Neuromag triux

We acquired magnetic brain signals using a whole-head MEG system with 306 sensors (204 planar gradiometers, 102 magnetometers, Elekta Neuromag TRIUX, Elekta, Stockholm, Sweden). Participants were placed in the supine position into the scanner located inside the magnetically shielded room (Vacuumschmelze GmbH, Hanau, Germany). During acquisition, the MEG data was sampled at 1000 Hz and the head position of each study participant was recorded, before and after each task, relatively to the MEG sensors by using five head-localization coils. The head-localization coil positions and scalp outline (roughly 300 points) were digitized using a three-dimensional digitizer (Fastrak; Polhemus, Colchester, VT, United States). All digitized points were used to achieve accurate co-registration between the structural T1 image of the participant and the position of the head inside the MEG helmet with the sensors.
The stimuli were presented via projector onto the 80 cm screen, positioned on the ceiling above the participant at a distance of approximately 200 cm. The stimuli presentation paradigms were programmed by using STIM2 (Neuroscan, Compumedics) software. At the time when the events of interest have occurred, a trigger was sent to mark the trigger channel within the MEG recording.

The measurements were carried out at the Jyväskylä Centre for Interdisciplinary Brain Research. MEG signals were recorded in a magnetically shielded room (Magnetical Shielding Cabin, VACOSHIELD, Vacuumschmelze GmbH & Co. KG, Hanau, Germany) with a 306-channel whole-scalp neuromagnetometer (Elekta Neuromag^® TRIUX™, Elekta Oy, Helsinki, Finland). The recording passband was 0.1–330 Hz and the signals were sampled at 1 kHz. The individual’s head position inside the MEG helmet was continuously monitored by feeding current to five head-tracking coils. The coils were attached on the scalp prior to measurement and their locations were determined with respect to anatomical fiducials with an electromagnetic tracker (Fastrak, Polhemus, Colchester, VT, USA).

For the healthy control subjects, we recorded MEG with a 306-channel whole-cortex magnetometer (Elekta Neuromag TRIUX, Elekta, Stockholm, Sweden) in a magnetically shielded room. Participants sat upright in the MEG. All recordings were sampled at 2000 Hz and online-filtered with a pass-band of 0.1–660 Hz. Headshape was digitized analogue to patient’s measurements.
As above, we downsampled the MEG recordings to 500 Hz. Second, we filtered the recordings using a 165 Hz Butterworth low-pass filter (order = 6), two Butterworth band-stop filters (to attenuate line noise; 49-51 Hz, 99–101 Hz; order = 6), and a 0.5 Hz Butterworth high-pass filter (order = 6). Third, we epoched the recordings around the onset of the visual target, starting 2 seconds before target onset and ending 2 seconds after target onset. Fourth, we used ICA to detect and remove spatially-stationary artifacts including eye blinks, eye movements, cardiac artifacts, and residual motion related artifacts. Fifth, we inspected the recordings for artifactual activity. Any trials/channels exhibiting such activity were excluded. LCMV beamforming was conducted in the same manner as described above.

MEG signals were acquired from 306 channels (204 planar gradiometers, 102 magnetometers, Elekta Neuromag TRIUX, Elekta, Stockholm, Sweden) using a sampling rate of 1 kHz, filtered between 0.03 and 330 Hz. We applied temporal source space separation (maxfilter software, Elekta, Stockholm) [81 (link),82 (link)] before analyzing data with Brainstorm [83 (link)]. For each trial, we extracted peri-stimulus data from −200 ms to +1,000 ms, removed baseline mean, and smoothed data with a 30 Hz low pass filter. We obtained 25 trials for each condition, session, and participant.

Brain and peripheral signals were recorded at 1000 Hz with hardware filters between 0.1 and 330 Hz, in a passive magnetically shielded room using a whole head MEG (Elekta Neuromag Triux). Magnetic brain data were sampled with 204 orthogonal gradiometers and 102 magnetometers. Simultaneously, we recorded electric brain signal with 18 active monopolar EEG channels spanning the whole scalp according to the international 10–20 system (Fp1, Fpz, Fp2, F3, Fz, F4, FC5, FC6, C3, Cz, C4, P3, Pz, P4, O1, O2, left and right mastoid), with a reference placed on the left ear and a ground placed on the right shoulder. Additional bipolar peripheral channels were recorded: EMG on the chin, ECG across the chest, and two EOG (horizontal and vertical), following standard recommendations for sleep EEG data acquisition of the American Association of Sleep Medicine (Iber and Iber, 2007 ). EEG impedances were kept <5 kOhm for the monopolar channels, and <75 kOhm for the bipolar channels. The position of the head in the MEG helmet was acquired at the beginning of each session with 5 HPI localization coils (three placed on the forehead, two placed on the left and right periauricular points). Shape of the head as well as positions of the electrodes and the localization coils were 3D digitized with Polhemous FASTTRACK.

Participants will be seated inside a magnetically shielded room (VACOSHIELD, Vacuumschmelze GmbH & Co. KG, Hanau, Germany) with their heads positioned within the sensor array of the whole-head-MEG System (Elekta Neuromag TRIUX, Elekta Oy, Helsinki, Finland), consisting of 204 planar gradiometers and 102 magnetometers. MEG data will be acquired at 1000 Hz and band-pass filtered (0.1–330 Hz).

A whole‐head MEG system (Elekta Neuromag Triux, Elekta Oy, Finland), placed within a standard, passive magnetically shielded room (AK3b, Vacuumschmelze, Germany), was used to capture magnetic brain activity with a sampling frequency of 1 kHz (hardware filters: 0.1–330 Hz). The signal was recorded with 102 magnetometers and 204 orthogonally placed planar gradiometers at 102 different positions. In a first step, a signal space separation algorithm, implemented in the Maxfilter program (version 2.2.15) provided by the MEG manufacturer, was used to clean the data of external noise and realign data from different blocks to a common standard head position. Data preprocessing was performed using Matlab R2020b (The MathWorks, Natick, Massachusetts, USA) and the FieldTrip Toolbox (Oostenveld et al., 2010 (link)). To identify eye blinks and heartbeat artifacts, 50 independent components were identified from filtered (0.1–100 Hz) continuous data of the first experimental block of both modalities (auditory + visual). On average, 2.6 (range = 2–5) components were removed for each subject. All data were filtered between 0.1 Hz and 30 Hz (Kaiser windowed finite impulse response filter) and downsampled to 100 Hz. Then, the data of each block were epoched into segments of 1200 ms (from 400 ms before stimulus onset to 800 ms after onset) for further analysis (as in Schubert et al., 2023 (link)).