1,363 research outputs found

    Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

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    In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework

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    International audienceIdentifying 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 demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool

    Tensor Analysis and Fusion of Multimodal Brain Images

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    Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

    Locally Estimated Hemodynamic Response Function and Activation Detection Sensitivity in Heroin-Cue Reactivity Study

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    INTRODUCTION: A fixed hemodynamic response function (HRF) is commonly used for functional magnetic resonance imaging (fMRI) analysis. However, HRF may vary from region to region and subject to subject. We investigated the effect of locally estimated HRF (in functionally homogenous parcels) on activation detection sensitivity in a heroin cue reactivity study. METHODS: We proposed a novel exploratory method for brain parcellation based on a probabilistic model to segregate the brain into spatially connected and functionally homogeneous components. Then, we estimated HRF and detected activated regions in response to an experimental task in each parcel using a joint detection estimation (JDE) method. We compared the proposed JDE method with the general linear model (GLM) that uses a fixed HRF and is implemented in FEAT (as a part of FMRIB Software Library, version 4.1). RESULTS: 1) Regions detected by JDE are larger than those detected by fixed HRF, 2) In group analysis, JDE found areas of activation not detected by fixed HRF. It detected drug craving a priori regions-of-interest in the limbic lobe (anterior cingulate cortex [ACC], posterior cingulate cortex [PCC] and cingulate gyrus), basal ganglia, especially striatum (putamen and head of caudate), and cerebellum in addition to the areas detected by the fixed HRF method, 3) JDE obtained higher Z-values of local maxima compared to those obtained by fixed HRF. CONCLUSION: In our study of heroin cue reactivity, our proposed method (that estimates HRF locally) outperformed the conventional GLM that uses a fixed HRF

    Hemodynamic Brain Parcellation Using A Non-Parametric Bayesian Approach

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    One of the most challenging issues in task-related fMRI data analysis consists of deriving a meaningful functional brain parcellation. The joint parcellation 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 appropriate initialization for the mask parcellation must be provided too. Hence, this difficult task generally depends on the subject. In this paper, an automatic 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 hemodynamic point of view, while discriminating them from other parcels having different hemodynamic properties. The HRF estimates of the dfferent hemodynamic territories obtained with our approach are close the the canonical HRF shape in both the right motor and the bilateral occipital cortices. The discrimination power of the proposed approach is increased compared to its ancestors where the results on real data show its ability to discriminate HRF profiles with different Full Width at Half Maximum (FWHM). The robust performance of detecting the elicited task-related activity is confirmed by comparing the neural response level estimates obtained using our approach with those obtained using the joint detection estimation (JDE) model
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