Averaging auditory evoked magnetoencephalographic and electroencephalographic responses: a critical discussion

Time-series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattended listening, and frequency-modulated tones classification

BACKGROUND

The nonstationarity of EEG/MEG signals is important for understanding the functioning of the human brain. From our previous research we know that short, 250-500-ms MEG signals are variance-nonstationary. The covariance of a stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time.

NEW METHOD

We analyse data from 148-channel MEG, which represent rest state, unattended listening, and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain and for the dominant frequencies of 8-12 Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity, and gaussianity, and propose ARMA-modelling for their description.

RESULTS

The analysed time series have weak-stationarity properties independently of the functional state of the brain and channel localization. Only a small percentage of them, mostly related to the cognitive task, reveal nonstationarity. The obtained mathematical models show that the spectral density of the analysed signals depends on only two to three previous trials.

COMPARISON WITH EXISTING METHODS

The presented method has limitations related to FFT resolution and univariate models, but it is computationally simple and allows obtaining a low-complex stochastic model of the EEG/MEG spectrum variability.

CONCLUSIONS

Although physiological short-time MEG signals are in principle nonstationary in time, their power spectrum at the dominant (alpha) frequencies varies as a weakly stationary process. The proposed methodology has possible applications in prediction of EEG/MEG spectral properties in theoretical and clinical neuroscience.

Why do humans have unique auditory event-related fields? Evidence from computational modeling and MEG experiments

Auditory event-related fields (ERFs) measured with magnetoencephalography (MEG) are useful for studying the neuronal underpinnings of auditory cognition in human cortex. They have a highly subject-specific morphology, albeit certain characteristic deflections (e.g., P1m, N1m, and P2m) can be identified in most subjects. Here, we explore the reason for this subject-specificity through a combination of MEG measurements and computational modeling of auditory cortex. We test whether ERF subject-specificity can predominantly be explained in terms of each subject having an individual cortical gross anatomy, which modulates the MEG signal, or whether individual cortical dynamics is also at play. To our knowledge, this is the first time that tools to address this question are being presented. The effects of anatomical and dynamical variation on the MEG signal is simulated in a model describing the core-belt-parabelt structure of the auditory cortex, and with the dynamics based on the leaky-integrator neuron model. The experimental and simulated ERFs are characterized in terms of the N1m amplitude, latency, and width. Also, we examine the waveform grand-averaged across subjects, and the standard deviation of this grand average. The results show that the intersubject variability of the ERF arises out of both the anatomy and the dynamics of auditory cortex being specific to each subject. Moreover, our results suggest that the latency variation of the N1m is largely related to subject-specific dynamics. The findings are discussed in terms of how learning, plasticity, and sound detection are reflected in the auditory ERFs. The notion of the grand-averaged ERF is critically evaluated.

Symmetric patterns with different luminance polarity (anti-symmetry) generate an automatic response in extrastriate cortex

People can quickly detect bilateral reflection in an image. This is true when elements of the same luminance are matched on either side of the axis (symmetry) and when they have opposite luminance polarity (anti-symmetry). Using electroencephalography, we measured the well-established sustained posterior negativity (SPN) response to symmetry and anti-symmetry. In one task, participants judged the presence or absence of regularity (Regularity Discrimination Task). In another, they judged the presence or absence of rare colored oddball trials (Colored Oddball Task). Previous work has concluded that anti-symmetry is only detected indirectly, through serial visual search of element locations. This selective attention account predicts that the anti-symmetry SPN should be abolished in the Colored Oddball Task because there is no need to search for anti-symmetry. However, this prediction was not confirmed: The symmetry and anti-symmetry SPN waves were not modulated by task. We conclude that at least some forms of anti-symmetry can be extracted from the image automatically, in much the same way as symmetry. This is an important consideration for models of symmetry perception, which must be flexible enough to accommodate opposite luminance polarity, while also accounting for the fact anti-symmetry is often perceptually weaker than symmetry.

