Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment

Ciuciu, Philippe; Poline, Jean‐Baptiste; Marrelec, Guillaume; Idier, Jérôme; Pallier, Christophe; Benali, Habib

doi:10.1109/tmi.2003.817759

Cited by 115 publications

(91 citation statements)

References 40 publications

(80 reference statements)

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“…In the GLM approach, statistical analysis tools are used to assess the convolution of the neural activity signal with a predefined convolution kernel called the Hemodynamic Response Function (HRF). Several basis functions have been used to represent HRF including Poisson functions [4], Gaussian functions A c c e p t e d M a n u s c r i p t [5], Gamma functions [6,7], and inverse Logit functions [8]. However, this class of methods is blind to the physiological aspects that underlie the BOLD transients and has the main drawback of excluding the nonlinear effects of the BOLD, as shown in [9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements

Khoram

Zayane

Djellouli

et al. 2016

Journal of Neuroscience Methods

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Page 1 of 42A c c e p t e d M a n u s c r i p t• An efficient solution methodology for estimating the parameters of the brain response model is proposed.• This method distinguishes itself from existing calibrating techniques by employing intelligently (a) the Newton algorithm, (b) a Tikhonov regularization approach, and (c) a Kalman filtering procedure.• Both synthetic and real fMRI measurements were used to assess the performance of this method.• The fast convergence, the accuracy, and the robustness to the noise effect of this method are clearly demonstrated by the reported numerical results.• This methods outperforms existing methods. Highlights (for review)Page 2 of 42 A c c e p t e d M a n u s c r i p t Abstract BackgroundThe calibration of the hemodynamic model that describes changes in blood flow and blood oxygenation during brain activation is a crucial step for successfully monitoring and possibly predicting brain activity. This in turn has the potential to provide diagnosis and treatment of brain diseases in early stages. New MethodWe propose an efficient numerical procedure for calibrating the hemodynamic model using some fMRI measurements. The proposed solution methodology is a regularized iterative method equipped with a Kalman filtering-type procedure. The Newton component of the proposed method addresses the nonlinear aspect of the problem. The regularization feature is used to ensure the stability of the algorithm. The Kalman filter procedure is incorporated here to address the noise in the data. Results Numerical results obtained with synthetic data as well as with real fMRI measurements are presented to illustrate the accuracy, robustness to the noise, and the cost-effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements

Khoram

Zayane

Djellouli

et al. 2016

Journal of Neuroscience Methods

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“…In [7], Groutte et al propose a non-parametric approach where a Finite Impulse Response (FIR) filter is used to describe the Hemodynamic Response Function (HRF) and smoothing constraints are imposed at the solution by using a regularization matrix. Ciuciu et al describe another non-parametric approach for the Bayesian estimation of the HRF in [3]. The authors make use of temporal prior terms to introduce physiological knowledge about the HRF.…”

Section: Introductionmentioning

confidence: 99%

Automatic HyperParameter Estimation in fMRI

Afonso

Figueiredo²,

Sanches

2011

Pattern Recognition and Image Analysis

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Abstract. Maximum a posteriori (MAP) in the scope of the Bayesian framework is a common criterion used in a large number of estimation and decision problems. In image reconstruction problems, typically, the image to be estimated is modeled as a Markov Random Fields (MRF) described by a Gibbs distribution. In this case, the Gibbs energy depends on a multiplicative coefficient, called hyperparameter, that is usually manually tuned [13] in a trial and error basis. In this paper we propose an automatic hyperparameter estimation method designed in the scope of functional Magnetic Resonance Imaging (fMRI) to identify activated brain areas based on Blood Oxygen Level Dependent (BOLD) signal. This problem is formulated as classical binary detection problem in a Bayesian framework where the estimation and inference steps are joined together. The prior terms, incorporating the a priori physiological knowledge about the HRF, drift and spatial correlation across the brain (using edge preserving priors), are automatically tuned with the new proposed method. Results on real and synthetic data are presented and compared against the conventional General Linear Model (GLM) approach.

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“…In a semi-parametric framework, the HRF time course is decomposed into different periods (initial dip, attack, rise, decay, fall, …), each of them being described by specific parameters. At the same time, non-parametric approaches or Finite Impulse Response (FIR) models have emerged in the fMRI literature as a powerful tool to infer on the HRF shape (Nielsen et al, 1997;Goutte et al, 2000;Marrelec et al, 2003Marrelec et al, , 2004Ciuciu et al, 2003). Most of these works take place in the Bayesian setting and constrain the HRF to be temporally smooth, which warrants a stable estimation in case of illposed identification.…”

Section: Introductionmentioning

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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Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment

Cited by 115 publications

References 40 publications

A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements

A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements

Automatic HyperParameter Estimation in fMRI

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

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