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

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

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

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-Detection-Estimation (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 demonstrates the JPDE performance over standard detection estimation schemes and suggests it as a new brain exploration tool.
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A Bayesian Non-Parametric Hidden Markov Random Model for Hemodynamic Brain Parcellation

A Bayesian Non-Parametric Hidden Markov Random Model for Hemodynamic Brain Parcellation

c CEA/NeuroSpin and INRIA Saclay, Parietal, France Abstract Deriving a meaningful functional brain parcellation is a very challenging is- sue in task-related fMRI analysis. The joint parcellation detection estimation model addresses this issue by inferring the parcels from fMRI data. However, it requires a priori fixing the number of parcels through an initial mask for parcellation. Hence, this difficult task generally depends on the subject. The proposed automatic parcellation approach in this paper overcomes this limita- tion at the subject-level relying on a Dirichlet process mixture model combined with a hidden Markov random field to estimate the parcels and their number online. The proposed method adopts a variational expectation maximization strategy for inference. Compared to the model selection procedure in the joint parcellation detection estimation framework, our method appears more efficient in terms of computational time and does not require finely tuned initialization. Synthetic data experiments show that our method is able to estimate the right model order and an accurate parcellation. Real data results demonstrate the ability of our method to aggregate parcels with similar hemodynamic behaviour in the right motor and bilateral occipital cortices while its discriminating power is increased compared to its ancestors. Moreover, the obtained HRF estimates are close to the canonical HRF in both cortices.
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Multi-subject joint parcellation detection estimation in functional MRI

Multi-subject joint parcellation detection estimation in functional MRI

subject-level [4] where the ”P” means that an additional layer complements the model to infer the brain parcellation. However, so far, the JPDE model has not been used for multi-subject fMRI analysis. Besides, the JDE model has been used in a multi-subject context [6] but the parcellation remained fixed a priori and identical across subjects. Another approach was proposed in [2] based on a semi-parametric framework with the general linear model. This approach assumes that for a fixed voxel under a given stimulus, the HRFs share the same unknown functional form across subjects but with different characteristics such as the time to peak, height and width. This common functional form was estimated using a nonparametric spline-smoothing method. In this paper, we introduce a joint intra and inter-subject fMRI data analysis model in the JPDE framework.The latter allows us to estimate a group-level parcellation as well as the group-level underlying HRF in contrast with the JPDE model. This group-level JPDE also recovers evoked activity in each individual. Moreover, the analysis is carried out for all the parcels of a region of interest (ROI) contrary to [6] where the analysis is done for a specific parcel at a time. The rest of the paper is organized as follows; Section II recalls the JPDE model introduced in [4]. Section III de- scribes our Multi-Subject Joint Parcellation Detection Esti- mation (MS-JPDE) extension. Experiments on multi-subject synthetic and real data are shown in Section IV. Conclusions are drawn in Section V.
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Hemodynamic estimation based on Consensus Clustering

Hemodynamic estimation based on Consensus Clustering

VI. C ONCLUSION In this paper, a consensus clustering-based procedure was introduced to robustify the hemodynamic estimation performed in the context of the JDE formalism. Instead of relying on a prior parcellation of the data, different clusterings are performed after perturbating the data and extracting the corresponding hemodynamic features. Results from artificial and real data showed that the new procedure is better adapted to recover the hemodynamics feature of the BOLD signal. To improve the hemodynamics estimation, we plan to further extend our procedure using Weighted Ensemble Clustering techniques [8]. These methods jointly use different representations for temporal data clustering algorithms. The resulting clusters are weighted and com- bined to form a final clustering. Moreover, we also plan to compare our approach with the other alternative to the prior parcellation, the Joint parcellation-detection-estimation framework [5].
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IMPACT OF THE JOINT DETECTION-ESTIMATION APPROACH ON RANDOM EFFECTS GROUP STUDIES IN FMRI

