A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.

Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs.

Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators.

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.

Neural correlates of visuospatial bias in patients with left hemisphere stroke: a causal functional contribution analysis based on game theory.

Stroke patients frequently display spatial neglect, an inability to report, or respond to, relevant stimuli in the contralesional space. Although this syndrome is widely considered to result from the dysfunction of a large-scale attention network, the individual contributions of damaged grey and white matter regions to neglect are still being disputed. Moreover, while the neuroanatomy of neglect in right hemispheric lesions is well studied, the contributions of left hemispheric brain regions to visuospatial processing are less well understood. To address this question, 128 left hemisphere acute stroke patients were investigated with respect to left- and rightward spatial biases measured as severity of deviation in the line bisection test and as Center of Cancellation (CoC) in the Bells Test. Causal functional contributions and interactions of nine predefined grey and white matter regions of interest in visuospatial processing were assessed using Multi-perturbation Shapley value Analysis (MSA). MSA, an inference approach based on game theory, constitutes a robust and exact multivariate mathematical method for inferring functional contributions from multi-lesion patterns. According to the analysis of performance in the Bells test, leftward attentional bias (contralesional deficit) was associated with contributions of the left superior temporal gyrus and rightward attentional bias with contributions of the left inferior parietal lobe, whereas the arcuate fascicle was contributed to both contra- and ipsilesional bias. Leftward and rightward deviations in the line bisection test were related to contributions of the superior longitudinal fascicle and the inferior parietal lobe, correspondingly. Thus, Bells test and line bisection tests, as well as ipsi- and contralesional attentional biases in these tests, have distinct neural correlates. Our findings demonstrate the contribution of different grey and white matter structures to contra- and ipsilesional spatial biases as revealed by left hemisphere stroke. The results provide new insights into the role of the left hemisphere in visuospatial processing.

A dynamical pattern recognition model of γ activity in auditory cortex.

This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75-150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain.

A neural mass model to simulate different rhythms in a cortical region.

An original neural mass model of a cortical region has been used to investigate the origin of EEG rhythms. The model consists of four interconnected neural populations: pyramidal cells, excitatory interneurons and inhibitory interneurons with slow and fast synaptic kinetics, GABA(A, slow) and GABA(A,fast) respectively. A new aspect, not present in previous versions, consists in the inclusion of a self-loop among GABA(A,fast) interneurons. The connectivity parameters among neural populations have been changed in order to reproduce different EEG rhythms. Moreover, two cortical regions have been connected by using different typologies of long range connections. Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density (PSD) peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma (gamma) band, in agreement with experimental studies. Moreover the effect of different kinds of connections between two regions has been investigated, suggesting that long range connections toward GABA(A,fast) interneurons have a major impact than connections toward pyramidal cells. The model can be of value to gain a deeper insight into mechanisms involved in the generation of gamma rhythms and to provide better understanding of cortical EEG spectra.

Time-varying cortical connectivity by adaptive multivariate estimators applied to a combined foot-lips movement.

In this paper we propose the use of an adaptive multivariate approach to define time-varying multivariate estimators based on the Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC). DTF and PDC are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. Time-varying DTF and PDC were obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Such estimators are able to follow rapid changes in the connectivity between cortical areas during an experimental task. We provide an application to the cortical estimations obtained from high resolution EEG data, recorded from a group of healthy subject during a combined foot-lips movement, and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected, one constant across the task and the other evolving during the preparation of the joint movement.

Assessment of effective connectivity among cortical regions based on a neural mass model.

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this work is to use a neural mass model to assess the effect of various connectivity patterns in the power spectral density (PSD) of cortical EEG, and investigate the possibility to derive connectivity circuits from real EEG data. To this end, a model of an individual region of interest (ROI) has been built as the parallel arrangement of three populations. The present study suggests that the model can be used as a simulation tool, able to produce reliable intracortical EEG signals. Moreover, it can be used to look for simple connectivity circuits, able to explain the main features of observed cortical PSD. These results may open new prospective in the use of neurophysiological models, instead of empirical models, to assess effective connectivity from neuroimaging information.

Comparison of different multivariate methods for the estimation of cortical connectivity: simulations and applications to EEG data.

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during the latest years, as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. Among various methods established during the years, the Directed Transfer Function (DTF), the Partial Directed Coherence (PDC) and the direct DTF (dDTF) are frequency-domain approaches to this problem, all based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. In this paper we propose the use of these methods on cortical signals estimated from high resolution EEG recordings, a non invasive method which exhibits a higher spatial resolution than conventional cerebral electromagnetic measures. The principle contribution of this work are the results of a simulation study, testing the capability of the three estimators to reconstruct a connectivity model imposed, with a particular eye on the capability to distinguish between direct and indirect causality. An application to high resolution EEG recordings during a foot movement is also presented.