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

Granger causality for circular variables

In this letter we discuss use of Granger causality to the analyze systems of coupled circular variables, by modifying a recently proposed method for multivariate analysis of causality. We show the application of the proposed approach on several Kuramoto systems, in particular one living on networks built by preferential attachment and a model for the transition from deeply to lightly anaesthetized states. Granger causalities describe the flow of information among variables.Comment: 4 pages, 5 figure

Musical Ratios in Sounds from the Human Cochlea

The physiological roots of music perception are a matter of long-lasting debate. Recently light on this problem has been shed by the study of otoacoustic emissions (OAEs), which are weak sounds generated by the inner ear following acoustic stimulation and, sometimes, even spontaneously. In the present study, a high-resolution time–frequency method called matching pursuit was applied to the OAEs recorded from the ears of 45 normal volunteers so that the component frequencies, amplitudes, latencies, and time-spans could be accurately determined. The method allowed us to find that, for each ear, the OAEs consisted of characteristic frequency patterns that we call resonant modes. Here we demonstrate that, on average, the frequency ratios of the resonant modes from all the cochleas studied possessed small integer ratios. The ratios are the same as those found by Pythagoras as being most musically pleasant and which form the basis of the Just tuning system. The statistical significance of the results was verified against a random distribution of ratios. As an explanatory model, there are attractive features in a recent theory that represents the cochlea as a surface acoustic wave resonator; in this situation the spacing between the rows of hearing receptors can create resonant cavities of defined lengths. By adjusting the geometry and the lengths of the resonant cavities, it is possible to generate the preferred frequency ratios we have found here. We conclude that musical perception might be related to specific geometrical and physiological properties of the cochlea

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

Review of the methods of determination of directed connectivity from multichannel data

The methods applied for estimation of functional connectivity from multichannel data are described with special emphasis on the estimators of directedness such as directed transfer function (DTF) and partial directed coherence. These estimators based on multivariate autoregressive model are free of pitfalls connected with application of bivariate measures. The examples of applications illustrating the performance of the methods are given. Time-varying estimators of directedness: short-time DTF and adaptive methods are presented

International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies

In 1999, the International Federation of Clinical Neurophysiology (IFCN) published “IFCN Guidelines for topographic and frequency analysis of EEGs and EPs” (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of “synchronization” features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of “connectivity” features based on frequency analysis (linear and nonlinear measures); (6) extraction of “topographic” features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising “synchronization” and “connectivity” features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses

From wavelets to adaptive approximations: time-frequency parametrization of EEG

This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG). It covers in details two major steps: introduction of wavelets and adaptive approximations. Presented studies include time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. Based upon these results we conclude that the matching pursuit algorithm provides a unified parametrization of EEG, applicable in a variety of experimental and clinical setups. This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals