Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG

Using Granger-Geweke causality model to evaluate the effective connectivity of primary motor cortex (M1), supplementary motor area (SMA) and cerebellum

Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task can produce relatively reliable brain response. As an extension of our previous study, we developed an algorithm based on the classical Granger-Geweke causality model to further investigate the effective connectivity of three brain regions (left primary motor cortex (M1), supplementary motor area (SMA) and right cerebellum) that showed the most robust brain activations. Our computational results not only confirm the strong linear feedback among SMA, M1 and right cerebellum, but also demonstrate that M1 is the hub of these three regions indicated by the anatomy research. Moreover, the model predicts the high intermediate node density existing in the area between SMA and M1, which will stimulate the imaging experimentalists to carry out new experiments to validate this postulation.

Neocortical seizure foci localization by means of a directed transfer function method

PURPOSE

Determination of the origin of extratemporal neocortical onset seizures is often challenging due to the rapid speed at which they propagate throughout the cortex. Typically, these patients are poor surgical candidates and many times experience recurrences of seizure activity following resection of the assumed seizure focus.

METHODS

We applied a causal measurement technique--the directed transfer function (DTF)--in an effort to determine the cortical location responsible for the propagation of the seizure activity. Intracranial seizure recordings were obtained from a group of 11 pediatric patients with medically intractable neocortical-onset epilepsy. Time windows were selected from the recordings following onset of the ictal activity. The DTF was applied to the selected time windows, and the frequency-specific statistically significant source activity arising from each cortical recording site was quantified. The DTF-estimated source activity was then compared with the seizure-onset zone(s) identified by the epileptologists.

RESULTS

In an analysis of the 11 pediatric patients, the DTF was shown to identify estimated ictal sources that were highly correlated with the clinically identified foci. In addition, it was observed that in the patients with multiple ictal foci, the topography of the casual source activity from the analyzed seizures was associated with the separate clinically identified seizure-onset zones.

DISCUSSION

Although localization of neocortical-onset seizures is typically challenging, the causal measures employed in this study-namely the directed transfer function-identified generators of the ictal activity that were highly correlated with the cortical regions identified as the seizure-onset zones by the epileptologists. This technique could prove useful in the identification of seizure-specific propagation pathways in the presurgical evaluation of patients with epilepsy.

Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using structural equation modeling (SEM) and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested that the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain.

Changes in EEG power spectral density and cortical connectivity in healthy and tetraplegic patients during a motor imagery task

Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs) during a foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. The model assumes that each ROI can generate an intrinsic rhythm in the beta range, and receives rhythms in the alpha and gamma ranges from other two regions. Connectivity strengths among the ROIs were estimated by means of an original genetic algorithm that tries to minimize several cost functions of the difference between real and model power spectral densities. Results show that the stronger connections are those from the cingulate cortex to the primary and supplementary motor areas, thus emphasizing the pivotal role played by the CMA_L during the task. Tetraplegic patients exhibit higher connectivity strength on average, with significant statistical differences in some connections. The results are commented and virtues and limitations of the proposed method discussed.

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.

Source connectivity analysis with MEG and EEG

Interactions between functionally specialized brain regions are crucial for normal brain function. Magnetoencephalography (MEG) and electroencephalography (EEG) are techniques suited to capture these interactions, because they provide whole head measurements of brain activity in the millisecond range. More than one sensor picks up the activity of an underlying source. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. Consequentially, neuronal interactions should be studied on the level of the reconstructed sources. This article reviews several methods that have been applied to investigate interactions between brain regions in source space. We will mainly focus on the different measures used to quantify connectivity, and on the different strategies adopted to identify regions of interest. Despite various successful accounts of MEG and EEG source connectivity, caution with respect to the interpretation of the results is still warranted. This is due to the fact that effects of field spread can never be completely abolished in source space. However, in this very exciting and developing field of research this cautionary note should not discourage researchers from further investigation into the connectivity between neuronal sources.

Directional information flows between brain hemispheres across waking, non-REM and REM sleep states: an EEG study

The present electroencephalographic (EEG) study evaluated the hypothesis of a preferred directionality of communication flows between brain hemispheres across 24 h (i.e., during the whole daytime and nighttime), as an extension of a recent report showing changes in preferred directionality from pre-sleep wake to early sleep stages. Scalp EEGs were recorded in 10 normal volunteers during daytime wakefulness (eyes closed; first period: from 10:00 to 13:00 h; second period: from 14:00 to 18:00 h; third period: from 19:00 to 22:00 h) and nighttime sleep (four NREM-REM cycles). EEG rhythms of interest were delta (1-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), sigma (12-15 Hz) and beta (16-28 Hz). The direction of the inter-hemispheric information flow was evaluated by computing the directed transfer function (DTF) from these EEG rhythms. Inter-hemispheric directional flows varied as a function of the state of consciousness (wake, NREM sleep, REM sleep) and in relation to different cerebral areas. During the daytime, alpha and beta rhythms conveyed inter-hemispheric signals with preferred Left-to-Right hemisphere direction in parietal and central areas, respectively. During the NREM sleep periods of nighttime, the direction of inter-hemispheric DTF information flows conveyed by central beta rhythms was again preponderant from Left-to-Right hemisphere in the stage 2, independent of cortical areas. No preferred direction emerged across the REM periods. These results support the hypothesis that specific directionality of communication flows between brain hemispheres is associated with wakefulness, NREM (particularly stage 2) and REM states during daytime and nighttime. They also reinforce the suggestive hypothesis of a relationship between inter-hemispheric directionality of EEG functional coupling and frequency of the EEG rhythms.

