Modulation of perception and brain activity by predictable trajectories of facial expressions.

People track facial expression dynamics with ease to accurately perceive distinct emotions. Although the superior temporal sulcus (STS) appears to possess mechanisms for perceiving changeable facial attributes such as expressions, the nature of the underlying neural computations is not known. Motivated by novel theoretical accounts, we hypothesized that visual and motor areas represent expressions as anticipated motion trajectories. Using magnetoencephalography, we show predictable transitions between fearful and neutral expressions (compared with scrambled and static presentations) heighten activity in visual cortex as quickly as 165 ms poststimulus onset and later (237 ms) engage fusiform gyrus, STS and premotor areas. Consistent with proposed models of biological motion representation, we suggest that visual areas predictively represent coherent facial trajectories. We show that such representations bias emotion perception of subsequent static faces, suggesting that facial movements elicit predictions that bias perception. Our findings reveal critical processes evoked in the perception of dynamic stimuli such as facial expressions, which can endow perception with temporal continuity.

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models.

In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power.

Changing meaning causes coupling changes within higher levels of the cortical hierarchy.

Processing of speech and nonspeech sounds occurs bilaterally within primary auditory cortex and surrounding regions of the superior temporal gyrus; however, the manner in which these regions interact during speech and nonspeech processing is not well understood. Here, we investigate the underlying neuronal architecture of the auditory system with magnetoencephalography and a mismatch paradigm. We used a spoken word as a repeating "standard" and periodically introduced 3 "oddball" stimuli that differed in the frequency spectrum of the word's vowel. The closest deviant was perceived as the same vowel as the standard, whereas the other 2 deviants were perceived as belonging to different vowel categories. The neuronal responses to these vowel stimuli were compared with responses elicited by perceptually matched tone stimuli under the same paradigm. For both speech and tones, deviant stimuli induced coupling changes within the same bilateral temporal lobe system. However, vowel oddball effects increased coupling within the left posterior superior temporal gyrus, whereas perceptually equivalent nonspeech oddball effects increased coupling within the right primary auditory cortex. Thus, we show a dissociation in neuronal interactions, occurring at both different hierarchal levels of the auditory system (superior temporal versus primary auditory cortex) and in different hemispheres (left versus right). This hierarchical specificity depends on whether auditory stimuli are embedded in a perceptual context (i.e., a word). Furthermore, our lateralization results suggest left hemisphere specificity for the processing of phonological stimuli, regardless of their elemental (i.e., spectrotemporal) characteristics.

Bayesian estimation of synaptic physiology from the spectral responses of neural masses.

We describe a Bayesian inference scheme for quantifying the active physiology of neuronal ensembles using local field recordings of synaptic potentials. This entails the inversion of a generative neural mass model of steady-state spectral activity. The inversion uses Expectation Maximization (EM) to furnish the posterior probability of key synaptic parameters and the marginal likelihood of the model itself. The neural mass model embeds prior knowledge pertaining to both the anatomical [synaptic] circuitry and plausible trajectories of neuronal dynamics. This model comprises a population of excitatory pyramidal cells, under local interneuron inhibition and driving excitation from layer IV stellate cells. Under quasi-stationary assumptions, the model can predict the spectral profile of local field potentials (LFP). This means model parameters can be optimised given real electrophysiological observations. The validity of inferences about synaptic parameters is demonstrated using simulated data and experimental recordings from the medial prefrontal cortex of control and isolation-reared Wistar rats. Specifically, we examined the maximum a posteriori estimates of parameters describing synaptic function in the two groups and tested predictions derived from concomitant microdialysis measures. The modelling of the LFP recordings revealed (i) a sensitization of post-synaptic excitatory responses, particularly marked in pyramidal cells, in the medial prefrontal cortex of socially isolated rats and (ii) increased neuronal adaptation. These inferences were consistent with predictions derived from experimental microdialysis measures of extracellular glutamate levels.

Dynamic causal modelling of induced responses.

This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic sources to a spectral perturbation. The model parameters encode the frequency response to exogenous input and coupling among sources and different frequencies. The Bayesian inversion of this model, given data enables inferences about the parameters of a particular model and allows us to compare different models, or hypotheses. One key aspect of the model is that it differentiates between linear and non-linear coupling; which correspond to within and between-frequency coupling respectively. To establish the face validity of our approach, we generate synthetic data and test the identifiability of various parameters to ensure they can be estimated accurately, under different levels of noise. We then apply our model to EEG data from a face-perception experiment, to ask whether there is evidence for non-linear coupling between early visual cortex and fusiform areas.

Dynamic causal modelling for fMRI: a two-state model.

