EMBC.2014.6943771
language
English
LU publication?
yes
id
8b3cf172-2ba5-44b5-80f2-779e8facd983 (old id 4643651)
alternative location
http://miplab.epfl.ch/pub/behjat1401.pdf
date added to LUP
2014-10-16 11:56:51
date last changed
2017-08-13 04:09:05
@inproceedings{8b3cf172-2ba5-44b5-80f2-779e8facd983,
  abstract     = {Wavelet-based statistical parametric mapping (WSPM) is an extension of the classical approach in fMRI activation mapping that combines wavelet processing with voxel-wise statistical testing. We recently showed how WSPM, using graph wavelets tailored to the full gray-matter (GM) structure of each individual’s brain, can improve brain activity detection compared to using the classical wavelets that are only suited for the Euclidian grid. However, in order to perform analysis on a subject-invariant graph, canonical graph wavelets should be designed in normalized brain space. We here introduce an approach to define a fixed template graph of the cerebellum, an essential component of the brain, using the SUIT cerebellar template. We construct a corresponding set of canonical cerebellar graph wavelets, and adopt them in the analysis of both synthetic and real data. Compared to classical SPM, WSPM using cerebellar graph wavelets shows superior type-I error control, an empirical higher sensitivity on real data, as well as the potential to capture subtle patterns of cerebellar activity.},
  author       = {Behjat, Hamid and Leonardi, Nora and Sörnmo, Leif and Van De Ville, Dimitri},
  booktitle    = {[Host publication title missing]},
  issn         = {1557-170X},
  language     = {eng},
  pages        = {1039--1042},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Canonical cerebellar graph wavelets and their application to fMRI activation mapping},
  url          = {http://dx.doi.org/10.1109/EMBC.2014.6943771},
  year         = {2014},
}