The P3m reflects resource allocation in cognitive domains, and its amplitude can be influenced by task parameters such as stimulus probability or task difficulty. Its sources are located in multiple brain areas, suggesting a widespread network generating the P3m, but the sources responsible for the task-difficulty-dependent modulation are not well understood. Identifying these sources could facilitate future research and interventions. The present MEG study uses an oddball-like visual discrimination task with varying levels of difficulty to 1) demonstrate decreased P3m amplitudes with increasing task difficulty and 2) estimate the sources of task-difficulty-dependent P3m modulation. Furthermore, we analyzed the effects of increased task difficulty on behavioral outcomes. We observed decreased P3m amplitudes with increasing task difficulty and identified the sources of this modulation primarily in the centro-parietal regions of both hemispheres. Increased task difficulty also affected behavioral outcomes, with decreased d' values and longer reaction times for standard trials. In addition, we found significant differences in source activity between the easy and hard conditions in parts of the pre- and post-central gyrus and the inferior parietal lobe. Our findings are consistent with previous research suggesting that the brain areas responsible for conventional P3m generators also contribute to task difficulty-dependent modulation.

Invasive and noninvasive studies in humans under physiological and pathological conditions converged on the suggestion that brain rhythms are related to cognitive processes such as sensory representations, attentional selection, and dynamical routing/gating of information. Interestingly, rhythms are also evident outside the brain. A prominent example is respiration. I will present recent studies that aim to further our understanding of the link between body and brain rhythms. I will start by describing recently discovered gradients in oscillatory frequency across the brain and then continue to show how brain oscillations are linked to peripheral signals such as respiration and pupil dilation. Together, these studies emphasize the importance of considering the body when studying the brain.

Our long-term goal is to investigate spars electrode arrays (e.g., cEEGrid) performances. For that purpose, we utilize MEG as a reference modality due to its extensive sensor array and sensitivity. For using cEEGrid (https://doi.org/10.1038/srep16743) in the MEG, we designed custom non-magnetic connectors. This study uses cEEGrid for the first time in the MEG. The test study design was inspired by a previously conducted study with cEEGrids by Hölle and colleagues (https://doi.org/10.3758/s13428-021-01538-0). In this study, a rapid Oddball paradigm was used. A single participant was measured with a cEEGrid attached to the right ear. The results confirm the ability to use both the cEEGrid with the MEG and the MEG and replicate the findings reported by Hölle and colleagues (https://doi.org/10.3758/s13428-021-01538-0). This paves the way for more complex paradigms and demonstrates the usability of the cEEGrid connector.

In this session we will give a hands-on demonstration of how MEG data is typically handled. Step by step we show how to import MEG data, extract triggers, preprocess and analyze time series to obtain results such as event related fields and coherence plots on the sensor and the source level. The data set used for this demo was recorded during a finger tapping task. The analysis is performed in MATLAB using the FieldTrip software toolbox. Our goal is an interactive tutorial. Therefore, the dataset and processing scripts are provided here. 

Dry electroencephalography (EEG) electrodes provide rapid, gel-free, and easy EEG preparation, but with limited wearing comfort. Moreover, dry EEG caps are often not suited for combination with magnetoencephalography (MEG). We propose a novel dry electrode comprising multiple tilted pins in a flower-like arrangement. The novel Flower electrode increases wearing comfort and contact area while maintaining ease of use. In a study with 20 volunteers, we compare the performance of a novel 64-channel dry Flower electrode cap to a commercial dry Multipin electrode cap in sitting and supine positions.
The wearing comfort of the Flower cap was rated as significantly improved both in sitting and supine positions. The channel reliability and average impedances of both electrode systems were comparable. Averaged VEP components showed no considerable differences in global field power amplitude and latency, as well as in signal-to-noise ratio and topography. No considerable differences were found in the power spectral density of the resting state EEGs between 1-40 Hz. Overall, our findings provide evidence for equivalent channel reliability and signal characteristics of the compared cap systems in the sitting and supine positions.
The reliability, signal quality, and significantly improved wearing comfort of the Flower electrode allow new fields of applications for dry EEG in long-term monitoring, sensitive populations, and recording in supine position. Consequently, these electrodes are also suited for combination with MEG.

Transcranial alternating current stimulation (tACS) can be used to modulate brain oscillations in human electroencephalogram (EEG) and, in turn, cognitive functions. It is desirable to monitor whether tACS actually modulates brain oscillations with an EEG or MEG. In EEG data, tACS introduces a huge artifact due to electric conduction between stimulation electrodes and recording electrodes via the skin. In MEG data, however, the MEG sensors do not touch the skin and grasp much less of the artifact. It is therefore possible to record MEG during tACS sessions. We will review our recent experiments combining MEG and tACS. In addition, we will demonstrate how MEG recordings of the brain oscillation that is to be modulated by tACS help in predicting effects.

