Abstract
Functional connectivity analyses for task-based fMRI data are generally preceded by methods for identification of network nodes. As there is no general canonical approach to identifying network nodes, different identification techniques may exert different effects on inferences drawn regarding functional network properties. Here, we compared the impact of two different node identification techniques on estimates of local node importance (based on Degree Centrality, DC) in two working memory domains: verbal and visual. The two techniques compared were the commonly used Activation Likelihood Estimate (ALE) technique (with node locations based on data aggregation), against a hybrid technique, Experimentally Derived Estimation (EDE). In the latter, ALE was first used to isolate regions of interest; then participant-specific nodes were identified based on individual-participant local maxima. Time series were extracted at each node for each dataset and subsequently used in functional connectivity analysis to: (1) assess the impact of choice of technique on estimates of DC, and (2) assess the difference between the techniques in the ranking of nodes (based on DC) in the networks they produced. In both domains, we found a significant Technique by Node interaction, signifying that the two techniques yielded networks with different DC estimates. Moreover, for the majority of participants, node rankings were uncorrelated between the two techniques (85% for the verbal working memory task and 92% for the visual working memory task). The latter effect is direct evidence that the identification techniques produced different rankings at the level of individual participants. These results indicate that node choice in task-based fMRI data exerts downstream effects that will impact interpretation and reverse inference regarding brain function.
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Acknowledgements
Preparation of this work was supported by the Charles H. Gershenson Distinguished Faculty Fellowship from Wayne State University, the Lyckaki-Young Fund from the State of Michigan, the Prechter Family Bipolar Foundation, the Children’s Hospital of Michigan Foundation, the Children’s Research Center of Michigan, the Cohen Neuroscience Endowment, the Dorsey Neuroscience Endowment, and the National Institute of Mental Health (MH 59299).
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429_2020_2061_MOESM1_ESM.eps
Supplementary Figure 1. The figure depicts additional analyses investigating the role of Euclidean distance D (x axis of all graphs), in predicting differences (EDE−ALE) in estimates of Degree Centrality (left graphs) and Node Ranking (right graphs). Each point in each scatterplot represents the difference in each measure for each participant. The data are presented for a) the verbal n-back (Degree Centrality Difference: r2=0.013, F1,321=5.21, p=0.02: Nodal Ranking Difference: r2=0.001, F1,321=.52, p=0.47) and b) the visual n-back (Degree Centrality Difference: r2=0.026, F1,1636=44.9, p<0.001: Nodal Ranking Difference: r2=0.055, F1,1636=96.7, p<0.001). As noted in the text although the analyses were overpowered to detect significant correlations, D, was predictive of only a small percentage of variance (resulting in small effect sizes; all β≤.03) associated with the difference measures
429_2020_2061_MOESM2_ESM.eps
Supplementary Figure 2. The figures depict the mean Degree Centrality estimates for the three techniques, EDE2, ALE and EDE0. The data are shown for a) the verbal n-back and b) the visual n-back. Error bars are the standard error associated with the residuals obtained from each ANOVA. The main effect of Technique was significant in each analysis and pair-wise differences are denoted by the symbols (see Text for statistical information and pair-wise comparisons). In addition, the following effects were significant: a) Verbal n-back: Main effect of Regions, F3,860=15.94, p<0.001, MSe=85.13; Techniques x Nodes(Regions) interaction, F16,860=1.92, p=0.02, MSe=11.79; b) visual n-back: Main effect of Nodes(Regions), F7,4844=15.94, p<0.001, MSe=40.86; Techniques x Nodes(Regions), F14,4844=15.94, p<0.001, MSe=34.13
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Falco, D., Chowdury, A., Rosenberg, D.R. et al. ALE meta-analysis, its role in node identification and the effects on estimates of local network organization. Brain Struct Funct 225, 1089–1102 (2020). https://doi.org/10.1007/s00429-020-02061-2
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DOI: https://doi.org/10.1007/s00429-020-02061-2