2,734 research outputs found
Application and validation of spatial mixture modelling for the joint detection-estimation of brain activity in fMRI.
International audienceWithin-subject analysis in event-related functional Magnetic Resonance Imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and second on (ii) an estimation step to recover the temporal dynamics of the brain response. Recently, a Bayesian detection-estimation approach that jointly addresses (i)-(ii) has been proposed in [1]. This work is based on an independent mixture model (IMM) and provides both a spatial activity map and an estimate of brain dynamics. In [2], we accounted for spatial correlation using a spatial mixture model (SMM) based on a binary Markov random field. Here, we assess the SMM robustness and flexibility on simulations which diverge from the priors and the generative BOLD model and further extend comparison between SMM and IMM on real fMRI data, focusing on a region of interest in the auditory cortex
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
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
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
Physiologically Informed Bayesian Analysis of ASL fMRI Data
Arterial Spin Labelling (ASL) functional Magnetic Resonance Imaging (fMRI)
data provides a quantitative measure of blood perfusion, that can be correlated
to neuronal activation. In contrast to BOLD measure, it is a direct measure of
cerebral blood flow. However, ASL data has a lower SNR and resolution so that
the recovery of the perfusion response of interest suffers from the
contamination by a stronger hemodynamic component in the ASL signal. In this
work we consider a model of both hemodynamic and perfusion components within
the ASL signal. A physiological link between these two components is analyzed
and used for a more accurate estimation of the perfusion response function in
particular in the usual ASL low SNR conditions
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
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