A symmetric multivariate leakage correction for MEG connectomes

By Giles L. Colclough, Matthew J. Brookes, Stephen M. Smith, Mark William Woolrich. NeuroImage, 2015

416 citations recorded by the source corpus.

Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d7858e3f24f9ef2a8bd29a426c8ffc3ccdf18452

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Citations in the corpus

How reliable are MEG resting-state connectivity metrics? (2016, methodology): “highlighting the associated problems (Palva and Palva, 2012; Colclough et al., 2015; Brookes et al., 2012; Hipp et al., 2012;Maldjian et al., 2014).”
Spectrally resolved fast transient brain states in electrophysiological data (2016, neutral): “Alternatively, we can orthogonalise the time courses prior to estimating envelope correlation (Brookes et al., 2011), (Hipp et al., 2012), (Colclough et al., 2015).”
Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity (2024, unknown): “Controlling how the signal of one source is affected by the signal of others has been shown to be the most crucial point for connectivity analysis (Colclough et al., 2015).”
Disrupted working memory event-related network dynamics in multiple sclerosis (2024, unknown): “The ROIs ’ time courses were orthogonalized by multivariate symmetric leakage correction to discard signal leakage across parcels 62 .”
Impaired activation of the prefrontal executive network during working memory processing in multiple sclerosis (2023, methodology): “33 Lastly, we applied the sign flipping algorithm, as proposed by Vidaurre et al 32 , to overcome the sign ambiguity issue affecting beamformed data.”