Movement-related neural oscillations are typically studied in the context of simple motor paradigms requiring participants to perform ballistic movements. Here we shed light on the neural processes underlying rapid changes of continuous movement and the effect of movement context on brain activity.
We combined magnetoencephalography and local field potential recordings from the subthalamic nucleus (STN) in Parkinson’s patients to study beta dynamics during initiation, stopping and rapid reversal of rotational movements. The timing of the action prompts was manipulated in two separate blocks to be temporally predictable or unpredictable.
As expected, we observed movement-related beta suppression shortly before movement onset and a post-movement beta rebound following movement termination in STN power, cortical power and STN-cortex coherence. In the STN only, some subjects exhibited a movement-related frequency shift of high-beta power peaks. During rapid reversals of movement direction, STN power modulations were variable across subjects, with some subjects showing little modulation and others a brief increase of beta power when movement came to halt before progressing in the opposite direction. Beta power in motor cortex, in contrast, decreased in both hemispheres after changing direction, but more so in the hemisphere ipsilateral to movement, due to a floor effect on the contralateral side. STN-cortex coherence had similar dynamics at start and stop but, unlike cortical or STN power, increased following reversals. Importantly, beta coherence but not beta power was sensitive to context: unpredictable movement prompts were associated with higher levels of STN-cortex beta coherence than predictable movement prompts.
Our study demonstrates that STN beta power, motor cortical beta power and STN-cortex beta coherence dissociate during rapid adaptations of ongoing movement in different contexts. STN-cortex synchronization appears to be particularly important for post-movement processing of recent motor adaptations, particularly when an unpredictable movement context necessitates cautious behavior.

Depressed patients typically show increased attention to unpleasant and decreased attention to pleasant emotional material and also suffer from reduced expectation and processing of rewards. Imaging studies suggest that the so-called ventromedial prefrontal cortex (vmPFC) is strongly associated with such negativity biases not only in depression and dysphoria, but probably in all disorders associated with impaired processing of reward and safety signals. Accordingly, changes in the excitability of the vmPFC should mediate changes in the processing of emotional and rewarding stimuli. In a series of MEG studies using transcranial direct current stimulation (tDCS) of the vmPFC, we demonstrated that excitatory compared with inhibitory tDCS enhanced processing of pleasant scenes, happy facial expressions, and positive words compared with unpleasant scenes, fearful expressions, and negative words. Excitatory stimulation of the vmPFC compared with inhibitory also decreased loss aversion and increased expectation and processing of rewards in studies of monetary gambling. Because excitatory stimulation of the vmPFC has been shown to attenuate negativity biases in healthy adults, it is reasonable to speculate that such excitatory brain stimulation could serve as an adjunctive treatment to normalize severely disturbed affective and cognitive biases in psychiatric disorders.

The human nervous system is limited with respect to the temporal precision with which incoming information is processed. Seemingly, compensatory mechanisms exist that cover these perceptual imprecisions in everyday life. The neuronal mechanism underlying such continuous perception, however, is not well understood.
Recent studies have linked the temporal resolution of our nervous system to dominant frequencies of ongoing neural activity (i.e., peak frequencies). The evidence, however, mainly rests on the link between alpha-band peak frequencies in parieto-occipital cortices and visual temporal resolution. Evidence from other senses or sensory cortices is limited. Due to these limits, it remains unknown if perceptual sampling in other sensory modalities similarly relies on neural peak frequency. Further, it remains unknown whether the peak frequencies are uniform across modalities, suggesting a higher-order mechanism, or if sampling resolution in different modalities is mediated by different frequency bands, indicating a primarily sensory process.
In our study, 17 controls (12 male, 63 ± 2.8 y) and 17 patients (13 male, 59 ± 2.3 y) with hepatic encephalopathy (HE) performed a tactile and a visual temporal discrimination task. HE patients are known to exhibit impaired perceptual sampling resolution and decreased neural peak frequencies. This allowed us to test the relation of perceptual sampling and neural activity across a wider parameter space. In the tactile task, participants received two suprathreshold electrical stimuli with varying stimulus-onset-asynchrony (SOA; 0-400 ms). Participants reported if they perceived the stimulation as one single or two temporally separate sensations, yielding an individual measure of tactile temporal resolution. In addition, we determined the visual temporal resolution by measuring the critical flicker frequency (CFF). Simultaneously, we measured neuromagnetic activity with an MEG (MEGIN Oy, Finland). The MEG data was used to compute source-level peak frequencies, which were compared across groups and correlated with tactile and visual temporal resolution.
HE patients perceived a single stimulus significantly more often than controls for SOAs between 125-400 ms, indicating impaired tactile temporal resolution. In addition, patients showed significantly decreased CFF, indicating impaired visual temporal resolution. Furthermore, both tactile and visual sampling resolution are linked to posterior alpha- and frontal beta-band peak frequencies. We also correlated individual peak frequencies across groups with peak frequencies. Correlation effects were not limited to the respective sensory cortices but extended to parietal and frontal areas. Effect location was determined by frequency band rather than modality, suggesting cross-modal mechanisms.
We conclude that neural peak frequencies might therefore be linked to general parameters influencing performance in perceptual tasks, instead of underlying modality-specific perceptual sampling.

