| 1. |
- Behjat, Hamid, et al.
(author)
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Anatomically-adapted Graph Wavelets for Improved Group-level fMRI Activation Mapping
- 2015
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In: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 123:Online 07 June 2015, s. 185-199
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Journal article (peer-reviewed)abstract
- 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 grey 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.
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| 2. |
- Behjat, Hamid, et al.
(author)
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Canonical cerebellar graph wavelets and their application to fMRI activation mapping
- 2014
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In: [Host publication title missing]. - 1557-170X. ; , s. 1039-1042
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Conference paper (peer-reviewed)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.
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| 3. |
- Behjat, Hamid, et al.
(author)
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fMRI activation mapping using wavelet-based SPM (WSPM) integrated with gray-matter graphs
- 2014
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Conference paper (peer-reviewed)abstract
- In many fMRI task-evoked studies, localized brain activity can be detected by GLM fitting and statistical hypothesis testing. Statistical parametric mapping (SPM) is the classical method that requires Gaussian pre-smoothing of the data. Instead, the wavelet transform provides a compact representation of activation patterns. Wavelet based SPM (WSPM) is an extension of SPM that combines wavelet processing with voxel-wise statistical testing. However, classical wavelets used in WSPM are designed for regular Euclidean grids and thus not adapted to the convoluted nature of the cerebral cortex. We recently showed how WSPM using graph wavelets tailored to the gray-matter structure of the cortex can improve detection of brain activity in single-subject studies. Here we extend this approach to group-level analysis by modifying the design of the brain graph.
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| 4. |
- Behjat, Hamid, et al.
(author)
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Statistical parametric mapping of functional MRI data using wavelets adapted to the cerebral cortex
- 2013
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In: [Host publication title missing]. - 1945-7928 .- 1945-8452. ; , s. 1070-1073
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Conference paper (peer-reviewed)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.
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