Dynamical causal modelling (DCM) for functional magnetic resonance imaging (fMRI) is a technique to infer directed connectivity among brain regions. These models distinguish between a neuronal level, which models neuronal interactions among regions, and an observation level, which models the hemodynamic responses each region. The original DCM formulation considered only one neuronal state per region. In this work, we adopt a more plausible and less constrained neuronal model, using two neuronal states (populations) per region. Critically, this gives us an explicit model of intrinsic (between-population) connectivity within a region. In addition, by using positivity constraints, the model conforms to the organization of real cortical hierarchies, whose extrinsic connections are excitatory (glutamatergic). By incorporating two populations within each region we can model selective changes in both extrinsic and intrinsic connectivity. Using synthetic data, we show that the two-state model is internal consistent and identifiable. We then apply the model to real data, explicitly modelling intrinsic connections. Using model comparison, we found that the two-state model is better than the single-state model. Furthermore, using the two-state model we find that it is possible to disambiguate between subtle changes in coupling; we were able to show that attentional gain, in the context of visual motion processing, is accounted for sufficiently by an increased sensitivity of excitatory populations of neurons in V5, to forward afferents from earlier visual areas.

A neural mass model of spectral responses in electrophysiology.

We present a neural mass model of steady-state membrane potentials measured with local field potentials or electroencephalography in the frequency domain. This model is an extended version of previous dynamic causal models for investigating event-related potentials in the time-domain. In this paper, we augment the previous formulation with parameters that mediate spike-rate adaptation and recurrent intrinsic inhibitory connections. We then use linear systems analysis to show how the model's spectral response changes with its neurophysiological parameters. We demonstrate that much of the interesting behaviour depends on the non-linearity which couples mean membrane potential to mean spiking rate. This non-linearity is analogous, at the population level, to the firing rate-input curves often used to characterize single-cell responses. This function depends on the model's gain and adaptation currents which, neurobiologically, are influenced by the activity of modulatory neurotransmitters. The key contribution of this paper is to show how neuromodulatory effects can be modelled by adding adaptation currents to a simple phenomenological model of EEG. Critically, we show that these effects are expressed in a systematic way in the spectral density of EEG recordings. Inversion of the model, given such non-invasive recordings, should allow one to quantify pharmacologically induced changes in adaptation currents. In short, this work establishes a forward or generative model of electrophysiological recordings for psychopharmacological studies.

Anatomically informed basis functions.

This paper introduces the general framework, concepts, and procedures of anatomically informed basis functions (AIBF), a new method for the analysis of functional magnetic resonance imaging (fMRI) data. In contradistinction to existing voxel-based univariate or multivariate methods the approach described here can incorporate various forms of prior anatomical knowledge to specify sophisticated spatiotemporal models for fMRI time-series. In particular, we focus on anatomical prior knowledge, based on reconstructed gray matter surfaces and assumptions about the location and spatial smoothness of the blood oxygenation level dependent (BOLD) effect. After reconstruction of the grey matter surface from an individual's high-resolution T1-weighted MRI, we specify a set of anatomically informed basis functions, fit the model parameters for a single time point, using a regularized solution, and finally make inferences about the estimated parameters over time. Significant effects, induced by the experimental paradigm, can then be visualized in the native voxel-space or on the reconstructed folded, inflated, or flattened cortical surface. As an example, we apply the approach to a fMRI study (finger opposition task) and compare the results to those of a voxel-based analysis as implemented in the Statistical Parametric Mapping package (SPM99). Additionally, we show, using simulated data, that the approach offers several desirable features particularly in terms of superresolution and localization.

Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model.

The assessment of significant activations in functional imaging using voxel-based methods often relies on results derived from the theory of Gaussian random fields. These results solve the multiple comparison problem and assume that the spatial correlation or smoothness of the data is known or can be estimated. End results (i. e., P values associated with local maxima, clusters, or sets of clusters) critically depend on this assessment, which should be as exact and as reliable as possible. In some earlier implementations of statistical parametric mapping (SPM) (SPM94, SPM95) the smoothness was assessed on Gaussianized t-fields (Gt-f) that are not generally free of physiological signal. This technique has two limitations. First, the estimation is not stable (the variance of the estimator being far from negligible) and, second, physiological signal in the Gt-f will bias the estimation. In this paper, we describe an estimation method that overcomes these drawbacks. The new approach involves estimating the smoothness of standardized residual fields which approximates the smoothness of the component fields of the associated t-field. Knowing the smoothness of these component fields is important because it allows one to compute corrected P values for statistical fields other than the t-field or the Gt-f (e.g., the F-map) and eschews bias due to deviation from the null hypothesis. We validate the method on simulated data and demonstrate it using data from a functional MRI study.

MRI and PET coregistration--a cross validation of statistical parametric mapping and automated image registration.

Coregistration of functional PET and T1-weighted MR images is a necessary step for combining functional information from PET images with anatomical information in MR images. Several coregistration algorithms have been published and are used in functional brain imaging studies. In this paper, we present a comparison and cross validation of the two most widely used coregistration routines (Friston et al., 1995, Hum. Brain Map. 2: 165-189; Woods et al., 1993, J. Comput. Assisted Tomogr: 17: 536-546). Several transformations were applied to high-resolution anatomical MR images to generate simulated PET images so that the exact (rigid body) transformations between each MR image and its associated simulated PET images were known. The estimation error of a coregistration in relation to the known transformation allows a comparison of the performance of different coregistration routines. Under the assumption that the simulated PET images embody the salient features of real PET images with respect to coregistration, this study shows that the routines examined reliably solve the MRI to PET coregistration problem.