Modulation of the N170 adaptation profile by higher level factors.

Event-related potentials provide strong evidence for a face-specific process that peaks at about 170 ms following stimulus onset--the N170 effect. The N170 has been shown to be sensitive to adaptation, reflected in an amplitude reduction by repeated face presentation, which is usually considered to be driven by bottom-up processes. Here we investigated whether the N170 adaptation profile can be modulated by top-down factors aiming at holistic or feature-based processing. Adaptor stimuli were Mooney faces, isolated facial features (eyes or mouths), or houses. Target faces required either a gender decision (holistic task), or a decision on a facial feature (detail task). We observed an intricate crossover interaction pattern, reflected in opposite effects on adaptation to Mooney faces and eyes as compared to mouth conditions. These findings provide evidence that adaptation effects can be modulated by top-down processes.

Age-related task sensitivity of frontal EEG entropy during encoding predicts retrieval.

Age-related declines in memory may be due in part to changes in the complexity of neural activity in the aging brain. Electrophysiological entropy provides an accessible measure of the complexity of ongoing neural activity. In the current study, we calculated the permutation entropy of the electroencephalogram (EEG) during encoding of relevant (to be learned) and irrelevant (to be ignored) stimuli by younger adults, older adults, and older cognitively declined adults. EEG entropy was differentially sensitive to task requirements across groups, with younger and older controls exhibiting greater control of encoding-related activity than older declined participants. Task sensitivity of frontal EEG during encoding predicted later retrieval, in line with previous evidence that cognitive decline is associated with reduced ability to self-initiate encoding-related processes.

Order Patterns Networks (ORPAN)-a method to estimate time-evolving functional connectivity from multivariate time series.

Complex networks provide an excellent framework for studying the function of the human brain activity. Yet estimating functional networks from measured signals is not trivial, especially if the data is non-stationary and noisy as it is often the case with physiological recordings. In this article we propose a method that uses the local rank structure of the data to define functional links in terms of identical rank structures. The method yields temporal sequences of networks which permits to trace the evolution of the functional connectivity during the time course of the observation. We demonstrate the potentials of this approach with model data as well as with experimental data from an electrophysiological study on language processing.

Functional network analysis reveals differences in the semantic priming task.

The recent years have seen the emergence of graph theoretical analysis of complex, functional brain networks estimated from neurophysiological measurements. The research has mainly focused on the graph characterization of the resting-state/default network, and its potential for clinical application. Functional resting-state networks usually display the characteristics of small-world networks and their statistical properties have been observed to change due to pathological conditions or aging. In the present paper we move forward in the application of graph theoretical tools in functional connectivity by investigating high-level cognitive processing in healthy adults, in a manner similar to that used in psychological research in the framework of event-related potentials (ERPs). More specifically we aim at investigating how graph theoretical approaches can help to discover systematic and task-dependent differences in high-level cognitive processes such as language perception. We will show that such an approach is feasible and that the results coincide well with the findings from neuroimaging studies.

Brain signal analysis based on recurrences.

The EEG is one of the most commonly used tools in brain research. Though of high relevance in research, the data obtained is very noisy and nonstationary. In the present article we investigate the applicability of a nonlinear data analysis method, the recurrence quantification analysis (RQA), to such data. The method solely rests on the natural property of recurrence which is a phenomenon inherent to complex systems, such as the brain. We show that this method is indeed suitable for the analysis of EEG data and that it might improve contemporary EEG analysis.

Order patterns recurrence plots in the analysis of ERP data.

Recurrence quantification analysis (RQA) is an established tool for data analysis in various behavioural sciences. In this article we present a refined notion of RQA based on order patterns. The use of order patterns is commonplace in time series analysis. Exploiting this concept in combination with recurrence plots (RP) and their quantification (RQA) allows for advances in contemporary EEG research, specifically in the analysis of event related potentials (ERP), as the method is known to be robust against non-stationary data. The use of order patterns recurrence plots (OPRPs) on EEG data recorded during a language processing experiment exemplifies the potentials of the method. We could show that the application of RQA to ERP data allows for a considerable reduction of the number of trials required in ERP research while still maintaining statistical validity.