1,721,056 research outputs found
Correlati elettrofisiologici el processa mento della relazione sguardo-oggetto nel primo anno di vita
No full textNeural connettivity in hand sensorimotor area: an evaluation by evoked fields morphology
No full textBispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals
Full text linkBrain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral pairwise interacting source analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA makes it possible to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e., volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the pairwise interacting source analysis (PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci
Optimization of an Independent Component Analysis approach for artifact identification and removal in MEG signals
No full textImpact of the reference choice on scalp EEG connectivity estimation
Full text linkObjective. Several scalp EEG functional connectivity studies, mostly clinical, seem to overlook the reference electrode impact. The subsequent interpretation of brain connectivity is thus often biased by the choice of a non-neutral reference. This study aims at systematically investigating these effects. Approach. As EEG reference, we examined the vertex electrode (Cz), the digitally linked mastoids (DLM), the average reference (AVE), and the reference electrode standardization technique (REST). As a connectivity metric, we used the imaginary part of the coherency. We tested simulated and real data (eyes-open resting state) by evaluating the influence of electrode density, the effect of head model accuracy in the REST transformation, and the impact on the characterization of the topology of functional networks from graph analysis. Main results. Simulations demonstrated that REST significantly reduced the distortion of connectivity patterns when compared to AVE, Cz, and DLM references. Moreover, the availability of high-density EEG systems and an accurate knowledge of the head model are crucial elements to improve REST performance, with the individual realistic head model being preferable to the standard realistic head model. For real data, a systematic change of the spatial pattern of functional connectivity depending on the chosen reference was also observed. The distortion of connectivity patterns was larger for the Cz reference, and progressively decreased when using the DLM, the AVE, and the REST. Strikingly, we also showed that network attributes derived from graph analysis, i.e. node degree and local efficiency, are significantly influenced by the EEG reference choice. Significance. Overall, this study highlights that significant differences arise in scalp EEG functional connectivity and graph network properties, in dependence on the chosen reference. We hope that our study will convey the message that caution should be used when interpreting and comparing results obtained from different laboratories using different reference schemes. © 2016 IOP Publishing Ltd
An ICA approach to detect functionally different intra-regional neuronal signals in MEG data
No full textParsing the Flanker task to reveal behavioral and oscillatory correlates of unattended conflict interference
Full text linkStimulus-Response conflict is generated by an overlap between stimulus and response dimensions, but the intrinsic nature of this interaction is not yet deeply clarified. In this study, using a modified Eriksen flanker task, we have investigated how flankers have to be incongruent to target in order to produce an interference and whether and how this interference interacts with the one produced by Stimulus features overlap. To these aims, an Eriksen-like task employing oriented handsarrows has been designed to distinguish between two types of Stimulus-Response (S-R) interferences: one derived by a short-term association and one based on automatic processes. Stimulus-Stimulus (S-S) conflict has been also included in the same factorial design. Behavioral, Event Related Potential (ERP) and oscillatory activity data have been measured. Results revealed distinct S-S and automatic S-R effects on behavioral performance. ERP and Theta band power modulation results suggested an early frontal S-S conflict processing followed by a posterior simultaneous S-S and automatic S-R conflict processing. These findings provide evidence that, in presence of different conflicts, the sequence of stimulus identification and response selection could not move forward in a linear serial direction, but it may involve further effort, mirrored in posterior late components and response time prolongation
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