Statistical parametric mapping of functional MRI data using wavelets adapted to the cerebral cortex
(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:
https://lup.lub.lu.se/record/4272561
- author
- Behjat, Hamid LU ; Leonardi, Nora and Van De Ville, Dimitri
- organization
- publishing date
- 2013
- 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
- 2016-04-04 08:01:17
- date last changed
- 2024-03-29 18:08:57
@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}},
booktitle = {{[Host publication title missing]}},
issn = {{1945-8452}},
keywords = {{Statistical testing; functional MRI; spectral graph theory; graph; wavelet transform; wavelet thresholding}},
language = {{eng}},
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}},
doi = {{10.1109/ISBI.2013.6556663}},
year = {{2013}},
}