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A symmetric multivariate leakage correction for MEG connectomes.

Colclough GL, Brookes MJ, Smith SM, Woolrich MW - Neuroimage (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK; University of Oxford, Dept. Engineering Sciences, Parks Rd., Oxford, UK; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK. Electronic address: giles.colclough@magd.ox.ac.uk.

No MeSH data available.


Related in: MedlinePlus

Alpha-band (8–13 Hz) resting-state partial correlations, with a comparison between orthogonalisation methods. Partial correlation, inferred without any regularisation to sparsity, yields denser connectomes (c.f. Fig. 6) and may be noisier than regularised estimates. In this analysis, there is little to differentiate the two leakage correction methods. Edges are shown as joining centres of mass of each ROI, with the colour scale and edge thicknesses indicating the group-level inference on regularised partial correlations between power envelopes of ROI time-courses. Only edges above the 5% false discovery rate thresholds are shown (z = 3.2, 3.4 and 3.3 respectively for the symmetrically-corrected, pairwise-corrected and uncorrected networks).
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f0035: Alpha-band (8–13 Hz) resting-state partial correlations, with a comparison between orthogonalisation methods. Partial correlation, inferred without any regularisation to sparsity, yields denser connectomes (c.f. Fig. 6) and may be noisier than regularised estimates. In this analysis, there is little to differentiate the two leakage correction methods. Edges are shown as joining centres of mass of each ROI, with the colour scale and edge thicknesses indicating the group-level inference on regularised partial correlations between power envelopes of ROI time-courses. Only edges above the 5% false discovery rate thresholds are shown (z = 3.2, 3.4 and 3.3 respectively for the symmetrically-corrected, pairwise-corrected and uncorrected networks).

Mentions: Lastly, in Fig. 7 we present, for comparison, the partial correlations between all 38 ROIs, thresholded at a 5% FDR. Estimating direct network connections using partial correlations without regularisation is noisy; here we merely observe the similarity between partial correlations estimated after the pair-wise and symmetric orthogonalisation processes. Full correlation, partial and regularised partial correlation matrices in the alpha and beta-bands for this dataset are available in the supplementary information.


A symmetric multivariate leakage correction for MEG connectomes.

Colclough GL, Brookes MJ, Smith SM, Woolrich MW - Neuroimage (2015)

Alpha-band (8–13 Hz) resting-state partial correlations, with a comparison between orthogonalisation methods. Partial correlation, inferred without any regularisation to sparsity, yields denser connectomes (c.f. Fig. 6) and may be noisier than regularised estimates. In this analysis, there is little to differentiate the two leakage correction methods. Edges are shown as joining centres of mass of each ROI, with the colour scale and edge thicknesses indicating the group-level inference on regularised partial correlations between power envelopes of ROI time-courses. Only edges above the 5% false discovery rate thresholds are shown (z = 3.2, 3.4 and 3.3 respectively for the symmetrically-corrected, pairwise-corrected and uncorrected networks).
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528074&req=5

f0035: Alpha-band (8–13 Hz) resting-state partial correlations, with a comparison between orthogonalisation methods. Partial correlation, inferred without any regularisation to sparsity, yields denser connectomes (c.f. Fig. 6) and may be noisier than regularised estimates. In this analysis, there is little to differentiate the two leakage correction methods. Edges are shown as joining centres of mass of each ROI, with the colour scale and edge thicknesses indicating the group-level inference on regularised partial correlations between power envelopes of ROI time-courses. Only edges above the 5% false discovery rate thresholds are shown (z = 3.2, 3.4 and 3.3 respectively for the symmetrically-corrected, pairwise-corrected and uncorrected networks).
Mentions: Lastly, in Fig. 7 we present, for comparison, the partial correlations between all 38 ROIs, thresholded at a 5% FDR. Estimating direct network connections using partial correlations without regularisation is noisy; here we merely observe the similarity between partial correlations estimated after the pair-wise and symmetric orthogonalisation processes. Full correlation, partial and regularised partial correlation matrices in the alpha and beta-bands for this dataset are available in the supplementary information.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK; University of Oxford, Dept. Engineering Sciences, Parks Rd., Oxford, UK; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK. Electronic address: giles.colclough@magd.ox.ac.uk.

No MeSH data available.


Related in: MedlinePlus