Replicable frequency-specific modularity among data-driven resting networks in MEG
Poster No:
1331
Submission Type:
Abstract Submission
Authors:
Bradley Baker1, Spencer Kinsey2, Oktay Agcaoglu1, Yu-Ping Wang3, Julia Stephen4, Nathan Petro5, Lauren Webert5, Anna Coutant5, Erica Steiner5, Grace Ende5, Danielle Rice6, Maggie Rempe5, Tony Wilson5, Vince Calhoun7
Institutions:
1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, 2Georgia State University, Atlanta, GA, 3Tulane University, New Orleans, LA, 4Mind Research Network, Albuquerque, NM, 5Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, 6Marquette University, Milwaukee, WI, 7GSU/GATech/Emory, Atlanta, GA
First Author:
Bradley Baker
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Co-Author(s):
Oktay Agcaoglu, PhD
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Introduction:
The use of magnetoencephalography (MEG) for functional brain imaging has received increased attention in recent years due to MEG's affinity for characterizing neural oscillations and dynamics (Uhlhaas 2008). The high temporal resolution of MEG allows for the analysis of fast oscillatory dynamics and frequency bands not available to lower-resolution modalities such as functional magnetic resonance imaging (fMRI).
Data-driven network discovery techniques such as independent component analysis (ICA) have been widely utilized in other modalities such as fMRI [2]; however, the application to MEG has been limited (Houck 2017). In this work, we demonstrate that group ICA of MEG allows for the discovery of networks that are highly modular in their functional network connectivity (FNC). Additionally, we utilize the high temporal resolution of MEG to compute FNCs within specific frequency bands and to evaluate replicability across time.
Data-driven network discovery techniques such as independent component analysis (ICA) have been widely utilized in other modalities such as fMRI [2]; however, the application to MEG has been limited (Houck 2017). In this work, we demonstrate that group ICA of MEG allows for the discovery of networks that are highly modular in their functional network connectivity (FNC). Additionally, we utilize the high temporal resolution of MEG to compute FNCs within specific frequency bands and to evaluate replicability across time.
Methods:
This initial study included 6 participants (mean age 28.95; 4 male, 2 female). MEG data were sampled at 1kHz and motion-corrected and noise-reduced prior to processing. A notch filter at 60 Hz and harmonics and a bandpass filter from 1 to 200 Hz were employed. Cardiac and blink artifacts were removed using signal space projection. The raw time series were epoched in 4-second segments and noisy segments, and channels were removed for further analysis. These data were source-imaged using a time-domain linear constraint minimum variance beamformer. The resulting voxel-wise maps were down-sampled, resulting in maps that are 4x4x4 mm with a temporal resolution of 250 Hz.
We ran group independent component analysis (gICA) using the GIFT toolbox (Rachakonda 2007). Infomax ICA with 100 components, using ICASSO with 50 runs, was used. For each participant, the first 3 epochs were dropped after manual inspection of the FNC revealed artifacts across multiple participants. We then used the GIFT autolabeler (Salman 2022) to discard noise-related networks and identify networks correlated with the Yeo-Buckner functional atlas (Buckner 2011).
FNC was computed by applying a band-pass filter to the ICA timecourses to extract frequencies from the corresponding bands (Delta [1-4Hz], Theta [4-8 Hz], Alpha [8-12 Hz], Beta [12-30 Hz], Gamma [>30 Hz]). ICA timecourses were despiked and detrended, the Pearson correlation coefficient was computed between the timecourse from each component pair. Bandwise differences were also computed. To measure the replicability of the FNCs across epochs, we also measured the correlation of the FNCs between the first 30 and last 30 epochs.
We ran group independent component analysis (gICA) using the GIFT toolbox (Rachakonda 2007). Infomax ICA with 100 components, using ICASSO with 50 runs, was used. For each participant, the first 3 epochs were dropped after manual inspection of the FNC revealed artifacts across multiple participants. We then used the GIFT autolabeler (Salman 2022) to discard noise-related networks and identify networks correlated with the Yeo-Buckner functional atlas (Buckner 2011).
FNC was computed by applying a band-pass filter to the ICA timecourses to extract frequencies from the corresponding bands (Delta [1-4Hz], Theta [4-8 Hz], Alpha [8-12 Hz], Beta [12-30 Hz], Gamma [>30 Hz]). ICA timecourses were despiked and detrended, the Pearson correlation coefficient was computed between the timecourse from each component pair. Bandwise differences were also computed. To measure the replicability of the FNCs across epochs, we also measured the correlation of the FNCs between the first 30 and last 30 epochs.
Results:
Figure 1 provides the spatial maps of the 49 resting-state networks identified with group ICA of the MEG data collected in this study.
Figure 2 illustrates the FNC computed for each frequency band (on-diagonal) and the differences between each pair of bands (off-diagonal). We observe increased connectivity in the delta band compared to all other bands. We also observe increased connectivity in the theta band compared to the beta band and decreased connectivity in the gamma bands compared to the theta, alpha, and beta bands.
When comparing the correlation of FNC of the first 30 epochs with the last 30, we found that the computed FNC matrices were highly stable. The correlations in each band were measured as: DELTA 0.962, THETA 0.976, ALPHA 0.978, BETA 0.992, GAMMA 0.995.
Figure 2 illustrates the FNC computed for each frequency band (on-diagonal) and the differences between each pair of bands (off-diagonal). We observe increased connectivity in the delta band compared to all other bands. We also observe increased connectivity in the theta band compared to the beta band and decreased connectivity in the gamma bands compared to the theta, alpha, and beta bands.
When comparing the correlation of FNC of the first 30 epochs with the last 30, we found that the computed FNC matrices were highly stable. The correlations in each band were measured as: DELTA 0.962, THETA 0.976, ALPHA 0.978, BETA 0.992, GAMMA 0.995.
Conclusions:
Forty-nine resting-state networks were identified with group ICA of MEG data and showed a highly modular functional network connectivity profile (strongest in visual areas but also including somatomotor and attentional modularity, among others), which is stable across epochs and allows for the study of connectivity differences across frequency bands. In future work, we aim to collect and analyze more MEG data in a similar fashion, with the ultimate goal of creating a resting-state template for MEG that would allow for future replicable analyses like the one pursued in this work.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis 2
Keywords:
Informatics
MEG
1|2Indicates the priority used for review
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[4] Rachakonda, S., Egolf, E., Correa, N., & Calhoun, V. (2007). Group ICA of fMRI toolbox (GIFT) manual.
[5] Salman, M. S., Wager, T. D., Damaraju, E., Abrol, A., Vergara, V. M., Fu, Z., & Calhoun, V. D. (2022). An approach to automatically label and order brain activity/component maps. Brain Connectivity, 12(1), 85-95.
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