Notes

[Statics and dynamics involving psychopathies from the concept of Ε. Κraepelin].
Inhibitory control processes are indispensable for goal-directed behavior. On a neurophysiological level, theta band power has been suggested to be important during inhibitory control as well as proactive control processes. Here we ask whether theta activity in the pre-trial/between-trial time period, reflects proactive control that correlates with theta activity in the upcoming trial. Theoretical considerations also suggest that such a correlation is modulated by the demands on inhibition. To investigate these questions, we conducted an EEG study using a Go/Nogo task in which demands on inhibitory control are varied. We used different EEG beamforming approaches to focus the analysis on the functional neuroanatomical level. We show that theta band activity in the ventromedial prefrontal cortex (vmPFC, BA10) is associated with the proactive control in the pre-trial interval and correlates with theta-related processes in the right inferior frontal gyrus (rIFG, BA45) during response inhibition. In addition, we show that demands on inhibitory control modulate the inter-relation between proactive vmPFC and inhibitory control-related rIFG-theta activity. Such effects were not observed for beta frequency activity. The study is the first to emphasize the relevance of pre-trial (proactive) vmPFC theta-band activity during inhibitory control in humans. It is shown that pre-trial activity, which is often neglected in EEG research, provides valuable information about the neuronal dynamics of cognitive control.Functional connectivity (FC), thought to provide a window into neural communication, has become a core focus in the study of brain function and cognition. However, there is no consensus on how to conceptualize large-scale FC in electrophysiology. Phase coupling (PhC), defined as coupling between the phases of two signals, reflects the synchronization of rhythmic oscillation cycles. Conversely, amplitude coupling (AmpC), defined as coupling between the envelopes of two signals, reflects correlation of activation amplitude. Despite quantifying different electrophysiological properties, the relationship between PhC and AmpC remains largely unknown. We assessed spatial and temporal correspondence between PhC and AmpC over 5 canonical frequency bands during a cue-based motor task using electrocorticography (ECoG) in 18 patients (8 females) undergoing presurgical monitoring. Significant correspondence between the spatial pattern of PhC and AmpC was detected during stimulus processing across all subjects and frequency bands (R ≈ 0.50 for theta, decreasing with increasing frequency). The cross-measure spatial correlation vanished almost entirely when accounting for the portion of FC equally present during pre- and post-stimulus intervals, suggesting that the spatial correlations reflect intrinsic FC independent of stimulus processing. Stimulus-related processing modulated both PhC and AmpC, however in a spatially independent manner. Examining the linear temporal correlation, we found no evidence for linear dependence between PhC and AmpC. Supporting the robustness of our findings, results extended to a verb generation task in a second ECoG dataset of 6 subjects. We conclude that PhC and AmpC reflect intrinsic FC similarly across space, but exhibit divergent stimulus-related FC changes over space and time.Deep brain stimulation (DBS) can be a very efficient treatment option for movement disorders and psychiatric diseases. To better understand DBS mechanisms, brain activity can be recorded using magnetoencephalography (MEG) with the stimulator turned on. However, DBS produces large artefacts compromising MEG data quality due to both the applied current and the movement of wires connecting the stimulator with the electrode. Defactinib To filter out these artefacts, several methods to suppress the DBS artefact have been proposed in the literature. A comparative study evaluating each method's effectiveness, however, is missing so far. In this study, we evaluate the performance of four artefact rejection methods on MEG data from phantom recordings with DBS acquired with an Elekta Neuromag and a CTF system (i) Hampel-filter, (ii) spectral signal space projection (S3P), (iii) independent component analysis with mutual information (ICA-MI), and (iv) temporal signal space separation (tSSS). In the sensor space, the largest increase in signal-to-noise (SNR) ratio was achieved by ICA-MI, while the best correspondence in terms of source activations was obtained by tSSS. LCMV beamforming alone was not sufficient to suppress the DBS-induced artefacts.Real-world environments are extremely rich in visual information. At any given moment in time, only a fraction of this information is available to the eyes and the brain, rendering naturalistic vision a collection of incomplete snapshots. Previous research suggests that in order to successfully contextualize this fragmented information, the visual system sorts inputs according to spatial schemata, that is knowledge about the typical composition of the visual world. Here, we used a large set of 840 different natural scene fragments to investigate whether this sorting mechanism can operate across the diverse visual environments encountered during real-world vision. We recorded brain activity using electroencephalography (EEG) while participants viewed incomplete scene fragments at fixation. Using representational similarity analysis on the EEG data, we tracked the fragments' cortical representations across time. We found that the fragments' typical vertical location within the environment (top or bottom) predicted their cortical representations, indexing a sorting of information according to spatial schemata. The fragments' cortical representations were most strongly organized by their vertical location at around 200 ms after image onset, suggesting rapid perceptual sorting of information according to spatial schemata. In control analyses, we show that this sorting is flexible with respect to visual features it is neither explained by commonalities between visually similar indoor and outdoor scenes, nor by the feature organization emerging from a deep neural network trained on scene categorization. Demonstrating such a flexible sorting across a wide range of visually diverse scenes suggests a contextualization mechanism suitable for complex and variable real-world environments.
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