Rafal Kus

Determination of EEG activity propagation: pair-wise versus multichannel estimate

Performance of different estimators describing propagation of electroencephalogram (EEG) activity, namely: Granger causality, directed transfer function (DTF), direct DTF (dDTF), short-time DTF (SDTF), bivariate coherence, and partial directed coherence are compared by means of simulations and on the examples of experimental signals. In particular, the differences between pair-wise and multichannel estimates are studied. The results show unequivocally that in most cases, the pair-wise estimates are incorrect and a complete set of signals involved in a given process has to be used to obtain the correct pattern of EEG flows. Different performance of multivariate estimators of propagation depending on their normalization is discussed. Advantages of multivariate autoregressive model are pointed out.

Transmission of Brain Activity During Cognitive Task

The transmission of brain activity during constant attention test was estimated by means of the short-time directed transfer function (SDTF). SDTF is an estimator based on a multivariate autoregressive model. It determines the propagation as a function of time and frequency. For nine healthy subjects the transmission of EEG activity was determined for target and non-target conditions corresponding to pressing of a switch in case of appearance of two identical images or withholding the reaction in case of different images. The involvement of prefrontal and frontal cortex manifested by the propagation from these structures was observed, especially in the early stages of the task. For the target condition there was a burst of propagation from C3 after pressing the switch, which can be interpreted as beta rebound upon completion of motor action. In case of non-target condition the propagation from F8 or Fz to C3 was observed, which can be connected with the active inhibition of motor cortex by right inferior frontal cortex or presupplementary motor area.

Single-Trial Evoked Brain Responses Modeled by Multivariate Matching Pursuit

We present a new approach to the analysis of brain evoked electromagnetic potentials and fields. Multivariate version of the matching pursuit algorithm (MMP) performs an iterative, exhaustive search for waveforms, which optimally fit to signal structures, persistent in all the responses (trials) with the same time of occurrence, frequency, phase, and time width, but varying amplitude. The search is performed in a highly redundant time--frequency dictionary of Gabor functions, i.e., sines modulated by Gaussians. We present the feasibility of such a single-trial MMP analysis of the auditory M100 response, using an illustrative dataset acquired in a magnetoencephalographic (MEG) measurement with auditory stimulation with sinusoidal 1-kHz tones. We find that the morphology of the M100 estimate obtained from simple averaging of single trials can be very well explained by the average reconstruction with a few Gabor functions that parametrize those single trials. The M100 peak amplitude of single-trial reconstructions is observed to decrease with repetitions, which indicates habituation to the stimulus. This finding suggests that certain waveforms fitted by MMP could possibly be related to physiologically distinct components of evoked magnetic fields, which would allow tracing their dynamics on a single-trial level.

Nonlinear Origin of SSVEP Spectra—A Combined Experimental and Modeling Study

Steady state visual evoked potentials (SSVEPs) are steady state oscillatory potentials elicited in the electroencephalogram (EEG) by flicker stimulation. The frequency of these responses maches the frequency of the stimulation and of its harmonics and subharmonics. In this study, we investigated the origin of the harmonic and subharmonic components of SSVEPs, which are not well understood. We applied both sine and square wave visual stimulation at 5 and 15 Hz to human subjects and analyzed the properties of the fundamental responses and harmonically related components. In order to interpret the results, we used the well-established neural mass model that consists of interacting populations of excitatory and inhibitory cortical neurons. In our study, this model provided a simple explanation for the origin of SSVEP spectra, and showed that their harmonic and subharmonic components are a natural consequence of the nonlinear properties of neuronal populations and the resonant properties of the modeled network. The model also predicted multiples of subharmonic responses, which were subsequently confirmed using experimental data.

Propagation of Brain Electrical Activity During Real and Imagined Motor Task by Directed Transfer Function

Short-time directed transfer function, defined in the framework of multivariate autoregressive model, makes possible determination of the directions of propagation of EEG activity as a function of time and frequency. The EEG propagation in beta and gamma bands was estimated for the same subjects in imagined and real hand movement task. The concise patterns of propagations bearing some similarities in both tasks, but differing mainly in respect of time course of communication between structures, was found

Multivariate autoregressive model for a study of phylogenetic diversity.

We present a computationally effective model to parameterize DNA sequences in a way describing comprehensively its auto and cross-correlation structure. The approach is based on four-channel Multivariate Autoregressive Model (MVAR). The model was applied to a study of genes from the globin family for 6 vertebrate species. First, the sequences were coded as four signals (corresponding to the nucleotides), which were fitted to a four-channel MVAR. From the correlation matrices the vectors of model coefficients were calculated as functions of the nucleotide distance. The between-chromosomes and inter-species differences were best distinguished in the cross-coefficients binding different nucleotide sequences. For clustering purposes different metrics were tested and then two clustering procedures (Nearest Neighbor and UPGMA) were applied. The clustering trees and consensus trees were constructed for exons, introns and whole genes. The results were in agreement with the known dependencies between the chromosomes of the globin family. The orthological genes for different species were grouped together. Inside these groups the phylogenetically close organisms were localized in proximity.

The Time-Varying Causal Coupling in Brain and Organization of Its Networks

For investigation of time-varying brain networks an approach based on estimation of causal coupling by means of multivariate method was applied. Two cognitive experiments: Constant Attention Test and Working Memory task are considered. Time varying version of a multivariate estimator—Directed Transfer Function was used for calculating dynamically changing patterns of transmission during the tasks. Well-defined centers of activity congruent with imaging, anatomical and electrophysiological evidence were found. These centers exchanged the information only during short epochs. The strengths of coupling inside the tightly connected modules and between them was found by means of assortative mixing. The results point out to the well determined, far from randomness structure of brain networks in cognitive tasks. Very dense and disorganized structure of networks reported in literature may be explained by the presence of spurious connections produced by bi-variate measures of connectivity and further enhanced by giving all connections equal weights.

Information infrastructure for cooperative research in neuroscience

The paper describes a framework for efficient sharing of knowledge between research groups, which have been working for several years without flaws. The obstacles in cooperation are connected primarily with the lack of platforms for effective exchange of experimental data, models, and algorithms. The solution to these problems is proposed by construction of the platform (EEG.pl) with the semantic aware search scheme between portals. The above approach implanted in the international cooperative projects like NEUROMATH may bring the significant progress in designing efficient methods for neuroscience research.

Transmission of brain activity in cognitive and motor tasks

The Short-time Directed Transfer Function was used for estimation of dynamical patterns of brain activity propagation. The SDTF is based on the multivariate autoregressive model, where all channels of the process are considered simultaneously. Time-frequency patterns of EEG propagation were found for the task of finger movement and its imagination and for the Continuous Attention Test. The results supported the neurophysiological hypotheses concerning information processing in brain and in particular the theory of active inhibition.

Electrocorticographical Transfer of Information During Motor Task

Electrocorticograms of presurgical epileptic patients were analyzed by means of a multichannel autoregressive model. The causal relations between channels were found by means of short-time Directed Transfer Function. The topographical patterns of transmission of information were found for beta and gamma bands