Explaining event-related fields by a mechanistic model encapsulating the anatomical structure of auditory cortex

Event-related fields of the magnetoencephalogram are triggered by sensory stimuli and appear as a series of waves extending hundreds of milliseconds after stimulus onset. They reflect the processing of the stimulus in cortex and have a highly subject-specific morphology. However, we still have an incomplete picture of how event-related fields are generated, what the various waves signify, and why they are so subject-specific. Here, we focus on this problem through the lens of a computational model which describes auditory cortex in terms of interconnected cortical columns as part of hierarchically placed fields of the core, belt, and parabelt areas. We develop an analytical approach arriving at solutions to the system dynamics in terms of normal modes: damped harmonic oscillators emerging out of the coupled excitation and inhibition in the system. Each normal mode is a global feature which depends on the anatomical structure of the entire auditory cortex. Further, normal modes are fundamental dynamical building blocks, in that the activity of each cortical column represents a combination of all normal modes. This approach allows us to replicate a typical auditory event-related response as a weighted sum of the single-column activities. Our work offers an alternative to the view that the event-related field arises out of spatially discrete, local generators. Rather, there is only a single generator process distributed over the entire network of the auditory cortex. We present predictions for testing to what degree subject-specificity is due to cross-subject variations in dynamical parameters rather than in the cortical surface morphology.

Bis(4,4'-dimethyl-2,2'-bipyridine)oxidovanadium(IV) Sulfate Dehydrate: Potential Candidate for Controlling Lipid Metabolism?

Vanadium is a trace element mainly connected with regulation of insulin metabolism which is particularly important in diabetes. In recent years, organic complexes of vanadium seem to be more interesting than inorganic salts. Nevertheless, the effect of vanadium on lipid metabolism is still a problematic issue; therefore, the main purpose of this study was to investigate the effect of 3 organic complexes of vanadium such as sodium (2,2′-bipyridine)oxidobisperoxovanadate(V) octahydrate, bis(2,2′-bipyridine)oxidovanadium(IV) sulfate dehydrate, and bis(4,4′-dimethyl-2,2′-bipyridine)oxidovanadium(IV) sulfate dihydrate in conjunction with high-fat as well as control diet in nondiabetes model on the following lipid parameters: total cholesterol, triglycerides, and high density lipoprotein as well as activity of paraoxonase 1. All of these parameters were determined in plasma of Wistar rats. The most significant effect was observed in case of bis(4,4′-dimethyl-2,2′ bipyridine)oxidovanadium(IV) sulfate dehydrate in rats fed with high-fat diet. Based on our research, bis(4,4′-dimethyl-2,2′-bipyridine)oxidovanadium(IV) sulfate dihydrate should be the aim of further research and perhaps it will be an important factor in the regulation of lipid metabolism.

Persistent neural activity in auditory cortex is related to auditory working memory in humans and nonhuman primates

Working memory is the cognitive capacity of short-term storage of information for goal-directed behaviors. Where and how this capacity is implemented in the brain are unresolved questions. We show that auditory cortex stores information by persistent changes of neural activity. We separated activity related to working memory from activity related to other mental processes by having humans and monkeys perform different tasks with varying working memory demands on the same sound sequences. Working memory was reflected in the spiking activity of individual neurons in auditory cortex and in the activity of neuronal populations, that is, in local field potentials and magnetic fields. Our results provide direct support for the idea that temporary storage of information recruits the same brain areas that also process the information. Because similar activity was observed in the two species, the cellular bases of some auditory working memory processes in humans can be studied in monkeys.

Detecting neuromagnetic synchrony in the presence of noise

BACKGROUND

Synchrony between neuroelectric oscillations in distant brain areas is currently used as an indicator of functional connectivity between the involved neural substrates. Coherence measures, which quantify synchrony, are affected by concurrent brain activities, commonly subsumed as noise.

NEW METHOD

Using Monte-Carlo simulation, we analysed the properties of circular statistics and how those are affected by noise. We considered three different models of neuroelectric signal generation, which are an additive model, phase-reset, and reciprocal phase-interaction. Using the receiver-operating characteristic method, we compared the performances of currently implemented algorithms for coherence detection such as phase-coherence or phase-locking factor, magnitude-squared coherence, and phase-lagging index, all based on circular statistics, and a more general approach to synchrony, using measures of mutual information. We compared inter-trial coherence as a method for signal detection with coherence between multiple sources as measure of source interaction and connectivity.

RESULTS

Charts of performance characteristics showed that the choice of methods depend on the underlying signal generation model. Detection of coherence requires in general a higher signal-to-noise ratio than detection of the signal itself, and again, the difference in performance depends strongly on the underlying model of signal generation.

COMPARISON WITH EXISTING METHODS

Previous comparisons of the performances of different algorithms for signal detection and coherence have not considered systematically the underlying neural generation mechanisms.

CONCLUSION

Detection of coherence generated by additive signals or a phase-reset requires largely higher signal-to-noise ratio compared to signal detection. Only in case of true phase interaction, signal detection and coherence measures are similarly sensitive.