IMPACT OF THE JOINT DETECTION-ESTIMATION APPROACH ON RANDOM EFFECTS GROUP STUDIES IN FMRI

Email: firstname.lastname@cea.fr ABSTRACT Inter-subject analysis of functional Magnetic Resonance Imaging (fMRI) data relies on single intra-subject studies, which are usually conducted using a massively univari- ate approach. In this paper, we investigate the impact of an improved intra-subject analysis on group studies. basi- cally the joint detection-estimation (JDE) framework [1–3] where an explicit characterization of the Hemodynamic Re- sponse Function (HRF) is performed at a regional scale and a stimulus-specific adaptive spatial correlation model en- ables the detection of activation clusters at voxel level. For the group statistics, we conducted several Random effect analyses (RFX) which relied either on the General Linear Model (GLM), or on the JDE analyses, or even on an inter- mediate approach named Spatially Adaptive GLM (SAGLM). Our comparative study perfomed during a fast-event related paradigm involves 18 subjects and illustrates the region- specific differences between the GLM, SAGLM and JDE analyses in terms of statistical sensitivity. On different con- trasts of interest, spatial regularization is shown to have a beneficial impact on the statistical sensitivity. Also, by study- ing the spatial variability of the HRF, we demonstrate that the JDE framework provides more robust detection perfor- mance in cognitive regions due to the higher hemodynamic variability in these areas.
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Bayesian Joint Detection-Estimation of cerebral vasoreactivity from ASL fMRI data

Bayesian Joint Detection-Estimation of cerebral vasoreactivity from ASL fMRI data

8 Vincent T., Warnking J., Villien M., Krainik A., Ciuciu P., and Forbes F. analysis in terms of goodness-of-fit and also sensitivity, especially in its ability to better segregate the gray and white matter within the estimated perfusion levels. Future work will focus on generalizing our study to a group of subjects and provide more quantitative results. Methodological extensions will improve the modeling of the perfusion response function with a positivity constraint. A hierarchical prior modeling is also envisaged to couple the responses in the BOLD and perfusion components. Finally, as the input parcellation has only been validated in the BOLD-only context [12], it will be further tested in the ASL context.
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Adaptive Mean Shift Based Hemodynamic Brain Parcellation in fMRI

Adaptive Mean Shift Based Hemodynamic Brain Parcellation in fMRI

The detection of the evoked activity and the estimation of the dynamics have been mainly addressed as two separate tasks while each of them depends on the other. A precise localization of activations depends on a reliable HRF estimate, while a robust HRF shape is only achievable in brain regions eliciting task-related activity [11,12]. In this context, the joint detection estimation (JDE) model per- forms both tasks simultaneously [13–15]. In the JDE model, a single HRF shape is considered for a specific parcel (group of voxels). Although the JDE model jointly detects the evoked activity within the brain and estimates the HRF, it still requires a prior parcellation of the brain into functionally homogeneous regions. This challenge motivated the development of the joint parcellation de- tection estimation (JPDE) model [16,17] that performs online parcellation along with the detection and estimation tasks by setting voxels that share the same HRF pattern in the same HRF group (parcel). The JPDE model can be inferred using the VEM algorithm. However, this model still requires manual settings of the number of parcels. To overcome this issue, a model selection procedure was proposed in [18] to select the optimum number of parcels. This procedure depends mainly on free energy calculations where the model that maximizes the free energy is the best fit for the data. The limitation of this procedure arises from the fact that it needs to be run for each candidate model which can be time consuming especially if no prior information exists about the number of parcels. The standard JPDE model has been adopted in a Bayesian non-parametric ap- proach [19] by making use of the Dirichlet process mixtures model combined with a hidden Markov random field to automatically infer the number of parcels and their shapes simultaneously with the estimation and detection tasks. In this paper, a new approach is proposed to estimate the number of parcels from the fMRI BOLD signal. More precisely, we propose to embed the adaptive mean shift algorithm (which is a common clustering algorithm) within the variational inference framework associated with the JPDE model to estimate the parcels and their corresponding HRF profiles.
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Multi-session extension of the joint-detection framework in fMRI

Multi-session extension of the joint-detection framework in fMRI

Index Terms— Brain activity, hemodynamics, JDE, fMRI, Bayesian inference, multisession 1. INTRODUCTION In the context of fMRI data analyses, the present paper is a contribu- tion to encoding methods. In such studies, two main concerns arise at the subject-level analysis: (i) a precise localization of evoked brain activity elicited by sensorimotor or cognitive tasks, and (ii) a robust estimation of the underlying hemodynamic response associated with these activations. Since these two steps are inherently linked, the Joint Detection-Estimation (JDE) approach [1, 2], has been proposed to face these issues in a coordinated formalism. This approach per- forms a multivariate inference for both detection and estimation. It makes use of a regional bilinear generative model of the BOLD re- sponse and constrains parameter estimation by physiological priors using temporal and spatial information in a Markovian model. The efficiency and usefulness of this approach has been validated at the group level in [3] considering single-session datasets.
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Multi-subject joint parcellation detection estimation in functional MRI

