From nodes to networks: How methods for defining nodes influence inferences regarding network interactions

Functional connectivity (FC) analysis of fMRI data typically rests on prior identification of network nodes from activation profiles. We compared Activation Likelihood Estimate (ALE) and the Experimentally Derived Estimate (EDE) approaches to network node identification and functional inference for both verbal and visual forms of working memory. ALE arrives at canonical activation maxima that are assumed to reliably represent peaks of brain activity underlying a psychological process (e.g., working memory). By comparison, EDEs of activation maxima are typically derived from individual participant data, and are thus sensitive to individual participant activation profiles. Here, nodes were localized by both ALE and EDE methods for each participant, and subsequently extracted time series were compared using connectivity analysis. Two sets of significance tests were performed: (1) correlations computed between nodal time series of each method were compared, and (2) correlations computed between network edges (functional connections) of each network node pair were compared. Large proportions of edge correlations significantly differed between methods. ALE effectively summarizes working memory network node locations across studies and subjects, but the sensitivity to individual functional loci suggest that EDE methods provide individualized estimates of network connectivity. We suggest that a hybrid method incorporating both ALE and EDE is optimal for network inference.