Canonical cerebellar graph wavelets and their application to fMRI activation mapping
(2014) 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4643651
- author
- Behjat, Hamid LU ; Leonardi, Nora ; Sörnmo, Leif LU and Van De Ville, Dimitri
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- [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
- conference location
- Chicago, IL, United States
- conference dates
- 2014-08-26 - 2014-08-30
- external identifiers
-
- wos:000350044701010
- scopus:84929484906
- pmid:25570139
- 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
- 2016-04-01 14:44:16
- date last changed
- 2022-01-28 02:14:47
@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}},
doi = {{10.1109/EMBC.2014.6943771}},
year = {{2014}},
}