Joint Detection-Estimation of Brain Activity in fMRI using an Autoregressive Noise Model

Makni, Salima; Ciuciu, Philippe; Idier, Jérôme; Poline, Jean‐Baptiste

doi:10.1109/isbi.2006.1625101

Cited by 10 publications

(15 citation statements)

References 7 publications

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“…In short, it has been shown that the gamma-Gaussian mixture model (GaGMM) introduced on the NRLs is more efficient than a two-class Gaussian mixture model (GMM) in terms of specificity: it provides a better control of the false positive rate. Similar conclusions have been drawn in (Makni et al, 2006b) when considering an AR(1) noise model instead of a white Gaussian one in combination with a GMM prior. As the two changes induce higher computation time, it is worth assessing which modelling effort is preferable i.e.…”

Section: Results On Synthetic Datasupporting

confidence: 84%

“…Our method extends previous works (Makni et al, 2005(Makni et al, , 2006b,a) to deal with anatomically informed whole brain analysis. As already done in (Smith et al, 2003;Nieto-Castanon et al, 2003;Flandin et al, 2002), analysis was constrained to the mask of the grey matter obtained from a segmentation of the T1-weighted MRI.…”

Section: Discussionmentioning

confidence: 57%

“…It has been shown on simulated datasets that a gamma-Gaussian mixture as prior pdf on the NRLs outperforms a Gaussian mixture in terms of sensitivity/specificity trade-off. It has also been reported that the noise model has an influence over this compromise, particularly at lower SNR: a first-order AR model provides lower false positive and negative rates in comparison with a white noise model.Our method extends previous works (Makni et al, 2005(Makni et al, , 2006b,a) to deal with anatomically informed whole brain analysis. As already done in (Smith et al, 2003;Nieto-Castanon et al, 2003;Flandin et al, 2002), analysis was constrained to the mask of the grey matter obtained from a segmentation of the T1-weighted MRI.…”

mentioning

confidence: 57%

“…Importantly, when this temporal correlation is correctly estimated, the number of false positives decreases, yielding more conservative detection results. Similarly, in a joint detection-estimation framework, Makni et al (2006b) have shown that the introduction of a spatially-varying first-order AR noise in model (1) provides a lower false positive rate. Hence, b j is defined by b j;tn ¼ q j b j;tnÀ1 þ e j;tn ; 8j; t; with e j e N ð0 N ; r 2 ej I N Þ;, where 0 N is a null vector of length N, and I N is the identity matrix of size N.…”

Section: Parcel-based Modelling Of the Bold Signalmentioning

confidence: 97%

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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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Section: Results On Synthetic Datasupporting

confidence: 84%

Section: Discussionmentioning

confidence: 57%

mentioning

confidence: 57%

Section: Parcel-based Modelling Of the Bold Signalmentioning

confidence: 97%

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

Idier²,

et al. 2008

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“…In [8], an autoregressive (AR) noise model has been adopted to account for serial correlations in fMRI time series. It has also been shown in [8] that a spatially-varying AR noise model helped to control false positive rate.…”

Section: Likelihoodmentioning

confidence: 99%

Robust voxel-wise joint detection estimation of brain activity in fMRI

Chaâri

Forbes

Thomas

et al. 2012

2012 19th IEEE International Conference on Image Processing

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We address the issue of jointly detecting brain activity and estimating brain hemodynamics from functional MRI data. To this end, we adopt the so-called Joint-Detection-Estimation (JDE) framework introduced in [1] and augmented in [2]. An inherent difficulty is to find the right spatial scale at which brain hemodynamics estimation makes sense. The voxel level is clearly not appropriate as estimating a full hemodynamic response function (HRF) from a single voxel time course may suffer from a poor signal-to-noise-ratio and lead to potentially misleading results (non-physiological HRF shapes). More robust estimation can be obtained by considering groups of voxels (i.e. parcels) with some functional homogeneity properties. Current JDE approaches are therefore based on an initial parcellation but with no guarantee of its optimality or goodness. In this work, we propose a joint parcellation-detection-estimation (JPDE) procedure that incorporates an additional parcel estimation step solving this way both the parcellation choice and robust HRF estimation issues. As in [3], inference is carried out in a Bayesian setting using variational approximation techniques for computational efficiency.

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A Stochastic Linear Model for fMRI Activation Analyses

Johnston

Egan

2011

Lecture Notes in Computer Science

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Abstract. Purpose:The debate regarding how best to model variability of the hemodynamic response function in fMRI data has focussed on the linear vs. nonlinear nature of the optimal signal model, with few studies exploring the deterministic vs. stochastic nature of the dynamics. We propose a stochastic linear model (SLM) of the hemodynamic signal and noise dynamics to more robustly infer fMRI activation estimates. Methods: The SLM models the hemodynamic signal by an exogenous input autoregressive model driven by Gaussian state noise. Activation weights are inferred by a joint state-parameter iterative coordinate descent algorithm based on the Kalman smoother. Results: The SLM produced more accurate parameter estimates than the GLM for event-design simulated data. In application to block-design experimental visuo-motor task fMRI data, the SLM resulted in more punctate and well-defined motor cortex activation maps than the GLM, and was able to track variations in the hemodynamics, as expected from a stochastic model. Conclusions: We demonstrate in application to both simulated and experimental fMRI data that in comparison to the GLM, the SLM produces more flexible, consistent and enhanced fMRI activation estimates.

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Joint Detection-Estimation of Brain Activity in fMRI using an Autoregressive Noise Model

Cited by 10 publications

References 7 publications

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

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

Robust voxel-wise joint detection estimation of brain activity in fMRI

A Stochastic Linear Model for fMRI Activation Analyses

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