On network derivation, classification, and visualization: a response to Habeck and Moeller

Abstract

In the decade and a half since Biswal's fortuitous discovery of spontaneous correlations in functional imaging data, the field of functional connectivity (FC) has seen exponential growth resulting in the identification of widely-replicated intrinsic networks and the innovation of novel analytic methods with the promise of diagnostic application. As such a young field undergoing rapid change, we have yet to converge upon a desired and needed set of standards. In this issue, Habeck and Moeller begin a dialogue for developing best practices by providing four criticisms with respect to FC estimation methods, interpretation of FC networks, assessment of FC network features in classifying sub-populations, and network visualization. Here, we respond to Habeck and Moeller and provide our own perspective on the concerns raised in the hope that the neuroimaging field will benefit from this discussion.

Figure 1

A demonstration that the default mode network can be estimated regardless of experimental paradigm (auditory oddball task or resting-state) or analysis method (GLM, ICA, or seed-based). For seed-based analysis, the seed location is in posterior cingualte cortex (MNI=[−6, −52, 43]). For both ICA decompositions, the model order is 20. For GLM results, we display only negative activations to a novel–standard contrast.

Figure 2

From a seed voxel “A”, correlations with two arbitrary voxels “B” (r₁ = corr(A,B)) and “C” (r₂ = corr(A,C)) are known, however the correlation between “B” and “C” is not. We plot the minimum possible value for r₃ = corr(B,C). Note that if r₁×r₂ ≥ 0.5 (above the bold arc), then r₃ ≥ 0.

Figure 3

Balanced accuracy rates for out-of-sample prediction of age (young/old, left) and diagnosis (healthy/schizophrenia, right). The distribution of accuracy over 100 training/testing iterations is depicted with a violin pot and overlayed boxplot.