DOI: 10.5061/dryad.qz612jmq1
title: Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

description: Information processing and communication in neuronal circuits is enabled
 by dynamic networks of inter-areal coupling of neuronal oscillations in
 which hubs play a central role for regulation of communication.
 Oscillations are shaped by interactions between pyramidal cells and
 interneurons and are locally influenced by neuromodulatory systems. Here,
 we set out to investigate how sparial variability in neurotransmitter
 receptor and transporter density influences frequency-specific large-scale
 networks of phase-synchrony (PS) and amplitude-correlation (AC) in human
 magnetoencephalography data. We found that node centrality -
 indexing which individual brain regions function as hubs - covaried
 positively with GABA, NMDA, dopaminergic, and most serotonergic receptor
 and transporter densities in lower frequency bands (delta to low-alpha for
 PS, and delta for AC) and in the gamma band, but negatively in between.
 These results establish how local microarchitecture influences large-scale
 connectivity networks of neuronal oscillations in the human brain in
 frequency- and spatially-specific patterns.
description: <strong>MEG data acquisition</strong>
 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. <strong>MEG data
 preprocessing and filtering</strong> 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. <strong>Estimation of inter-areal phase
 synchrony and amplitude correlations in MEG data</strong>
 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). <strong>Neurotransmitter receptor and
 transporter maps</strong> 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.
description: # Linking the microarchitecture of neurotransmitter systems to large-scale
 MEG resting state networks
 [https://doi.org/10.5061/dryad.qz612jmq1](https://doi.org/10.5061/dryad.qz612jmq1) This dataset provides functional connectivity data from source-reconstructed MEG, as well as receptor maps and other support files that can be used to reproduce the article's findings. ## Description of the data and file structure **Functional connectivity files**: *conn_iPLV_67.npy*: A numpy file containing a 4D numpy array containing phase synchrony values (estimated with iPLV) between 200 cortical parcels of the 17-network Schaefer atlas, for 41 narrowband frequencies and 67 subjects. The shape of the array is [subjects x frequencies x parcels x parcels]. *conn_oCC_67.npy*: A numpy file containing a 4D numpy array containing amplitude correlation values (estimated with oCC) between 200 cortical parcels of the 17-network Schaefer atlas, for 41 narrowband frequencies and 67 subjects. The shape of the array is [subjects x frequencies x parcels x parcels]. **Receptor maps**:  *receptor_maps_17_200*: Folder containing neurotransmitter receptor and transporter density maps for 200 cortical parcels of the 17-network Schaefer atlas. These maps are the same as can be found at [https://github.com/netneurolab/hansen_receptors](https://github.com/netneurolab/hansen_receptors), but re-ordered into the same parcel order that is used in the functional connectivity files. Each file contains the values for all 200 parcels for a receptor or transporter, derived from one PET study. **Support files**:  *mf_inds_67.csv*: List of values of biological sex for subjects [1 = male, 2 = female] *parc2018yeo17_200.csv*: Parcel names (1st column) and abbreviations (2nd column) for the 17-network 200-parcel Schaefer atlas in MNE order. *parcel_coords_parc2018yeo17_200.csv*: Coordinates (x,y,z) for parcels' center of mass in the same atlas. ## Code/Software Custom python code for carrying out the analyses in the article can be found at [https://github.com/palvalab/receptor_centrality. ](https://github.com/palvalab/receptor_centrality) The code requires the packages os, sys, numpy, scipy, random, pandas, matplotlib, bct, netneurotools, networkx, pickle, statsmodels, nibabel, nilearn, sklearn, pyvista.