During my talk I will briefly introduce the working principle of zero-field optically pumped magnetometers. Then I am presenting different field compensation concepts considering both hardware and software solutions and discuss options for attaching the sensors to the participants’ heads. Finally, I show results from a few OPM-MEG based experiments.

In daily life humans are often exposed to highly uncertain environments in which future events are difficult to predict. However, humans also possess an inherent drive to explore uncertain environments, deliberately engaging with novelty. As yet we lack a complete picture of the mechanisms and functions with which human individuals process uncertainty and how they successfully reduce it. One question concerns how predictive processes are shaped by highly uncertain environments.
By adopting a naturalistic music listening paradigm, the project is centered on the analysis of brain data that were collected with magnetoencephalography (MEG). The stimulus set comprises music with varying degrees of uncertainty. Crucially, high-uncertainty music is represented by atonal New Music—a musical style that has an intrinsically high degree of predictive uncertainty, but is largely neglected by empirical research. By using a neural tracking analysis as well as an inter-subject correlation approach, the project aims to study how auditory uncertainty influences neural processing. Furthermore, it investigates, by drawing on a unique sample of musicians specialized in atonal music, whether long-term training with atonal music allows people to better track the sensory input.
Benefitting from a unique interdisciplinary convergence of neuroscience and musicology the results promise to have broad implications for the field of cognitive neuroscience and to further elucidate the role of atonal New Music in Western society.

Neuroscientific studies often involve some form of group analysis over multiple participants. This requires alignment of recordings across participants. A naive solution is to assume that participants' recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to anatomical and functional differences between individual brains. In magnetoencephalography (MEG) recordings the problem of inter-individual alignment is exacerbated by the susceptibility of MEG to individual cortical folding patterns as well as the inter-individual variability of sensor locations over the brain. Hence, an approach to combine MEG data over individual brains should relax the assumptions that brain anatomy and function are tightly linked and that the same sensors can capture functionally comparable brain activation across individuals.

Here we use multiset canonical correlation analysis (M-CCA) to find a common representation of MEG activations recorded from different participants performing a similar task. Our approach applies M-CCA to transform data of multiple participants into a common space with maximum pairwise correlation between participants. Importantly, we derive a method to transform data from a new, previously unseen participant into this common representation. We demonstrate the superiority of the approach over simpler, previously used ones. To this end, we train single-trial inter-individual decoders on the common data representation from one set of participants and test the transfer of the models to data from a new participant who was neither included in finding the common space nor in the training of the decoder. Finally, we show that our approach requires only a small number of labeled data from the new participant.

Our work demonstrates that inter-individual alignment via M-CCA has the potential for combining data of different participants and could become helpful in future endeavors on large open datasets. It also has potential applications in reducing training time of online brain-computer interfaces.

In this talk I will give a tutorial on coupling analysis of electrophysiological data including MEG data. First, I will explain the concepts of phase locking and coherence. The biggest problem for EEG and MEG is the presence of "artifacts of volume conduction", i.e., the mixture of source activities into sensors which cannot be resolved unambiguously. In a second part, methods to address this will be explained both for coupling itself but also for estimates of causal direction.