Multi-subject joint parcellation detection estimation in functional MRI

fMRI experiments are usually conducted over a population of interest for investigating brain activity across different regions stimuli and objects. Multi-subject analysis proceeds in two steps, intra-subject analysis is performed sequen- tially on each individual and then group-level analysis is addressed to report significant results at the population level. This paper considers an existing Joint Parcellation Detection Estimation (JPDE) model which performs joint hemodynamic parcellation, brain dynamics estimation and evoked activity detection. The hierarchy of the JPDE model is extended for multi-subject analysis in order to perform group-level parcellation. Then, the corresponding underlying dynamics is estimated in each parcel while the detection and estimation steps are iterated over each individual. Validation on synthetic and real fMRI data shows its robustness in inferring the group-level parcellation and the corresponding hemodynamic profiles.
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Robust voxel-wise Joint Detection Estimation of Brain Activity in fMRI

Robust voxel-wise Joint Detection Estimation of Brain Activity in fMRI

CEA/DSV/ I 2 BM/Neurospin, CEA, Saclay, Bbt. 145, Point Courrier 156, 91191 Gif-sur-Yvette cedex, France (firstname.lastname@cea.fr) ABSTRACT We address the issue of jointly detecting brain activity and esti- mating 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 parcella- tion but with no guarantee of its optimality or goodness. In this work, we propose a joint parcellation-detection-estimation (JPDE) proce- dure that incorporates an additional parcel estimation step solving this way both the parcellation choice and robust HRF estimation is- sues. As in [3], inference is carried out in a Bayesian setting using variational approximation techniques for computational efficiency.
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Bayesian joint detection-estimation in functional MRI with automatic parcellation and functional constraints

Bayesian joint detection-estimation in functional MRI with automatic parcellation and functional constraints

3.4 Conclusion In this chapter, we introduced the JPDE model which is an extension of the parcel-based JDE model. This model assumes that a single unknown HRF shape is driving hemodynamic responses in a given parcel. Activated voxels within the parcel are then localized by inferring a spatially regularized bi- linear model. One major limitation of the JDE model that it requires the parcellation to be fixed a priori by, e.g., using clustering algorithms. The JPDE model solves this problem by avoiding the pre-defined parcellation. This model allows the grouping of the regions that share a similar HRF pat- tern and relaxing the hard constraint of a single HRF profile over a given parcel to cope with possible parcellation errors. These concerns were ad- dressed by introducing HRF patterns represented by Gaussian distributions and assigned to representative voxels using latent variables. These latent variables are governed by a hidden Markov random field, namely a Potts model that enforces spatial correlation between neighbouring voxels. How- ever, the number of the hemodynamic territories (parcels) has to be specified a priori for the JPDE model. This number has a huge influence of the detec- tion and estimation tasks and its adjustment is generally a non-trivial task. In this context, we proposed a variational model selection procedure based on the free energy calculation. This procedure was added as an extension to the JPDE model where the free energy was calculated for different candidate models after convergence. Each of these models is characterized by a given number of parcels and the model maximizing the free energy is the best fit for the fMRI data. In other words, if we have Ω different models then we have to run the JPDE model with the model selection procedure Ω times and compute the value of the free energy each time. The proposed extension was validated using synthetic and real data experiments. For synthetic data, the proposed procedure managed to estimate the correct number of parcels (when compared to the ground truth) for all the experiments. As regards real data, the region of interest was the temporal lobes and the model with two parcels was selected as the best data fit. These results are coherent with those obtained by the JPDE model in ( Chaari et al. , 2012 ) where two similar HRF profiles were estimated in the left component and one HRF profile was estimated in the right component.
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Fast joint detection-estimation of evoked brain activity in event-related fmri using a variational approach