Estimation of effective and functional cortical connectivity from neuroelectric and hemodynamic recordings

In this paper, different linear and nonlinear methodologies for the estimation of cortical connectivity from neuroelectric and hemodynamic measurements are reviewed and applied on common data set in order to highlight similarities and differences in the results. Different effective and functional connectivity methods were applied to motor and cognitive data sets, including structural equation modeling (SEM), directed transfer function (DTF), partial directed coherence (PDC), and direct directed transfer function (dDTF). Comparisons were made between the results in order to understand if, for a same dataset, effective and functional connectivity estimators can return the same cortical connectivity patterns. An application of a nonlinear method [phase synchronization index (PSI)] to similar executed and imagined movements was also reviewed. Connectivity patterns estimated with the use of the neuroelectric information and of the information from the multimodal integration of neuroelectric and hemodynamic data were also compared. Results suggests that the estimation of the cortical connectivity patterns performed with the linear methods (SEM, DTF, PDC, dDTF) or with the nonlinear method (PSI) on movement related potentials returned similar cortical networks. Differences in cortical connectivity were noted between the patterns estimated with the use of multimodal integration and those estimated by using only the neuroelectric data.

A regulation role of the prefrontal cortex in the fist-edge-palm task: evidence from functional connectivity analysis

The Fist-Edge-Palm (FEP) task is a motor sequencing task that is widely used in neurological examination. Deficits in this task are believed to reflect impairment in the frontal lobe regions. However, two recent functional brain imaging studies of the FEP task using conventional subtraction analysis failed to demonstrate FEP-induced activation in the prefrontal cortex (PFC), which contradicts existing neuropsychological literature. In this study, psychophysiological interaction (PPI) analysis was used to reanalyze our previous neuroimaging dataset from 10 healthy subjects in order to evaluate the changes of functional connectivity between the sensorimotor cortex and the prefrontal regions during the performances of the FEP task relative to simple motor control tasks. The PPI analysis revealed significantly increased functional connectivity between bilateral sensorimotor cortex and the right inferior and middle frontal cortex during the performance of the FEP task compared with the control tasks. However, regional signal changes showed no significant activation differences in these prefrontal regions. These results provide evidence supporting the involvement of the frontal lobe in the performance of the FEP task, and suggest a role of regulation, rather than direct participation, of the prefrontal cortex in the execution of complex motor sequence tasks such as the FEP task.

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 basis for the brain responses to the marketing messages: an high resolution EEG study

We investigated the behaviour of the brain during the visualization of commercial videos by tracking the cortical activity and the functional connectivity changes in normal subjects. High resolution EEG recordings were performed in a group of healthy subjects, and the cortical activity during the visualization of standard commercial spots and emotional spots (no profit companies) was estimated by using the solution of the linear inverse problem with the use of realistic head models. The cortical activity was evaluated in several regions of interest (ROIs) coincident with the Brodmann areas. The pattern of cortical connectivity was obtained by using the partial directed coherence (PDC) and investigated in the time and frequency domains, in the principal four frequency bands, namely the theta (4-7 Hz), the alpha (8-12 Hz), the beta (13-30 Hz) and the gamma (above 30 Hz). Results suggest a time-varying engagement of the orbitofrontal circuits that is thought to be involved in the reward value of the stimuli.

Use of a neural mass model for the analysis of effective connectivity among cortical regions based on high resolution EEG recordings

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 cortical electroencephalogram (EEG) power spectral density, and investigate the possibility to derive connectivity circuits from EEG data. To this end, a model of an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each described as in Wendling et al. (Eur J Neurosci 15:1499-1508, 2002). Connectivity among ROIs includes three parameters, which specify the strength of connection in the different frequency bands. The following main steps have been followed: (1) we analyzed how the power spectral density (PSD) is significantly modified by the kind of coupling hypothesized among the ROIs; (2) with the model, and using an automatic fitting procedure, we looked for a simple connectivity circuit able to reproduce PSD of cortical EEG in three ROIs during a finger-movement task. The estimated parameters represent the strength of connections among the ROIs in the different frequency bands. Cortical EEGs were computed with an inverse propagation algorithm, starting from measurement performed with 96 electrodes on the scalp. The present study suggests that the model can be used as a simulation tool, able to mimic the effect of connectivity on EEG. 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 prospectives in the use of neurophysiological models, instead of empirical models, to assess effective connectivity from neuroimaging information.

Synchronous dynamic brain networks revealed by magnetoencephalography

We visualized synchronous dynamic brain networks by using prewhitened (stationary) magnetoencephalography signals. Data were acquired from 248 axial gradiometers while 10 subjects fixated on a spot of light for 45 s. After fitting an autoregressive integrative moving average model and taking the residuals, all pairwise, zero-lag, partial cross-correlations (PCC(ij)(0)) between the i and j sensors were calculated, providing estimates of the strength and sign (positive and negative) of direct synchronous coupling between neuronal populations at a 1-ms temporal resolution. Overall, 51.4% of PCC(ij)(0) were positive, and 48.6% were negative. Positive PCC(ij)(0) occurred more frequently at shorter intersensor distances and were 72% stronger than negative ones, on the average. On the basis of the estimated PCC(ij)(0), dynamic neural networks were constructed (one per subject) that showed distinct features, including several local interactions. These features were robust across subjects and could serve as a blueprint for evaluating dynamic brain function.