Open science tools and practices have been developed to advance reproducibility, as well as accessibility and transparency at all stages of the research cycle and across all levels of society. Together, they remove barriers to sharing data or other research products and facilitate collaboration, with the goal of improving reproducibility and, ultimately, accelerating scientific discoveries. However, empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. One potential reason for these low adoption rates might be that the information required for implementing these tools and practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. In our recent publication (Niso, Botvinik-Nezer et al. 2022) we provide an integrated overview of community-developed resources that can support collaborative, open, and reproducible neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities.
With this workshop we strive to provide orientation in the open neuroimaging eco system lowering the bar for the adoption of open science tools and practices across the research cycle from study inception to publication. In the first part of this workshop, we will therefore present a selection of open science tools and practices in an accessible and practice-oriented way (with the focus being on tools developed by/for the MEG community). While the first part will be a pure lecture-style presentation of tools, the second part will be more practical and hands-on. In this part we will briefly talk about the importance of monitoring the quality of ones’ neuroimaging data, again with a focus on Quality Control (QC) in the domain of MEG. This is followed by a hands-on session of MEGqc, a standardized workflow for monitoring the quality and visualization of MEG (BIDS) data, which is currently being developed by the ANCP lab (see the abstract below for more information on MEGqc). In this session, participants are supported to run MEGqc on their own data and interactively inspect the results of the workflow (e.g., inspecting their data with respect to the computed data quality metrics). We encourage participants of the workshop to bring their own (BIDS) data to run it through the pipeline. However, we will also host an online repository with some test data, such that everyone will get some hands-on experience using MEGqc. Please note, that MEGqc is yet only capable to work with .fif data, meaning data originating from systems of other manufacturers than MEGIN will not be supported by MEGqc, the tool presented in this hands-on part. Please bring your own laptops if you want to try running the workflow (Python required).

The second part of the workshop will focus on the importance of Quality assurance and Quality control in the domain of neuroimaging. We will start with some more detailed information on quality control, highlighting MEGqc. MEGqc is a tool for data visualization and monitoring the quality of your (BIDS) MEG data on the basis of data quality metrics.
Due to the high sensitivity of the sensors, magnetoencephalography (MEG) data are susceptible to noise, which can severely corrupt the data quality. Consequently, quality control (QC) of such data is an important step for valid and reproducible science (Niso, Botvinik-Nezer et al., 2022). However, the visual detection and annotation of artifacts in MEG data requires expertise, is a tedious and time extensive task and is hardly standardized. Since quality control is commonly done in an idiosyncratic fashion it might also be subject to individual biases. Despite the minimization of human biases, standardization of QC routines will additionally enable comparisons across datasets and acquisition sites. Hence, an automated and standardized approach to QC is desirable for the quality assessment of in-house and shared datasets. Therefore, we developed a software tool for automated and standardized quality control of MEG recordings: MEGqc. It is inspired by a software for quality control in the domain of fMRI, called mriqc (Esteban et al., 2017). MEGqc strives to support researchers to standardize and speed up their quality control workflow and is designed to be easy and intuitive to use, e.g., only minimal user input (path to the dataset) is required. To achieve this usability, the tool is tailored to the established BIDS standard (Gorgolewski et al., 2016; Niso et al., 2018). Among other metrics, we detect noise frequencies in the Power Spectral Density and calculate their relative power, calculate several metrics to describe the ‘noisiness’ of channels and/or epochs, e.g., STD or peak-to-peak amplitudes, and quantify EOG and ECG related noise averaged over all channels and on a per-channel basis. MEGqc generates BIDS compliant html reports for interactive visualization of the data quality metrics and moreover provides machine interoperable JSON outputs, which allow for the integration into automated workflows. MEGqc is open source, can be found on Github (https://github.com/ANCPLabOldenburg/MEG-QC-code), and its documentation is hosted on readthedocs (https://meg-qc.readthedocs.io/en/latest/).

My talk will address the efficacy of targeted (by combined MEG/EEG source analysis) and optimized multi-channel transcranial direct current stimulation (mc-tDCS) as therapy for focal epilepsy in a double-blind sham-controlled N-of-1 trial. Targeted and optimized mc-tDCS was applied in a 20 year-old pharmaco-resistant epilepsy patient. 
For mc-tDCS optimization we used our recently developed algorithm - Distributed Constrained Maximum Intensity (D-CMI) (Khan et al., 2022, doi: 10.1016/j.clinph.2021.10.016) - on a target region which was determined by mean of combined MEG/EEG source analysis of averaged interictal epileptiform discharges (IEDs) using realistic and skull-conductivity calibrated finite-element head modeling. D-CMI was shown to be superior to standard bipolar tDCS and sham in a somatosensory group study (Khan et al., 2023, doi: 10.1016/j.brs.2022.12.006). 
A total amplitude of 4 mA was applied twice for 20 minutes, with a pause of 20 minutes in between, for five consecutive days. An Acti-Sham montage was also applied. 
There was a washout of 5 weeks between the two stimulation weeks. With regard to interictal activity, targeted D-CMI mc-tDCS led to a highly significant reduction in IED frequency after treatment, while Acti-Sham did not. Side effects entirely pertained to transient sensations. With regard to ictal activity, the patient experienced a seizure with decreased severity during the stimulation week.