Canonical cerebellar graph wavelets and their application to FMRI activation mapping

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 ind...

Full description

Saved in:
Bibliographic Details
Published in2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2014; pp. 1039 - 1042
Main Authors Behjat, Hamid, Leonardi, Nora, Sornmo, Leif, Van De Ville, Dimitri
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2014
Subjects
Brain Mapping
cerebellum
Cerebellum - anatomy & histology
Cerebellum - diagnostic imaging
Engineering and Technology
functional MRI
graph wavelet transform
Gray Matter
Humans
Magnetic Resonance Imaging
Medical Engineering
Medicinteknik
Radiography
Sensitivity
Smoothing methods
spectral graph theory
Statistical testing
Teknik
Testing
Wavelet Analysis
Wavelet domain
wavelet thresholding
Wavelet transforms
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:1094-687X
1557-170X
DOI:10.1109/EMBC.2014.6943771