Transcranial alternating current stimulation (tACS) appears to modulate neuronal oscillations at the frequency of stimulation. Longer periods of stimulation with tACS (10–40 min) have shown to produce persistent changes, especially in alpha power (~8–12 Hz), whereas the efficacy of shorter periods of tACS (1–8 s) is less known. Thus, we investigated whether short periods of tACS applied to the somatosensory cortex elicit changes in alpha power following stimulation. With this aim, during simultaneous acquisition of MEG, we administered tACS and control (no-tACS) on separate days. We applied short trains of stimulation for durations of 10 s and 30 s at an individually adapted stimulation frequency (ISF). Each stimulation train was followed by a 15 s interval. We calculated power changes in the post-stimulation intervals, relative to a baseline period, and the resulting Δpower was used to statistically test the difference between tACS and control conditions. We found significant elevations in power at ISF following tACS compared with control. The extent of this effect spanned bilaterally over the somatosensory and frontal regions. While the observed increase in power was most prominent around ISF (i.e., in the alpha band), power modulations were also observed in the beta-band. When comparing the two stimulation durations, 10 s of tACS produced greater increases in power (at ISF) than 30 s of tACS. This study validates that 10 s of tACS produces robust elevations of power in the somatosensory cortex at ISF, thereby establishing its potential for use in future studies.

The present study investigated temporal aspects of cerebellar contributions to the processing of performance errors as indexed by the error-related negativity (ERN) in the response-locked event-related potential (ERP). We co-registered EEG and applied single-pulse transcranial magnetic stimulation (spTMS) to the left posterolateral cerebellum and an extra-cerebellar control region (vertex) while healthy adult volunteers performed a Go/Nogo Flanker Task. In Go trials, TMS pulses were applied at four different time points, with temporal shifts of -100 ms, -50 ms, 0 ms, or +50 ms relative to the individual error latency (IEL, i.e., individual ERN peak latency + median error response time). These stimulation timings were aggregated into early (-100 ms, -50 ms) and late (0 ms, +50 ms) stimulation for the analysis. In Nogo trials, TMS pulses occurred 0 ms, 100 ms, or 300 ms after stimulus onset. Mixed linear model analyses revealed that cerebellar stimulation did not affect error rates overall. No effects were found for response times. As hypothesized, ERN amplitudes were decreased for cerebellar stimulation. No significant differences were found for the error positivity (Pe). Similar to TMS application to probe cerebellar-brain inhibition in the motor domain, the inhibitory tone of the cerebellar cortex may have been disrupted by the pulses. Reduced inhibitory output of the cerebellar cortex may have facilitated the processing of error information for response selection, which is reflected in a decreased ERN.

The combination of subcortical Local Field Potential (LFP) recordings and stimulation with Magnetoencephalography (MEG) in Deep Brain Stimulation (DBS) patients enables the investigation of cortico-subcortical communication patterns and provides insights into DBS mechanisms. Until now, these recordings have been carried out in post-surgical patients with externalised leads. However, a new generation of telemetric stimulators makes it possible to record and stream LFP data in chronically implanted patients. Nevertheless, whether such streaming can be combined with MEG has not been tested. In the present study, we tested the most commonly implanted telemetric stimulator—Medtronic Percept PC with a phantom in three different MEG systems: two cryogenic scanners (CTF and MEGIN) and an experimental Optically Pumped Magnetometry (OPM)-based system. We found that when used in combination with the new SenSight segmented leads, Percept PC telemetric streaming only generates band-limited interference in the MEG at 123 Hz and harmonics. However, the “legacy streaming mode” used with older lead models generates multiple, dense artefact peaks in the physiological range of interest (below 50 Hz). The effect of stimulation on MEG critically depends on whether it is done in bipolar (between two contacts on the lead) or monopolar (between a lead contact and the stimulator case) mode. Monopolar DBS creates severe interference in the MEG as previously reported. However, we found that the OPM system is more resilient to this interference and could provide artefact-free measurements, at least for limited frequency ranges. A resting measurement in the MEGIN system from a Parkinson’s patient implanted with Percept PC and subthalamic SenSight leads revealed artefact patterns consistent with our phantom recordings. Moreover, analysis of LFP-MEG coherence in this patient showed oscillatory coherent networks consistent in their frequency and topography with those described in published group studies done with externalised leads. In conclusion, Percept PC telemetric streaming with SenSight leads is compatible with MEG. Furthermore, OPM sensors could provide additional new opportunities for studying DBS effects.

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1 H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad, and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log 10 normed-MSE of—1.79, an improvement of almost two orders of magnitude.