Measuring functional connectivity using MEG: Methodology and comparison with fcMRI

By Matthew J. Brookes, Joanne R. Hale, Johanna M. Zumer, Claire M. Stevenson, Susan T. Francis, Gareth Robert Barnes, Julia P. Owen, Peter G. Morris, Srikantan S. Nagarajan. NeuroImage, 2011

519 citations recorded by the source corpus.

Functional connectivity (FC) between brain regions is thought to be central to the way in which the brain processes information. Abnormal connectivity is thought to be implicated in a number of diseases. The ability to study FC is therefore a key goal for neuroimaging. Functional connectivity (fc) MRI has become a popular tool to make connectivity measurements but the technique is limited by its indirect nature. A multimodal approach is therefore an attractive means to investigate the electrodynamic mechanisms underlying hemodynamic connectivity. In this paper, we investigate resting state FC using fcMRI and magnetoencephalography (MEG). In fcMRI, we exploit the advantages afforded by ultra high magnetic field. In MEG we apply envelope correlation and coherence techniques to source space projected MEG signals. We show that beamforming provides an excellent means to measure FC in source space using MEG data. However, care must be taken when interpreting these measurements since cross talk between voxels in source space can potentially lead to spurious connectivity and this must be taken into account in all studies of this type. We show good spatial agreement between FC measured independently using MEG and fcMRI; FC between sensorimotor cortices was observed using both modalities, with the best spatial agreement when MEG data are filtered into the β band. This finding helps to reduce the potential confounds associated with each modality alone: while it helps reduce the uncertainties in spatial patterns generated by MEG (brought about by the ill posed inverse problem), addition of electrodynamic metric confirms the neural basis of fcMRI measurements. Finally, we show that multiple MEG based FC metrics allow the potential to move beyond what is possible using fcMRI, and investigate the nature of electrodynamic connectivity. Our results extend those from previous studies and add weight to the argument that neural oscillations are intimately related to functional connectivity and the BOLD response.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a9ed0d861c065adf7a503d6343f12b7485146e86

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Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, unknown): “…electrophysiology (Foster et al., 2015; Hacker et al., 2017; Lu et al., 2007; L. Wang, Saalmann, Pinsk, Arcaro, & Kastner, 2012) and MEG (Baker et al., 2014; Brookes et al., 2011; Hipp, Hawellek, Corbetta, Siegel, & Engel, 2012; Hipp & Siegel, Network Neuroscience 2015; Houck et al., 2017).”
Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, unknown): “…measured functional connectivity (Nunez et al., 1997), or using such estimates to derive a statistical threshold for the measured interactions (e.g. Brookes et al., 2011a); ii) the development of functional connectivity estimators that are insensitive to these confounds of field spread and volume…”
Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage (2012, methodology): “Two dipolar sources were simulated, located in the left and right primary motor cortices.”
The Human Connectome Project: A data acquisition perspective (2012, unknown): “Delineation of MEG resting state networks based on beamformer techniques (Brookes et al., 2011) will also be investigated.”
Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, unknown): “Notably, these amplitude modulations yield large-scale functional networks that correspond quantitatively to the resting-state networks derived from rs-fMRI (Brookes et al., 2011b).”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “Such methods have only recently become available bringing insightful information for the investigation of resting-state FC occurring at faster time-scales (Brookes et al., 2011a, Brookes et al., 2011b, Hipp et al., 2012, Engel et al., 2013, Marzetti et al., 2013, Hipp and Siegel, 2015).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “We note however, that graph theory can be used to characterize data acquired using other modalities, such as EEG/MEG (Stam, 2004a; Bassett, et al., 2006b; Stam, et al., 2007b; Rubinov, Knock, et al., 2009; Brookes, et al., 2011; Hipp, et al., 2011; Kitzbichler, et al., 2011; Zalesky, Cocchi, et al.,”
A critical assessment of connectivity measures for EEG data: A simulation study (2013, background): “…reconstruction A number of studies have investigated the (effective or functional) connectivity of source estimates obtained from linear inverse imaging (Astolfi et al., 2006; Babiloni et al., 2005; Gow et al., 2008) or beamforming (Brookes et al., 2011; Martino et al., 2011; Wibral et al., 2011).”
The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP) hypothesis (2013, methodology): “Testing this idea will likely require a combination of neuroimaging methods sensitive to temporal and spatial patterns of brain activity, such as MEG and fMRI, as well as techniques for describing directed information flow between brain regions (e.g., Brookes et al., 2011).”
Investigating the electrophysiological basis of resting state networks using magnetoencephalography (2011, methodology): “(10) used seed-based envelope correlation in conjunction with beamformer spatial filtering methods to show interhemispheric motor cortex connectivity in source space.”
Revolutionizing Pediatric Neurophysiology With Magnetoencephalography (2026, unknown): “…that can be examined both locally (e.g., alpha- to-gamma phase-amplitude coupling within a specific brain area or inter-region connectivity) and globally (e.g., frequency-based connectivity and graph-theory analyses) (Baillet 2017; Wilson et al. 2016; Brookes et al. 2016, 2011; Hunt et al. 2019).”
Characterising the spatial and oscillatory unfolding of Theory of Mind in adults using fMRI and MEG (2022, methodology): “Amplitude envelopes were computed using the absolute value of the Hilbert transform (Brookes et al., 2011; Hipp et al., 2012).”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, background): “While source reconstruction partially addresses this problem, it does not eliminate it entirely[82], and it additionally requires many parameter choices and has potentially confounding effects on estimates of functional connectivity [12, 22, 54].”