Abstract
Connectivity within the human connectome occurs between multiple neuronal systems—at small to very large spatial scales. Independent component analysis (ICA) is potentially a powerful tool to facilitate multi-scale analyses. However, ICA has yet to be fully evaluated at very low (10 or fewer) and ultra-high dimensionalities (200 or greater). The current investigation used data from the Human Connectome Project (HCP) to determine the following: (1) if larger networks, or meta-networks, are present at low dimensionality, (2) if nuisance sources increase with dimensionality, and (3) if ICA is prone to overfitting. Using bootstrap ICA, results suggested that, at very low dimensionality, ICA spatial maps consisted of Visual/Attention and Default/Control meta-networks. At fewer than 10 components, well-known networks such as the Somatomotor Network were absent from results. At high dimensionality, nuisance sources were present even in denoised high-quality data but were identifiable by correlation with tissue probability maps. Artifactual overfitting occurred to a minor degree at high dimensionalities. Basic summary statistics on spatial maps (maximum cluster size, maximum component weight, and average weight outside of maximum cluster) quickly and easily separated artifacts from gray matter sources. Lastly, by using weighted averages of bootstrap stability, even ultra-high dimensional ICA resulted in highly reproducible spatial maps. These results demonstrate how ICA can be applied in multi-scale analyses, reliably and accurately reproducing the hierarchy of meta-networks, large-scale networks, and subnetworks, thereby characterizing cortical connectivity across multiple spatial scales.
Highlights
Network models of neural processing used in neuroimaging are continually evolving and becoming increasingly sophisticated
When averaged across all components in a given model, mean Iq was above 0.9 up until ICA270 and remained at or above 0.88 until at least ICA300. These results suggest that unstable components are likely unavoidable, even at the low independent component analysis (ICA) model orders commonly used in fMRI
At the lowest model order, ICA2, results corroborated the combined Visual/Attention and Default/Control meta-networks observed in hierarchical clustering analyses (Meunier et al, 2009; FIGURE 6 | Bootstrap Stability Indices Iq and independent component analysis (ICA) Model Order K. (A) Unweighted bootstrap stability index Iq, plotted as sample quantiles in increments of 10, with the solid blue line showing median Iq
Summary
Network models of neural processing used in neuroimaging are continually evolving and becoming increasingly sophisticated. More recent work has identified subnetworks within these larger networks (Smith et al, 2009; Andrews-Hanna et al, 2010; Shirer et al, 2012) and even smaller regional parcellations (Glasser et al, 2016). This network topology, encompassing multiple spatial scales, has long been hypothesized as a fundamental architecture of cognitive neuroscience (Churchland and Sejnowski, 1988) and is the focus of active investigation (Park and Friston, 2013; Sporns, 2015; Betzel and Bassett, 2017; Eickhoff et al, 2018). When is a visual stimulus only processed within the primary visual cortex—on a relatively small spatial scale? When is it processed widely throughout the entire visual system—on a larger spatial scale? Under what circumstances will processing extend beyond the visual system, perhaps to encompass attention systems as well? While a simple line displayed on a screen may result in processing localized to a single circumscribed region, a complex visual recognition task may result in widespread engagement of occipitotemporal and occipitoparietal pathways (Kandel et al, 2000)
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