Fast joint detection-estimation of evoked brain activity in event-related fmri using a variational approach

becomes more difÞcult to perform and that statistical power is decreased. Moreover, the more coefÞcients to recover, the more ill-posed the problem becomes. The alternative approaches that aim at keeping a single regressor per condition and add also a temporal regularization constraint to Þx the ill-posedness are the so-called regularized FIR methods [18]–[20]. Still, they do not overcome the low signal-to-noise ratio (SNR) inherent to BOLD signals, and they lack robustness especially in nonac- tivated regions. All the issues encountered in the previously mentioned approaches are linked to the sequential treatment of the detection and estimation tasks. Indeed, these two problems are strongly linked: on the one hand, a precise localization of brain activated areas strongly depends on a reliable HRF model; on the other hand, a robust estimation of the HRF is only possible in activated areas where enough relevant signal is measured [21]. This interdependence and retroactivity has motivated the idea to jointly perform these two tasks [22]–[24] (detection and estimation) in a joint detection-estimation (JDE) framework [25] which is the basis of the approach developed in this paper. To improve the estimation robustness, a gain in HRF reproducibility is performed by spatially aggregating signals so that a constant HRF shape is locally considered across a small group of voxels, i.e., a region or a parcel. The procedure then implies a partitioning of the data into functionally homoge- neous parcels, in the form of a cerebral parcellation [26]. As will be recalled in more detail in Section II, the JDE approach rests upon three main elements: 1) a nonparametric or FIR parcel-level modeling of the HRF shape; 2) prior information about the temporal smoothness of the HRF to guarantee its physiologically plausible shape; and 3) the modeling of spatial correlation between the response magnitudes of neighboring voxels within each parcel using condition-speciÞc discrete hidden Markov Þelds. In [22], [23], [25], posterior inference is carried out in a Bayesian setting using a computationally intensive Markov Chain Monte Carlo (MCMC) method, which is computationally intensive and requires Þne tuning of several parameters.
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Impact of the joint detection-estimation approach on random effects group studies in fMRI

Impact of the joint detection-estimation approach on random effects group studies in fMRI

Email: firstname.lastname@cea.fr ABSTRACT Inter-subject analysis of functional Magnetic Resonance Imaging (fMRI) data relies on single intra-subject studies, which are usually conducted using a massively univari- ate approach. In this paper, we investigate the impact of an improved intra-subject analysis on group studies. basi- cally the joint detection-estimation (JDE) framework [?, 1, 2] where an explicit characterization of the Hemodynamic Re- sponse Function (HRF) is performed at a regional scale and a stimulus-specific adaptive spatial correlation model en- ables the detection of activation clusters at voxel level. For the group statistics, we conducted several Random effect analyses (RFX) which relied either on the General Linear Model (GLM), or on the JDE analyses, or even on an inter- mediate approach named Spatially Adaptive GLM (SAGLM). Our comparative study perfomed during a fast-event related paradigm involves 18 subjects and illustrates the region- specific differences between the GLM, SAGLM and JDE analyses in terms of statistical sensitivity. On different con- trasts of interest, spatial regularization is shown to have a beneficial impact on the statistical sensitivity. Also, by study- ing the spatial variability of the HRF, we demonstrate that the JDE framework provides more robust detection perfor- mance in cognitive regions due to the higher hemodynamic variability in these areas.
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A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation

