Detection of fMRI activation using Cortical Surface Mapping

Andrade, Alexandre; Kherif, Ferath; Mangin, Jean‐François; Worsley, Keith J.; Paradis, Anne‐Lise; Simon, Olivier; Dehaene, Stanislas; Bihan, Denis Le; Poline, Jean‐Baptiste

doi:10.1002/1097-0193(200102)12:2<79::aid-hbm1005>3.0.co;2-i

Cited by 133 publications

(142 citation statements)

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“…Previous studies following CSM approaches (Andrade et al, 2001;Chung et al, 2005;Hagler et al, 2006;Qiu et al, 2006), which anatomically constrain the activation mapping, suffer from a few shortcomings. First, the interpolation effects related to the projection of 3-D volumes onto the cortical surface is a major challenge (Grova et al, 2006;Operto et al, 2008).…”

Section: Comparison With Other Anatomically Constrained Methodsmentioning

confidence: 99%

“…Second, smoothing leads to spatial inaccuracy and artificial shift of activations, be it using conventional volume-based approaches (e.g., SPM) or CSM (Jo et al, 2007(Jo et al, , 2008Mikl et al, 2008). Although the cortical 2-D diffusion smoothing of CSM approaches, rather than 3-D isotropic Gaussian smoothing of conventional volume-based approaches, prevents overlap of activation centers that are geodesically distant but close in a Euclidean sense (e.g., two points, at the opposite sides of a sulcus) (Andrade et al, 2001), the resulting smoothed signal can still be influenced by sources that are geodesically adjacent. tSPMsgwt overcomes these two limitations by not smoothing the data in the first place and keeping the analysis within the native voxel-space.…”

Section: Comparison With Other Anatomically Constrained Methodsmentioning

confidence: 99%

“…One such approach is the anatomically-informed basis function (AIBF) method proposed in Kiebel et al (2000) and Kiebel and Friston (2002), where a forward model is determined for solving the inverse problem of explaining the distribution of the functional data using circular Gaussian basis functions defined on the cortical surface. In other approaches, here collectively referred to as cortical surface mapping (CSM), an interpolation scheme is used to map the functional data to the extracted cortical surface, followed by iteratively smoothing the data on the surface using different procedures such as diffusion smoothing (Andrade et al, 2001), heat kernel smoothing (Chung et al, 2005;Hagler et al, 2006) and spline smoothing (Qiu et al, 2006). Nevertheless, the problem of loss in spatial accuracy remains in CSM due to the irreversible smoothing.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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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.

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Section: Comparison With Other Anatomically Constrained Methodsmentioning

confidence: 99%

Section: Comparison With Other Anatomically Constrained Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…First, cortical surface models allow for better visualization of activations, providing a more global view than single slices and a better view of the spatial extent of activation foci and their locations relative to each other and to sulcal/ gyral landmarks (Dale and Sereno, 1993). Second, statistical methods for the analysis of single subject data can benefit from the exclusion of non-gray matter signals, and smoothing signals along the cortical surface, rather than in 3D results in superior resolution and sensitivity (Kiebel et al, 2000;Andrade et al, 2001;Formisano et al, 2004). Finally, group analysis with cortical surface models employs inter-subject alignment based on the patterns of sulci and gyri, as opposed to Talairach registration which often ignores sulcal/gyral landmarks and tends to blur activity across neighboring banks of a sulcus (Fischl et al, 1999a;Fischl et al, 1999b).…”

Section: Introductionmentioning

confidence: 99%

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

2006

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Cortical surface-based analysis of fMRI data has proven to be a useful method with several advantages over 3-dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surface-based analyses. Operating within the framework of the FreeSurfer software package, we have implemented a surface-based version of the cluster-size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the cortical surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster-size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise datasets. Because smoothing on a cortical surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing surface-based smoothing simulations and found a large disparity between the expected and actual kernel widths.

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“…Another extension that could be introduced at little expense concerns the analysis on the cortical surface, as proposed in (Andrade et al, 2001). This will probably improve the sensitivity of detection by constraining the analysis to the cortical surface.…”

Section: Discussionmentioning

confidence: 99%

A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

Idier²,

et al. 2008

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Within-subject analysis in fMRI essentially addresses two problems, i.e., the detection of activated brain regions in response to an experimental task and the estimation of the underlying dynamics, also known as the characterisation of Hemodynamic response function (HRF). So far, both issues have been treated sequentially while it is known that the HRF model has a dramatic impact on the localisation of activations and that the HRF shape may vary from one region to another. In this paper, we conciliate both issues in a region-based joint detection-estimation framework that we develop in the Bayesian formalism. Instead of considering function basis to account for spatial variability, spatially adaptive General Linear Models are built upon region-based non-parametric estimation of brain dynamics. Regions are first identified as functionally homogeneous parcels in the mask of the grey matter using a specific procedure [Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., Poline, J.-B., August 2006. Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets. Hum. Brain Mapp. 27 (8), 678-693.]. Then, in each parcel, prior information is embedded to constrain this estimation. Detection is achieved by modelling activating, deactivating and nonactivating voxels through mixture models within each parcel. From the posterior distribution, we infer upon the model parameters using Markov Chain Monte Carlo (MCMC) techniques. Bayesian model comparison allows us to emphasize on artificial datasets first that inhomogeneous gamma-Gaussian mixture models outperform Gaussian mixtures in terms of sensitivity/specificity trade-off and second that it is worthwhile modelling serial correlation through an AR(1) noise process at low signal-to-noise (SNR) ratio. Our approach is then validated on an fMRI experiment that studies habituation to auditory sentence repetition. This phenomenon is clearly recovered as well as the hierarchical temporal organisation of the superior temporal sulcus, which is directly derived from the parcel-based HRF estimates. © 2008 Elsevier Inc. All rights reserved.Keywords: Bayesian modelling; fMRI; Gamma-Gaussian mixture model; Detection-estimation; Markov Chain Monte Carlo methods; Bayes factor; Model comparison IntroductionSince the first report of the BOLD effect in human (Ogawa et al., 1990), functional magnetic resonance imaging (fMRI) has represented a powerful tool to non-invasively study the relation between cognitive stimulus and the hemodynamic (BOLD) response. Within-subject analysis in fMRI is usually addressed using a hypothesis-driven approach that actually postulates a model for the HRF and enable voxelwise inference in the General Linear Model (GLM) framework. In this formulation, the modelling of the BOLD response i.e., the definition of the design matrix is crucial. In its simplest form, this matrix relies on a spatially invariant temporal model of the BOLD signal across the brain meaning that the expected response to each stimulus is modelled by ...

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Detection of fMRI activation using Cortical Surface Mapping

Cited by 133 publications

References 26 publications

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

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

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

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