Toolbox for enhanced fMRI activation mapping using anatomically adapted graph wavelets

Research output: Contribution to conferencePoster


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.


Research areas and keywords


  • fMRI, signal processing
Original languageEnglish
Publication statusPublished - 2016
Publication categoryResearch
Event22nd Annual Meeting of the Organization for Human Brain Mapping (OHBM 2016) - Geneva, Switzerland
Duration: 2016 Jun 262016 Jun 30


Conference22nd Annual Meeting of the Organization for Human Brain Mapping (OHBM 2016)

Bibliographic note

This toolbox contains code implementing the framework presented in the following paper: H. Behjat, N. Leonardi, L. Sörnmo, D. Van De Ville, “Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping,” NeuroImage, vol. 123, pp. 185-199, 2015.

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