Dynamic representations and generative models of

Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequency-specific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However, their functional significance remains a matter of debate. Some of the mechanisms that generate these signals are known at the cellular level and rest on a balance of excitatory and inhibitory interactions within and between populations of neurons. The kinetics of the ensuing population dynamics determine the frequency of oscillations. In this work we extended the classical nonlinear lumped-parameter model of alpha rhythms, initially developed by Lopes da Silva and colleagues [Kybernetik 15 (1974) 27], to generate more complex dynamics. We show that the whole spectrum of MEG/EEG signals can be reproduced within the oscillatory regime of this model by simply changing the population kinetics. We used the model to examine the influence of coupling strength and propagation delay on the rhythms generated by coupled cortical areas. The main findings were that (1) coupling induces phase-locked activity, with a phase shift of 0 or π when the coupling is bidirectional, and (2) both coupling and propagation delay are critical determinants of the MEG/EEG spectrum. In forthcoming articles, we will use this model to (1) estimate how neuronal interactions are expressed in MEG/EEG oscillations and establish the construct validity of various indices of nonlinear coupling, and (2) generate event-related transients to derive physiologically informed basis functions for statistical modelling of average evoked responses.

Our data are random fields of multivariate Gaussian observations, and we fit a multivariate linear model with common design matrix at each point. We are interested in detecting those points where some of the coefficients are non-zero using classical multivariate statistics evaluated at each point. The problem is to find the P-value of the maximum of such a random field of test statistics. We approximate this by the expected Euler characteristic of the excursion set. Our main result is a very simple method for calculating this, which not only gives us the previous result of Cao and Worsley [5] for Hotelling’s T 2, but also random fields of Roy’s maximum root, maximum canonical correlations [4], multilinear forms [11], ¯χ 2 [12, 18] and χ 2 scale space [28]. The trick involves approaching the problem from the point of view of Roy’s union-intersection principle. The results are applied to a problem in shape analysis, where we look for brain damage due to non-missile trauma.

The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper we review two different approaches to modelling effective connectivity from fMRI data, Structural Equation Models (SEMs) and Dynamic Causal Models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM a distinction is made between the ‘neuronal level ’ and the ‘hemodynamic level’. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics which in turn cause changes in the observed hemodynamics. In SEM changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.

The classical model of blood oxygen level-dependent (BOLD) responses by Buxton et al. [Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model. Magn. Reson. Med. 39, 855–864] has been very important in providing a biophysically plausible framework for explaining different aspects of hemodynamic responses. It also plays an important role in the hemodynamic forward model for dynamic causal modeling (DCM) of fMRI data. A recent study by Obata et al. [Obata, T., Liu, T.T., Miller, K.L., Luh, W.M., Wong, E.C., Frank, L.R., Buxton, R.B., 2004. Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the Balloon model to the interpretation of BOLD transients. NeuroImage 21, 144–153] linearized the BOLD signal equation and suggested a revised form for the model coefficients. In this paper, we show that the classical and revised models are special

Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker–Planck equation. The solution of this equation is the temporal evolution of a probability density over state-space, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a

Effective mental functioning requires that cognition be protected from emotional conflict from interference by task-irrelevant emotionally salient stimuli. The neural mechanisms by which the brain detects and resolves emotional conflict are still largely unknown, however. Drawing on the classic Stroop conflict task, we developed a protocol that allowed us to dissociate the generation and monitoring of emotional conflict from its resolution. Using functional magnetic resonance imaging (fMRI), we find that activity in the amygdala and dorsomedial and dorsolateral prefrontal cortices reflects the amount of emotional conflict. By contrast, the resolution of emotional conflict is associated with activation of the rostral anterior cingulate cortex. Activation of the rostral cingulate is predicted by the amount of previous-trial conflict-related neural activity and is accompanied by a simultaneous and correlated reduction of amygdalar activity. These data suggest that emotional conflict is resolved through top-down inhibition of amygdalar activity by the rostral cingulate cortex.

This article is about intersubject variability in the functional integration of activity in different brain regions. Previous studies of functional and effective connectivity have dealt with intersubject variability by analyzing data from different subjects separately or pretending the data came from the same subject. These approaches do not allow one to test for differences among subjects. The aim of this work was to illustrate how differences in connectivity among subjects can be addressed explicitly using structural equation modeling. This is enabled by constructing a multisubject network that comprises m regions of interest for each of the n subjects studied, resulting in a total of m � n nodes. Constructing a network of regions from different subjects may seem counterintuitive

It is well known that performance on a given trial of a cognitive task is affected by the nature of previous trials. For example, conflict effects on interference tasks, such as the Stroop task, are reduced subsequent to high-conflict trials relative to low-conflict trials. This interaction effect between previous and current trial types is called bconflict adaptationQ and thought to be due to processing adjustments in cognitive control. The current study aimed to identify the neural substrates of cognitive control during conflict adaptation by isolating neural correlates of reduced conflict from those of increased cognitive control. We expected cognitive control to be implemented by prefrontal cortex through context-specific modulation of posterior regions involved in sensory and motor aspects of task performance. We collected event-related fMRI data on a color-word naming Stroop task and found distinct fronto-parietal networks of current trial conflict detection and conflict adaptation through cognitive control. Conflict adaptation was associated with increased activity in left middle frontal gyrus (GFm) and superior frontal gyrus (GFs), consistent with increased cognitive control, and with decreased activity in bilateral prefrontal and parietal cortices, consistent with reduced response conflict. Psychophysiological interaction analysis (PPI) revealed that cognitive control activation in GFs and GFm was accompanied by increased functional integration with bilateral inferior frontal, right temporal and parietal areas, and the anterior cerebellum. These data suggest that cognitive control is implemented by medial and lateral prefrontal cortices that bias processes in regions that have been implicated in high-level perceptual and motor processes.

We used fMRI to examine neural responses when subjects experienced a tactile stimulus that was either self-produced or externally produced. The somatosensory cortex showed increased levels of activity when the stimulus was externally produced. In the cerebellum there was less activity associated with a movement that generated a tactile stimulus than with a movement that did not. This difference suggests that the cerebellum is involved in predicting the specific sensory consequences of movements and providing the signal that is used to attenuate the sensory response to self-generated stimulation. In this paper, we use regression analyses to test this hypothesis explicitly. Specifically, we predicted that activity in the cerebellum contributes to the decrease in somatosensory cortex activity during self-produced tactile stimulation. Evidence in favor of this hypothesis was obtained by demonstrating that activity in the thalamus and primary and secondary somatosensory cortices significantly regressed on activity in the cerebellum when tactile stimuli were self-produced but not when they were externally produced. This supports the proposal that the cerebellum is involved in predicting the sensory consequences of movements. In the present study, this prediction is accurate when tactile stimuli are self-produced relative to when they are externally produced, and is therefore used to attenuate the somatosensory response to the former type of tactile stimulation but not the latter.