2,600 research outputs found
Hemodynamically informed parcellation of cerebral FMRI data
Standard detection of evoked brain activity in functional MRI (fMRI) relies
on a fixed and known shape of the impulse response of the neurovascular
coupling, namely the hemodynamic response function (HRF). To cope with this
issue, the joint detection-estimation (JDE) framework has been proposed. This
formalism enables to estimate a HRF per region but for doing so, it assumes a
prior brain partition (or parcellation) regarding hemodynamic territories. This
partition has to be accurate enough to recover accurate HRF shapes but has also
to overcome the detection-estimation issue: the lack of hemodynamics
information in the non-active positions. An hemodynamically-based parcellation
method is proposed, consisting first of a feature extraction step, followed by
a Gaussian Mixture-based parcellation, which considers the injection of the
activation levels in the parcellation process, in order to overcome the
detection-estimation issue and find the underlying hemodynamics
Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis
Background: Inference from fMRI data faces the challenge that the hemodynamic
system that relates neural activity to the observed BOLD fMRI signal is
unknown.
New Method: We propose a new Bayesian model for task fMRI data with the
following features: (i) joint estimation of brain activity and the underlying
hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a
Gaussian process (GP) prior guided by physiological information and (iii) the
predicted BOLD is not necessarily generated by a linear time-invariant (LTI)
system. We place a GP prior directly on the predicted BOLD response, rather
than on the hemodynamic response function as in previous literature. This
allows us to incorporate physiological information via the GP prior mean in a
flexible way, and simultaneously gives us the nonparametric flexibility of the
GP.
Results: Results on simulated data show that the proposed model is able to
discriminate between active and non-active voxels also when the GP prior
deviates from the true hemodynamics. Our model finds time varying dynamics when
applied to real fMRI data.
Comparison with Existing Method(s): The proposed model is better at detecting
activity in simulated data than standard models, without inflating the false
positive rate. When applied to real fMRI data, our GP model in several cases
finds brain activity where previously proposed LTI models does not.
Conclusions: We have proposed a new non-linear model for the hemodynamics in
task fMRI, that is able to detect active voxels, and gives the opportunity to
ask new kinds of questions related to hemodynamics.Comment: 18 pages, 14 figure
Multiscale adaptive smoothing models for the hemodynamic response function in fMRI
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
Semiparametric Bayesian models for human brain mapping
Functional magnetic resonance imaging (fMRI) has led to enormous progress in human brain mapping. Adequate analysis of the massive spatiotemporal data sets generated by this imaging technique, combining parametric and non-parametric components, imposes challenging problems in statistical modelling. Complex hierarchical Bayesian models in combination with computer-intensive Markov chain Monte Carlo inference are promising tools.The purpose of this paper is twofold. First, it provides a review of general semiparametric Bayesian models for the analysis of fMRI data. Most approaches focus on important but separate temporal or spatial aspects of the overall problem, or they proceed by stepwise procedures. Therefore, as a second aim, we suggest a complete spatiotemporal model for analysing fMRI data within a unified semiparametric Bayesian framework. An application to data from a visual stimulation experiment illustrates our approach and demonstrates its computational feasibility
Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach
In standard clinical within-subject analyses of event-related fMRI data, two
steps are usually performed separately: detection of brain activity and
estimation of the hemodynamic response. Because these two steps are inherently
linked, we adopt the so-called region-based Joint Detection-Estimation (JDE)
framework that addresses this joint issue using a multivariate inference for
detection and estimation. JDE is built by making use of a regional bilinear
generative model of the BOLD response and constraining the parameter estimation
by physiological priors using temporal and spatial information in a Markovian
modeling. In contrast to previous works that use Markov Chain Monte Carlo
(MCMC) techniques to approximate the resulting intractable posterior
distribution, we recast the JDE into a missing data framework and derive a
Variational Expectation-Maximization (VEM) algorithm for its inference. A
variational approximation is used to approximate the Markovian model in the
unsupervised spatially adaptive JDE inference, which allows fine automatic
tuning of spatial regularisation parameters. It follows a new algorithm that
exhibits interesting properties compared to the previously used MCMC-based
approach. Experiments on artificial and real data show that VEM-JDE is robust
to model mis-specification and provides computational gain while maintaining
good performance in terms of activation detection and hemodynamic shape
recovery
HRF estimation improves sensitivity of fMRI encoding and decoding models
Extracting activation patterns from functional Magnetic Resonance Images
(fMRI) datasets remains challenging in rapid-event designs due to the inherent
delay of blood oxygen level-dependent (BOLD) signal. The general linear model
(GLM) allows to estimate the activation from a design matrix and a fixed
hemodynamic response function (HRF). However, the HRF is known to vary
substantially between subjects and brain regions. In this paper, we propose a
model for jointly estimating the hemodynamic response function (HRF) and the
activation patterns via a low-rank representation of task effects.This model is
based on the linearity assumption behind the GLM and can be computed using
standard gradient-based solvers. We use the activation patterns computed by our
model as input data for encoding and decoding studies and report performance
improvement in both settings.Comment: 3nd International Workshop on Pattern Recognition in NeuroImaging
(2013
Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration
Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship
Group-level impacts of within- and between-subject hemodynamic variability in fMRI
International audienceInter-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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