Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping

• Published in: NeuroImage (Orlando, Fla.) Vol. 123; no. Online 07 June 2015; pp. 185 - 199
• Main Authors: Behjat, Hamid, Leonardi, Nora, Sörnmo, Leif, Van De Ville, Dimitri
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2015; Elsevier Limited
• Subjects: Algorithms; Brain Mapping - methods; Cerebellum - anatomy & histology; Cerebellum - physiology; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; Computer Simulation; Engineering and Technology; Euclidean space; Functional MRI; Graph wavelets; Gray Matter - anatomy & histology; Gray Matter - physiology; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging - methods; Medical Engineering; Medical Image Processing; Medical Imaging; Medicinsk bildbehandling; Medicinsk bildvetenskap; Medicinteknik; Signal processing; Software; Spectral graph theory; Statistical parametric mapping (SPM); Teknik; Wavelet Analysis; Wavelet thresholding; Wavelet transforms; Graph wavelets; Functional MRI; Spectral graph theory; Wavelet thresholding; Statistical parametric mapping (SPM)
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.06.010

A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar gray matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity. [Display omitted] •A graph based framework for group-level fMRI activation mapping is presented.•Graphs encoding the geometry of the gray matter (GM) are constructed.•Inter-subject GM variability is accounted by using group-level template GMs.•GM-adapted wavelets are constructed to perform an advanced transformation on data.•Results on real data suggest a higher sensitivity in detecting subtle brain activity.