Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction.

Keeping the lid on infection spread: From February to April 2020, many countries introduced variations on social distancing measures to slow the ravages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Publicly available data show that Germany has been particularly successful in minimizing death rates. Dehning et al. quantified three governmental interventions introduced to control the outbreak. The authors predicted that the third governmental intervention—a strict contact ban since 22 March—switched incidence from growth to decay. They emphasize that relaxation of controls must be done carefully, not only because there is a 2-week lag between a measure being enacted and the effect on case reports but also because the three measures used in Germany only just kept virus spread below the growth threshold. Science , this issue p. eabb9789

Magnetoencephalographic (MEG) recordings are a rich source of information about the neural dynamics underlying cognitive processes in the brain, with excellent temporal and good spatial resolution. In recent years there have been considerable advances in MEG hardware developments and methods. Sophisticated analysis techniques are now routinely applied and continuously improved, leading to fascinating insights into the intricate dynamics of neural processes. However, the rapidly increasing level of complexity of the different steps in a MEG study make it difficult for novices, and sometimes even for experts, to stay aware of possible limitations and caveats. Furthermore, the complexity of MEG data acquisition and data analysis requires special attention when describing MEG studies in publications, in order to facilitate interpretation and reproduction of the results. This manuscript aims at making recommendations for a number of important data acquisition and data analysis steps and suggests details that should be specified in manuscripts reporting MEG studies. These recommendations will hopefully serve as guidelines that help to strengthen the position of the MEG research community within the field of neuroscience, and may foster discussion in order to further enhance the quality and impact of MEG research.

Constraints from functional magnetic resonance imaging (fMRI) were used to identify the sources of the visual P300 event-related potential (ERP). Healthy subjects performed a visual three-stimulus oddball paradigm with a difficult discrimination task while fMRI and high-density ERP data were acquired in separate sessions. This paradigm allowed us to differentiate the P3b component of the P300, which has been implicated in the detection of rare events in general (target and distractor), from the P3a component, which is mainly evoked by distractor events. The fMRI-constrained source model explained >99% of the variance of the scalp ERP for both components. The P3b was mainly produced by parietal and inferior temporal areas, whereas frontal areas and the insula contributed mainly to the P3a. This source model reveals that both higher visual and supramodal association areas contribute to the visual P3b and that the P3a has a strong frontal contribution, which is compatible with its more anterior distribution on the scalp. The results point to the involvement of distinct attentional subsystems in target and distractor processing.

Previous studies in electrophysiology have provided consistent evidence for a relationship between neural oscillations in different frequency bands and the maintenance of information in working memory (WM). While the amplitude and cross-frequency coupling of neural oscillations have been shown to be modulated by the number of items retained during WM, interareal phase synchronization has been associated with the integration of distributed activity during WM maintenance. Together, these findings provided important insights into the oscillatory dynamics of cortical networks during WM. However, little is known about the cortical regions and frequencies that underlie the specific maintenance of behaviorally relevant information in WM. In the current study, we addressed this question with magnetoencephalography and a delayed match-to-sample task involving distractors in 25 human participants. Using spectral analysis and beamforming, we found a WM load-related increase in the gamma band (60–80 Hz) that was localized to the right intraparietal lobule and left Brodmann area 9 (BA9). WM-load related changes were also detected at alpha frequencies (10–14 Hz) in Brodmann area 6, but did not covary with the number of relevant WM-items. Finally, we decoded gamma-band source activity with a linear discriminant analysis and found that gamma-band activity in left BA9 predicted the number of target items maintained in WM. While the present data show that WM maintenance involves activity in the alpha and gamma band, our results highlight the specific contribution of gamma band delay activity in prefrontal cortex for the maintenance of behaviorally relevant items.