Variational Solution to the Joint Detection Estimation of Brain Activity in fMRI

Chaâri, Lotfi; Forbes, Florence; Thomas, Vincent; Dojat, Michel; Ciuciu, Philippe

doi:10.1007/978-3-642-23629-7_32

Cited by 10 publications

(26 citation statements)

References 14 publications

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“…With standard additional assumptions [1][2][3], and omitting the dependence on the parameters to be specified later, the distribution of both the observed and hidden variables can be decomposed as…”

Section: Hierarchical Model Of the Complete Data Distributionmentioning

confidence: 99%

“…Akin to [1][2][3], the NRLs are assumed to be statistically independent across conditions: p(A; θa) = …”

Section: Model Priorsmentioning

confidence: 99%

“…HRF. In contrast to [1][2][3] where a unique HRF is used for a whole parcel and where a smoothness constraint is imposed for the HRF by controlling its second order derivative, we define here…”

Section: Model Priorsmentioning

confidence: 99%

“…In [1,2], posterior inference is carried out in a Bayesian setting using Monte Carlo Markov Chain (MCMC) methods. In order to overcome the high computational cost of this MCMC-based approach, an alternative with similar performance has been proposed in [3] based on Variational Expectation Maximization (VEM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…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. …”

mentioning

confidence: 99%

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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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Section: Hierarchical Model Of the Complete Data Distributionmentioning

confidence: 99%

“…Akin to [1][2][3], the NRLs are assumed to be statistically independent across conditions: p(A; θa) = …”

Section: Model Priorsmentioning

confidence: 99%

Section: Model Priorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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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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Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework

Chaâri

Forbes

Thomas

et al. 2012

Lecture Notes in Computer Science

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Abstract. Identifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-DetectionEstimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool.

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Group-level impacts of within- and between-subject hemodynamic variability in fMRI

2013

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Inter-subject fMRI analyses have specific issues regarding the reliability of the results concerning both the detection of brain activation patterns and the estimation of the underlying dynamics. Among these issues lies the variability of the hemodynamic response function (HRF), that is usually accounted for using functional basis sets in the general linear model context. Here, we use the joint detection-estimation approach (JDE) (Makni et al., 2008; Vincent et al., 2010) which combines regional nonparametric HRF inference with spatially adaptive regularization of activation clusters to avoid global smoothing of fMRI images. We show that the JDE-based inference brings a significant improvement in statistical sensitivity for detecting evoked activity in parietal regions. In contrast, the canonical HRF associated with spatially adaptive regularization is more sensitive in other regions, such as motor cortex. This different regional behavior is shown to reflect a larger discrepancy of HRF with the canonical model. By varying parallel imaging acceleration factor, SNR-specific region-based hemodynamic parameters (activation delay and duration) were extracted from the JDE inference. Complementary analyses highlighted their significant departure from the canonical parameters and the strongest between-subject variability that occurs in the parietal region, irrespective of the SNR value. Finally, statistical evidence that the fluctuation of the HRF shape is responsible for the significant change in activation detection performance is demonstrated using paired t-tests between hemodynamic parameters inferred by GLM and JDE.

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Variational Solution to the Joint Detection Estimation of Brain Activity in fMRI

Cited by 10 publications

References 14 publications

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

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

Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework

Group-level impacts of within- and between-subject hemodynamic variability in fMRI

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