4,402 research outputs found
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
Deciding when to decide : time-variant sequential sampling models explain the emergence of value-based decisions in the human brain
The cognitive and neuronal mechanisms of perceptual decision making have been successfully linked to sequential sampling models. These models describe the decision process as a gradual accumulation of sensory evidence over time. The temporal evolution of economic choices, however, remains largely unexplored. We tested whether sequential sampling models help to understand the formation of value-based decisions in terms of behavior and brain responses. We used functional magnetic resonance imaging (fMRI) to measure brain activity while human participants performed a buying task in which they freely decided upon how and when to choose. Behavior was accurately predicted by a time-variant sequential sampling model that uses a decreasing rather than fixed decision threshold to estimate the time point of the decision. Presupplementary motor area, caudate nucleus, and anterior insula activation was associated with the accumulation of evidence over time. Furthermore, at the beginning of the decision process the fMRI signal in these regions accounted for trial-by-trial deviations from behavioral model predictions: relatively high activation preceded relatively early responses. The updating of value information was correlated with signals in the ventromedial prefrontal cortex, left and right orbitofrontal cortex, and ventral striatum but also in the primary motor cortex well before the response itself. Our results support a view of value-based decisions as emerging from sequential sampling of evidence and suggest a close link between the accumulation process and activity in the motor system when people are free to respond at any time
A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments
Effective connectivity analysis provides an understanding of the functional
organization of the brain by studying how activated regions influence one
other. We propose a nonparametric Bayesian approach to model effective
connectivity assuming a dynamic nonstationary neuronal system. Our approach
uses the Dirichlet process to specify an appropriate (most plausible according
to our prior beliefs) dynamic model as the "expectation" of a set of plausible
models upon which we assign a probability distribution. This addresses model
uncertainty associated with dynamic effective connectivity. We derive a Gibbs
sampling approach to sample from the joint (and marginal) posterior
distributions of the unknowns. Results on simulation experiments demonstrate
our model to be flexible and a better candidate in many situations. We also
used our approach to analyzing functional Magnetic Resonance Imaging (fMRI)
data on a Stroop task: our analysis provided new insight into the mechanism by
which an individual brain distinguishes and learns about shapes of objects.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS470 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A common neural scale for the subjective pleasantness of different primary rewards.
When an economic decision is taken, it is between goals with different values, and the values must be on the same scale. Here, we used functional MRI to search for a brain region that represents the subjective pleasantness of two different rewards on the same neural scale. We found activity in the ventral prefrontal cortex that correlated with the subjective pleasantness of two fundamentally different rewards, taste in the mouth and warmth on the hand. The evidence came from two different investigations, a between-group comparison of two independent fMRI studies, and from a within-subject study. In the latter, we showed that neural activity in the same voxels in the ventral prefrontal cortex correlated with the subjective pleasantness of the different rewards. Moreover, the slope and intercept for the regression lines describing the relationship between activations and subjective pleasantness were highly similar for the different rewards. We also provide evidence that the activations did not simply represent multisensory integration or the salience of the rewards. The findings demonstrate the existence of a specific region in the human brain where neural activity scales with the subjective pleasantness of qualitatively different primary rewards. This suggests a principle of brain processing of importance in reward valuation and decision-making
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
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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