FieldTrip Made Easy: An Analysis Protocol for Group Analysis of the Auditory Steady State Brain Response in Time, Frequency, and Space

The auditory steady state evoked response (ASSR) is a robust and frequently utilized phenomenon in psychophysiological research. It reflects the auditory cortical response to an amplitude-modulated constant carrier frequency signal. The present report provides a concrete example of a group analysis of the EEG data from 29 healthy human participants, recorded during an ASSR paradigm, using the FieldTrip toolbox. First, we demonstrate sensor-level analysis in the time domain, allowing for a description of the event-related potentials (ERPs), as well as their statistical evaluation. Second, frequency analysis is applied to describe the spectral characteristics of the ASSR, followed by group level statistical analysis in the frequency domain. Third, we show how time- and frequency-domain analysis approaches can be combined in order to describe the temporal and spectral development of the ASSR. Finally, we demonstrate source reconstruction techniques to characterize the primary neural generators of the ASSR. Throughout, we pay special attention to explaining the design of the analysis pipeline for single subjects and for the group level analysis. The pipeline presented here can be adjusted to accommodate other experimental paradigms and may serve as a template for similar analyses

BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

Published: 13 January 2022Analyses of brain function and anatomy using shared neuroimaging data is an important development, and have acquired the potential to be scaled up with the specification of a new Brain Imaging Data Structure (BIDS) standard. To date, a variety of software tools help researchers in converting their source data to BIDS but often require programming skills or are tailored to specific institutes, data sets, or data formats. In this paper, we introduce BIDScoin, a cross-platform, flexible, and user-friendly converter that provides a graphical user interface (GUI) to help users finding their way in BIDS standard. BIDScoin does not require programming skills to be set up and used and supports plugins to extend their functionality. In this paper, we show its design and demonstrate how it can be applied to a downloadable tutorial data set. BIDScoin is distributed as free and open-source software to foster the community-driven effort to promote and facilitate the use of BIDS standard.We would like to thank Rutger van Deelen for providing the initial (PyQt) setup and implementation of the bidseditor application and Yorguin José Mantilla Ramos for the useful architectural feedback and for the initial code of the sova2coin EEG/MEG plugin. We are also grateful for all the feedback, questions, and contributions that users have submitted on GitHub

Magnetoencephalography reveals increased slow-to-fast alpha power ratios in patients with chronic pain

INTRODUCTION: Objective disease markers are a key for diagnosis and personalized interventions. In chronic pain, such markers are still not available, and therapy relies on individual patients' reports. However, several pain studies have reported group-based differences in functional magnetic resonance imaging, electroencephalography, and magnetoencephalography (MEG). OBJECTIVES: We aimed to explore spectral differences in resting-state MEG brain signals between patients with chronic pain and pain-free controls and to characterize the cortical and subcortical regions involved. METHODS: We estimated power spectral density over 5 minutes of resting-state MEG recordings in patients with chronic pain and controls and derived 7 spectral features at the sensor and source levels: alpha peak frequency, alpha power ratio (power 7–9 Hz divided by power 9–11 Hz), and average power in theta, alpha, beta, low-gamma, and high-gamma bands. We performed nonparametric permutation t tests (false discovery rate corrected) to assess between-group differences in these 7 spectral features. RESULTS: Twenty-one patients with chronic pain and 25 controls were included. No significant group differences were found in alpha peak frequency or average power in any frequency band. The alpha power ratio was significantly higher (P < 0.05) in patients with chronic pain at both the sensor and brain source levels. The brain regions showing significantly higher ratios included the occipital, parietal, temporal and frontal lobe areas, insular and cingulate cortex, and right thalamus. CONCLUSION: The alpha power ratio is a simple, promising signal marker of chronic pain, affecting an expansive range of cortical and subcortical regions, including known pain-processing areas

Heterogeneous Cortical Effects of Spinal Cord Stimulation

Objectives: The understanding of the cortical effects of spinal cord stimulation (SCS) remains limited. Multiple studies have investigated the effects of SCS in resting-state electroencephalography. However, owing to the large variation in reported outcomes, we aimed to describe the differential cortical responses between two types of SCS and between responders and nonresponders using magnetoencephalography (MEG). Materials and Methods: We conducted 5-minute resting-state MEG recordings in 25 patients with chronic pain with active SCS in three sessions, each after a one-week exposure to tonic, burst, or sham SCS. We extracted six spectral features from the measured neurophysiological signals: the alpha peak frequency; alpha power ratio (power 7–9 Hz/power 9–11 Hz); and average power in the theta (4–7.5 Hz), alpha (8–12.5 Hz), beta (13–30 Hz), and low-gamma (30.5–60 Hz) frequency bands. We compared these features (using nonparametric permutation t-tests) for MEG sensor and cortical map effects across stimulation paradigms, between participants who reported low (&lt; 5, responders) vs high (≥ 5, nonresponders) pain scores, and in three representative participants. Results: We found statistically significant (p &lt; 0.05, false discovery rate corrected) increased MEG sensor signal power below 3 Hz in response to burst SCS compared with tonic and sham SCS. We did not find statistically significant differences (all p &gt; 0.05) between the power spectra of responders and nonresponders. Our data did not show statistically significant differences in the spectral features of interest among the three stimulation paradigms or between responders and nonresponders. These results were confirmed by the MEG cortical maps. However, we did identify certain trends in the MEG source maps for all comparisons and several features, with substantial variation across participants. Conclusions: The considerable variation in cortical responses to the various SCS treatment options necessitates studies with sample sizes larger than commonly reported in the field and more personalized treatment plans. Studies with a finer stratification between responders and nonresponders are required to advance the knowledge on SCS treatment effects.</p

