Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

Description

Notes

Methods

MEG data acquisition

Data from 67 healthy human volunteers (age 18 to 57 years old; mean age: 30.9 ± 8.3 years; 6 left-handed; 32 female, 35 male; 64 Caucasian, 2 South Asian, 1 East Asian) were collected for this study. The study was performed in accordance with the Declaration of Helsinki and with permission of the Ethical Committee of the hospital at which the recordings were carried out. All participants gave written informed consent prior to the recordings. 

MEG data was recorded with a 306-channel (204 planar gradiometers and 102 magnetometers) Triux MEG (Elekta-Neuromag/MEGIN, Helsinki, Finland). We recorded 10 minutes of eyes-open resting-state data from all participants. Bipolar horizontal and vertical EOG were recorded for the detection of ocular artifacts. MEG and EOG were recorded at a 1,000-Hz sampling rate.

T1-weighted anatomical MRI scans (MP-RAGE) were obtained for head models and cortical surface reconstruction at a resolution of 1 × 1 × 1 mm with a 1.5-Tesla MRI scanner.

MEG data preprocessing and filtering

Volumetric segmentation of MRI data, flattening, cortical parcellation, and neuroanatomical labeling were carried out using FreeSurfer software (http://surfer.nmr.mgh.harvard.edu). We used the Schaefer atlas with 200 parcels (Schaefer et al., 2018). MNE software (https://mne.tools/stable/index.html) was used for the preparation of cortically constrained source models for MEG–MRI colocalization, forward and inverse operators. The source models had dipole orientations fixed to pial-surface normals and a 5-mm inter-dipole separation throughout the cortex, where hemispheres had between 5080–7645 active source vertices.

Temporal signal space separation (tSSS) in the Maxfilter software (Elekta-Neuromag) was used to suppress extracranial noise from MEG sensors and to interpolate bad channels. We used independent components analysis (ICA) adapted from the MATLAB toolbox Fieldtrip (http://www.fieldtriptoolbox.org) to extract and identify components that were correlated with ocular artifacts (identified using the EOG signal), heartbeat artifacts (identified using the magnetometer signal as a reference), or muscle artifacts. We estimated vertex fidelity to obtain fidelity-weighted inverse operators that improve reconstruction accuracy, as described in previous work (Siebenhühner et al., 2020). After source reconstruction, the time-series data were collapsed to the 200 cortical parcels and then filtered into narrowband time series using a bank of 41 Morlet filters with wavelet width parameter m = 5 and approximately log-linear spacing of center frequencies ranging from 1.1 to 95.6 Hz.

Estimation of inter-areal phase synchrony and amplitude correlations in MEG data

To estimate pairwise phase synchrony between parcels, we estimated the imaginary PLV which is insensitive to zero-lag false-positive interactions which are often spurious due to residual linear mixing after inverse modeling (J. M. Palva et al., 2018).

Amplitude correlations were computed between the amplitude envelopes of the narrowband time series with the orthogonalized correlation coefficient (oCC), where one of the two time series is orthogonalized with respect to the other which also has the effect of eliminating spurious connections (Brookes et al., 2012; Hipp et al., 2012).

Neurotransmitter receptor and transporter maps

The original receptor maps (Hansen et al., 2022) and parcel coordinates were downloaded from https://github.com/netneurolab/hansen_receptors and then morphed to the 17-network Schaefer atlas with 200 cortical parcels in MNE order.