A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation

homogeneity and reliability [35] . Homogeneity means that the parcels should be small enough to meet the assumption of HRF shape invariance within each parcel, whereas reliability should guarantee that parcels are large enough to ensure reliable HRF estimation and detection performance. This issue has motivated a number of recent developments that try to cope with the identification of relevant brain parcellation of the brain [20,34,28,27,18] . In Lashkari et al. [27] , a non-parametric Bayesian approach, relying on a Dirichlet process mixture model, is considered for the activation classes in a multi- subject framework but they assume that the HRF is fixed for a given region of interest. However, among the latter works, none tries to uncover functional regions that appear homogeneous with respect to their hemodynamic profile. To the best of our knowledge, this issue has been rarely addressed in the literature. In Badillo et al. [2] the hemodynamic parcellation has been addressed using random parcella- tion and consensus clustering. A multivariate Gaussian probabilistic modelling has also been used in Fouque et al. [21] to cope with the hemodynamic parcellation issue. A joint parcellation within the JDE framework has been proposed in Chaari et al. [11,9] , giving rise to the joint parcellation detection estimation (JPDE) approach. This strategy performs online parcellation during the detection and estimation steps through the selection of hemodynamic territories, i.e., sets of voxels that share the same HRF pattern. Although automated inference of parcellation is performed in the JPDE methodology, the algorithm still requires the manual setting of the number of parcels. In a previous work Albughdadi et al. [1] , we have proposed to finely tune this parameter using an off-line model selection strategy. This procedure was based on the computation of the free energy associated with models of increasing complexity, (i.e., with an increasing number of parcels) in the VEM framework. The best model was then selected as the one maximizing the free energy. This technique was however of limited interest since it requires to run the JPDE algorithm for many candidate models, which is quite time-consuming especially when no prior information is available on the approximate number of parcels. Moreover, even if many analysis have to be conducted on the same subject, running the above-mentioned procedure to select the best model cannot be used only once since the best parcellation and estimation of HRF patterns also depend on the data and not only the number of parcels. Even if the number of parcels is right, the final result can be sub-optimal.
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Subject-level Joint Parcellation-Detection-Estimation in fMRI

Subject-level Joint Parcellation-Detection-Estimation in fMRI

Subject-level Joint Parcellation-Detection-Estimation in fMRI Lotfi CHAARI Member, Solveig BADILLO, Thomas VINCENT, Ghislaine DEHAENE-LAMBERTZ, Florence FORBES and Philippe CIUCIU Senior Member Abstract—Brain parcellation is one of the most important issues in functional MRI (fMRI) data analysis. This parcellation allows establish- ing homogeneous territories that share the same functional properties. This paper presents a model-based approach to perform a subject-level parcellation into hemodynamic territories with similar hemodynamic features which are known to vary between brain regions. We specifically investigate the use of the Joint Parcellation-Detection-Estimation (JPDE) model initially proposed in [1] to separate brain regions that match different hemodynamic response function (HRF) profiles. A hierarchi- cal Bayesian model is built and a variational expectation maximiza- tion (VEM) algorithm is deployed to perform inference. A more complete version of the JPDE model is detailed. Validation on synthetic data shows the robustness of this model to varying signal-to-noise ratio (SNR) as well as to different initializations. Our results also demonstrate that good parcellation performance is achieved even though the parcels do not involve the same amount of activation. On real fMRI data acquired in children during a language paradigm, we retrieved a parcellation along the superior temporal sulcus of the left hemisphere that matches the gradient of activation dynamics already reported in the literature.
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A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation

A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation

A B S T R A C T Deriving a meaningful functional brain parcellation is a very challenging issue in task-related fMRI analysis. The joint parcellation detection estimation model addresses this issue by inferring the parcels from fMRI data. However, it requires a priori fixing the number of parcels through an initial mask for parcellation. Hence, this di fficult task generally depends on the subject. The proposed automatic parcellation approach in this paper overcomes this limitation at the subject-level relying on a Dirichlet process mixture model combined with a hidden Markov random field to estimate the parcels and their number online. The proposed method adopts a variational expectation maximization strategy for inference. Compared to the model selection procedure in the joint parcellation detection estimation framework, our method appears more e fficient in terms of computational time and does not require finely tuned initialization. Synthetic data experiments show that our method is able to estimate the right model order and an accurate parcellation. Real data results demonstrate the ability of our method to aggregate parcels with similar hemodynamic behaviour in the right motor and bilateral occipital cortices while its discriminating power is increased compared to its ancestors. Moreover, the obtained HRF estimates are close to the canonical HRF in both cortices.
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Hemodynamically informed parcellation of cerebral FMRI data

Hemodynamically informed parcellation of cerebral FMRI data

Index Terms— joint detection-estimation, hemodynam- ics, Gaussian mixtures, parcellation, brain 1. INTRODUCTION Functional MRI (fMRI) is an imaging technique that indi- rectly measures neural activity through the Blood-oxygen- level-dependent (BOLD) signal [1], which captures the vari- ation in blood oxygenation arising from an external stimula- tion. This variation also allows the estimation of the under- lying dynamics, namely the characterization of the so-called hemodynamic response function (HRF). The hemodynamic characteristics are likely to spatially vary, but can be consid- ered constant up to a certain spatial extent. Hence, it makes sense to estimate a single HRF shape for any given area of the brain. To this end, parcel-based approaches that segment fMRI data into functionally homogeneous regions and per- form parcelwise fMRI data analysis provide an appealing framework [2].
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A joint detection-estimation framework for analysing within-subject fMRI data