Cerebral coherence between communicators marks the emergence of meaning

How can we understand each other during communicative interactions? An influential suggestion holds that communicators are primed by each other’s behaviors, with associative mechanisms automatically coordinating the production of communicative signals and the comprehension of their meanings. An alternative suggestion posits that mutual understanding requires shared conceptualizations of a signal’s use, i.e., “conceptual pacts” that are abstracted away from specific experiences. Both accounts predict coherent neural dynamics across communicators, aligned either to the occurrence of a signal or to the dynamics of conceptual pacts. Using coherence spectral-density analysis of cerebral activity simultaneously measured in pairs of communicators, this study shows that establishing mutual understanding of novel signals synchronizes cerebral dynamics across communicators’ right temporal lobes. This interpersonal cerebral coherence occurred only within pairs with a shared communicative history, and at temporal scales independent from signals’ occurrences. These findings favor the notion that meaning emerges from shared conceptualizations of a signal’s use

On-scalp MEG sensor localization using magnetic dipole-like coils: A method for highly accurate co-registration

Source modelling in magnetoencephalography (MEG) requires precise co-registration of the sensor array and the anatomical structure of the measured individual\u27s head. In conventional MEG, the positions and orientations of the sensors relative to each other are fixed and known beforehand, requiring only localization of the head relative to the sensor array. Since the sensors in on-scalp MEG are positioned on the scalp, locations of the individual sensors depend on the subject\u27s head shape and size. The positions and orientations of on-scalp sensors must therefore be measured a every recording. This can be achieved by inverting conventional head localization, localizing the sensors relative to the head - rather than the other way around. In this study we present a practical method for localizing sensors using magnetic dipole-like coils attached to the subject\u27s head. We implement and evaluate the method in a set of on-scalp MEG recordings using a 7-channel on-scalp MEG system based on high critical temperature superconducting quantum interference devices (high-T-c SQUIDs). The method allows individually localizing the sensor positions, orientations, and responsivities with high accuracy using only a short averaging time (&lt;= 2 mm, &lt; 3 degrees and &lt; 3%, respectively, with 1-s averaging), enabling continuous sensor localization. Calibrating and jointly localizing the sensor array can further improve the accuracy of position and orientation (&lt; 1 mm and &lt; 1 degrees, respectively, with 1-s coil recordings). We demonstrate source localization of on-scalp recorded somatosensory evoked activity based on coregistration with our method. Equivalent current dipole fits of the evoked responses corresponded well (within 4.2 mm) with those based on a commercial, whole-head MEG system

Audiovisual Processing of Chinese Characters Elicits Suppression and Congruency Effects in MEG

Learning to associate written letters/characters with speech sounds is crucial for reading acquisition. Most previous studies have focused on audiovisual integration in alphabetic languages. Less is known about logographic languages such as Chinese characters, which map onto mostly syllable-based morphemes in the spoken language. Here we investigated how long-term exposure to native language affects the underlying neural mechanisms of audiovisual integration in a logographic language using magnetoencephalography (MEG). MEG sensor and source data from 12 adult native Chinese speakers and a control group of 13 adult Finnish speakers were analyzed for audiovisual suppression (bimodal responses vs. sum of unimodal responses) and congruency (bimodal incongruent responses vs. bimodal congruent responses) effects. The suppressive integration effect was found in the left angular and supramarginal gyri (205–365 ms), left inferior frontal and left temporal cortices (575–800 ms) in the Chinese group. The Finnish group showed a distinct suppression effect only in the right parietal and occipital cortices at a relatively early time window (285–460 ms). The congruency effect was only observed in the Chinese group in left inferior frontal and superior temporal cortex in a late time window (about 500–800 ms) probably related to modulatory feedback from multi-sensory regions and semantic processing. The audiovisual integration in a logographic language showed a clear resemblance to that in alphabetic languages in the left superior temporal cortex, but with activation specific to the logographic stimuli observed in the left inferior frontal cortex. The current MEG study indicated that learning of logographic languages has a large impact on the audiovisual integration of written characters with some distinct features compared to previous results on alphabetic languages