Search

Abstract: Phase-synchronization is a manifestation of interaction between neuronal groups measurable from LFP, EEG or MEG signals, however, volume conduction can cause the coherence and the phase locking value to spuriously increase. It has been shown that the imaginary component of the coherency (ImC) cannot be spuriously increased by volume-conduction of independent sources. Recently, it was proposed that the phase lag index (PLI), which estimates to what extent the phase leads and lags between signals from two sensors are nonequiprobable, improves on the ImC. Compared to ImC, PLI has the advantage of being less influenced by phase delays. However, sensitivity to volume-conduction and noise, and capacity to detect changes in phase-synchronization, is hindered by the discontinuity of the PLI, as small perturbations turn phase lags into leads and vice versa. To solve this problem, we introduce a related index, namely the weighted phase lag index (WPLI). Differently from PLI, in WPLI the contribution of the observed phase leads and lags is weighted by the magnitude of the imaginary component of the cross-spectrum. We demonstrate two advantages of the WPLI over the PLI, in terms of reduced sensitivity to additional, uncorrelated noise sources and increased statistical power to detect changes in phase-synchronization. Another factor that can affect phase-synchronization indices is sample-size bias. We show that, when directly estimated, both PLI and the magnitude of the ImC have typically positively biased estimators. To solve this problem, we develop an unbiased estimator of the squared PLI, and a debiased estimator of the squared WPLI. [Copyright &y& Elsevier]

Abstract: PET and fMRI experiments have previously shown that several brain regions in the frontal and parietal lobe are involved in working memory maintenance. MEG and EEG experiments have shown parametric increases with load for oscillatory activity in posterior alpha and frontal theta power. In the current study we investigated whether the areas found with fMRI can be associated with these alpha and theta effects by measuring simultaneous EEG and fMRI during a modified Sternberg task This allowed us to correlate EEG at the single trial level with the fMRI BOLD signal by forming a regressor based on single trial alpha and theta power estimates. We observed a right posterior, parametric alpha power increase, which was functionally related to decreases in BOLD in the primary visual cortex and in the posterior part of the right middle temporal gyrus. We relate this finding to the inhibition of neuronal activity that may interfere with WM maintenance. An observed parametric increase in frontal theta power was correlated to a decrease in BOLD in regions that together form the default mode network. We did not observe correlations between oscillatory EEG phenomena and BOLD in the traditional WM areas. In conclusion, the study shows that simultaneous EEG–fMRI recordings can be successfully used to identify the emergence of functional networks in the brain during the execution of a cognitive task. [Copyright &y& Elsevier]

Abstract: An important step in perceptual processing is the integration of information from different sensory modalities into a coherent percept. It has been suggested that such crossmodal binding might be achieved by transient synchronization of neurons from different modalities in the gamma-frequency range (>30 Hz). Here we employed a crossmodal priming paradigm, modulating the semantic congruency between visual–auditory natural object stimulus pairs, during the recording of the high density electroencephalogram (EEG). Subjects performed a semantic categorization task. Analysis of the behavioral data showed a crossmodal priming effect (facilitated auditory object recognition) in response to semantically congruent stimuli. Differences in event-related potentials (ERP) were found between 250 and 350 ms, which were localized to left middle temporal gyrus (BA 21) using a distributed linear source model. Early gamma-band activity (40–50 Hz) was increased between 120 ms and 180 ms following auditory stimulus onset for semantically congruent stimulus pairs. Source reconstruction for this gamma-band response revealed a maximal increase in left middle temporal gyrus (BA 21), an area known to be related to the processing of both complex auditory stimuli and multisensory processing. The data support the hypothesis that oscillatory activity in the gamma-band reflects crossmodal semantic-matching processes in multisensory convergence sites. [Copyright &y& Elsevier]

• Concise overview and explanation of beamformers for M/EEG data analysis. • Practical considerations and best practices for beamforming analyses. • Unification of terminology across popular open source software packages. • Comparison of implementations and user interfaces between software packages. Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging. [ABSTRACT FROM AUTHOR]

Abstract: In October 2009, a group of neurologists, neurosurgeons, computational neuroscientists, and engineers congregated to present novel developments transforming human electrocorticography (ECoG) beyond its established relevance in clinical epileptology. The contents of the proceedings advanced the role of ECoG in seizure detection and prediction, neurobehavioral research, functional mapping, and brain–computer interface technology. The meeting established the foundation for future work on the methodology and application of surface brain recordings. [ABSTRACT FROM AUTHOR]