Abstract
Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.
Highlights
Resting state functional magnetic resonance imaging can be used to characterise the rich intrinsic functional organisation of the brain (Biswal et al, 1995)
Analytical approaches to functional connectivity can be broadly split into voxel-based methods and node-based methods (Bijsterbosch et al, 2017; Rubinov and Sporns, 2010)
Temporal network and spatial overlap matrix estimation in Independent Component Analysis (ICA) followed by dual regression We provide evidence that the negative association between node spatial map correlations and functional connectivity in Figure 3 could be the result of dual regression being used on spatial ICA maps that are incorrect, due to the assumption of spatial independence being wrong
Summary
Resting state functional magnetic resonance imaging (rfMRI) can be used to characterise the rich intrinsic functional organisation of the brain (Biswal et al, 1995). Analytical approaches to functional connectivity can be broadly split into voxel-based methods (deriving mapbased connectivity estimates to study the spatial organisation of networks) and node-based methods (approaches based on network-science that describe connectivity in terms of ‘edges’ between functional brain regions) (Bijsterbosch et al, 2017; Rubinov and Sporns, 2010). Our previous results revealed that node-based functional connectivity as normally estimated from rfMRI data is influenced by a mixture of spatial and temporal factors, with spatial information explaining up to 62% of interindividual variance.
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