A joint detection-estimation framework for analysing within-subject fMRI data

Titre: Un cadre de détection-estimation conjointe pour analyser les données individuelles d’IRMf Philippe Ciuciu 1 , Thomas Vincent 1 , Laurent Risser 2 and Sophie Donnet 3 Abstract: In this paper, we review classical and advanced methodologies for analysing within-subject functional Magnetic Resonance Imaging (fMRI) data. Such data are usually acquired during sensory or cognitive experiments that aims at stimulating the subject in the scanner and eliciting evoked brain activity. From such four-dimensional datasets (three in space, one in time), the goal is twofold: first, detecting brain regions involved in the sensory or cognitives processes that the experimental design manipulates; second, estimating the underlying activation dynamics. The first issue is usually addressed in the context of the General Linear Model (GLM), which a priori assumes a functional form for the impulse response of the hemodynamic filter. The second question aims at estimating this shape which makes sense in activating regions only. In the last five years, a novel Joint Detection-Estimation (JDE) framework addressing these two questions simultaneously has been proposed in [59, 60, 102]. We show to which extent this methodology outperforms the GLM approach in terms of statistical sensitivity and specificity, which additional questions it allows us to investigate theoretically and how it provides us with a well-adapted framework to treat spatially unsmoothed real fMRI data both in the 3D acquisition volume and on the cortical surface.
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Variational Physiologically Informed Solution to Hemodynamic and Perfusion Response Estimation from ASL fMRI Data

Variational Physiologically Informed Solution to Hemodynamic and Perfusion Response Estimation from ASL fMRI Data

1 INRIA, MISTIS, Grenoble University, LJK, Grenoble, France 2 CEA/DSV/IBM NeuroSpin center, Bˆat. 145, F-91191 Gif-sur-Yvette, France 3 INRIA, Parietal, F-91893 Orsay, France Abstract— Functional Arterial Spin Labeling (fASL) MRI can provide a quantitative measurement of cerebral blood flow. A joint detection-estimation (JDE) framework has been considered to extract task-related perfusion and hemodynamic responses not restricted to canonical response function shapes. In this work, we provide a variational expectation-maximization (VEM) algorithm for hemodynamic and perfusion responses estimation. This approach provides a lower computational load compared to previous attempts, and facilitates the incorporation of prior knowledge and constraints in the estimation. Validation on simulated and real data sets has been performed.
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Hemodynamic Brain Parcellation Using A Non-Parametric Bayesian Approach

Hemodynamic Brain Parcellation Using A Non-Parametric Bayesian Approach

b INRIA, MISTIS, Grenoble University, LJK, Grenoble, France c CEA/NeuroSpin and INRIA Saclay, Parietal, France Abstract One of the most challenging issues in task-related fMRI data analysis con- sists of deriving a meaningful functional brain parcellation. The joint par- cellation detection estimation (JPDE) model addresses this issue through an automatic inference of the parcels directly from fMRI data. However, for doing so, the number of parcels needs to be fixed a priori and an appropri- ate initialization for the mask parcellation must be provided too. Hence, this difficult task generally depends on the subject. In this paper, an auto- matic model selection approach is proposed to overcome this limitation at the subject-level. Our approach relies on a non-parametric Bayesian approach that estimates the number of parcels online using a Dirichlet process mixture model combined with a hidden Markov random field. The inference is carried out using a variational expectation maximization strategy. As compared to a standard model selection approach in the original JPDE framework, our non-parametric extension appears more efficient in terms of computational time and does not require finely tuned initialization. Our method is first validated on synthetic data to demonstrate its robustness in selecting the right model order and providing accurate estimates for the parcellation, the hemodynamic response function (HRF) shapes and the activation maps. The method is then validated on real fMRI data in two regions of interest (ROIs): right motor and bilateral occipital ROIs. The results show the ability of the proposed method to aggregate parcels with similar behaviour from a hemo- dynamic point of view, while discriminating them from other parcels having
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