Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: simulations and application to real data.

OBJECTIVE: To test a technique called Directed Transfer Function (DTF) for the estimation of human cortical connectivity, by means of simulation study and human study, using high resolution EEG recordings related to finger movements. METHODS: The method of the Directed Transfer Function (DTF) is a frequency-domain approach, based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. Since the spreading of the potential from the cortex to the sensors makes it difficult to infer the relation between the spatial patterns on the sensor space and those on the cortical sites, we propose the use of the DTF method on cortical signals estimated from high resolution EEG recordings, which exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. The simulation study was followed by an analysis of variance (ANOVA) of the results obtained for different levels of Signal to Noise Ratio (SNR) and temporal length, as they have been systematically imposed on simulated signals. The whole methodology was then applied to high resolution EEG data recorded during a visually paced finger movement. RESULTS: The statistical analysis performed returns that during simulations, DTF is able to estimate correctly the imposed connectivity patterns under reasonable operative conditions, i.e. when data exhibit a SNR of at least 3 and a length of at least 75 s of non-consecutive recordings at 64 Hz of sampling rate, equivalent, more generally, to 4800 data samples. CONCLUSIONS: Functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in any practical EEG recordings, by combining high resolution EEG techniques, linear inverse estimation and the DTF method. SIGNIFICANCE: The estimation of cortical connectivity can be performed not only with hemodynamic measurements, by using functional MRI recordings, but also with modern EEG recordings treated with advanced computational techniques.

Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function.

Nowadays, several types of brain imaging device are available to provide images of the functional activity of the cerebral cortex based on hemodynamic, metabolic, or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions communicate with each other. In this study, advanced methods for the estimation of cortical connectivity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multidipole source model, and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. We estimate functional cortical connectivity by computing the directed transfer function (DTF) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. The proposed method was able to unveil the direction of the information flow between the cortical regions of interest, as it is directional in nature. Furthermore, this method allows to detect changes in the time course of information flow between cortical regions in different frequency bands. The reliability of these techniques was further demonstrated by elaboration of high-resolution EEG and fMRI signals collected during visually triggered finger movements in four healthy subjects. Connectivity patterns estimated for this task reveal an involvement of right parietal and bilateral premotor and prefrontal cortical areas. This cortical region involvement resembles that revealed in previous studies where visually triggered finger movements were analyzed with the use of separate EEG or fMRI measurements.

Improved estimation of human cortical activity and connectivity with the multimodal integration of neuroelectric and hemodynamic data.

In the last decade, the possibility to noninvasively estimate cortical activity and connectivity has been highlighted by the application of the techniques known as high resolution EEG. These techniques include a subject's multi-compartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multi-dipole source model, and regularized linear inverse source estimates of cortical current density. More recently, it has proved as the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI improves dramatically the estimates of cortical activity and connectivity. Here, we present some applications of such estimation in two set of high resolution EEG and fMRI data, related to the motor (finger tapping) and cognitive (Stroop) tasks. We observed that the proposed technology was able to unveil the direction of the information flow between the cortical regions of interest.

Solving the neuroimaging puzzle: the multimodal integration of neuroelectromagnetic and functional magnetic resonance recordings.

In this chapter, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are reviewed. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model, and regularized linear inverse source estimates. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) and event-related (coupling of activated voxels) fMRI. As an example, these methods were applied to EEG (128 electrodes) and fMRI data, which were recorded in separate sessions while normal subjects executed voluntary right one-digit movements.

Time-varying cortical connectivity by high resolution EEG and directed transfer function: simulations and application to finger tapping 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. The method of the directed transfer function (DTF) is a frequency-domain approach to this problem, based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. So far, all the connectivity estimations performed on cerebral electromagnetic signals were computed between signals gathered from the electric or magnetic sensors. However, the spreading of the potential from the cortex to the sensors makes it difficult to infer the relation between the spatial patterns on the sensor space and those on the cortical sites. In this paper we propose the use of the DTF method on cortical signals estimated from high resolution EEG recordings, which exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. As main contributions of this work, we present the results of a wide simulation study, aiming to evaluate performances of DTF application on this kind of data, and a statistical analysis (via the ANOVA, analysis of variance) of the results obtained for different levels of signal to noise ratio and temporal length, as they have been systematically imposed on simulated signals. Finally, we provide an application to the estimation of cortical connectivity from high resolution EEG recordings related to finger tapping movements.

Changes in apoprotein distribution between lipoprotein classes of hypercholesterolemic rats treated with ascorbate.

It is known that ascorbate has a lipid lowering effect, accompanied by a drop of apo B, in rats fed a diet enriched with 1.5% of cholesterol (Nath diet). In order to better clarify the role exerted by ascorbate in lipid metabolism, the effect of ascorbate administration on apolipoprotein pattern in rats fed the Nath diet was investigated. Wistar male rats fed for two months the Nath diet were treated i.p. with 60 mg/kg of body weight of ascorbate for 10 days. Blood collection before and after the treatment was performed by intracardiac puncture. Lipoproteins were prepared by preparative ultracentrifugation and their apoprotein content was obtained by densitometric scanning of the apoprotein electhrophoretic pattern. The decrease of total plasma cholesterol and triglycerides and of cholesterol, triglycerides and protein content of all plasma lipoproteins observed in ascorbate treated rats, is accompanied by a marked modification of the apolipoprotein pattern of all lipoprotein classes studied, with an increase of apo E content in VLDL-IDL and LDL fractions (135 and 44% respectively), and a decrease of C (37%), AI (70%) and B (37.5%) apoproteins in VLDL-IDL and of apo C (36%) in LDL. On the contrary, in HDL fraction ascorbate induces an increase of C apoproteins (26%) and a decrease of E and B apoproteins (47% and 71% respectively). The data reported clearly show that in hypercholesterolemic rats the lipid lowering effect of ascorbate administration, is accompanied by a marked modification of the apoprotein pattern of all lipoprotein classes studied.