One can point to a variety of historical milestones for gender equality in STEM (science, technology, engineering, and mathematics), however, the practical effects are gradual and ongoing. It is important to quantify gender differences in subdomains of scientific work in order to detect potential biases and to monitor progress. In this work, we studied the relevance of gender in scientific collaboration patterns in the Institute for Operations Research and the Management Sciences (INFORMS), a professional organization with sixteen peer-reviewed journals. We constructed a large temporal bipartite network between authors and publications, using the organization's publication data from 1952 to 2016, and augmented the author nodes with gender labels. We characterized differences in several basic statistics of this network over time, highlighting how they change with respect to relevant historical events. We found a steady increase in participation by women (e.g., fraction of authorships by women and of new women authors) starting ∼1980. However, women still comprise less than 25% of the INFORMS society, and are additionally underrepresented among authors with many publications. Finally, we describe a methodology for quantifying differences in the role that authorships by women and men play in the overall connectivity of the network. Specifically, we propose a degree-preserving temporal and geometric null model with emergent communities. We use two measures of edge importance related to diffusion throughout the network, namely effective resistance and edge contraction importance to quantify gender differences in collaboration patterns that go beyond differences in local statistics.
How might one test the hypothesis that graphs were sampled from the same distribution? Here, we compare two statistical tests that address this question. The first uses the observed subgraph densities themselves as estimates of those of the underlying distribution. The second test uses a new approach that converts these subgraph densities into estimates of the graph cumulants of the distribution. We demonstrate -via theory, simulation, and application to real data -the superior statistical power of using graph cumulants.
To make sensible inferences about neural activation from fMRI data it is important to accurately model the hemodynamic response function (HRF), i.e., the hemodynamic response evoked by a punctuate neural event. HRF models have been derived for sensory areas, where it is relatively clear what events cause a neural impulse response.However, this is obviously harder to do for higher order cortices such as prefrontal areas. Therefore, one HRF model is commonly used for analyzing activity throughout the brain, despite the fact that hemodynamics are known to vary across regions. For instance, several fMRI studies use a canonical HRF to analyze ventral striatum (VS) activity where converging evidence indicates that reward prediction error signals drive neural activity. However, the VS is a target of prominent dopaminergic projections, known to modulate vasculature and affect BOLD activity, suggesting that the HRF in the VS may be especially different from those in other brain areas. To address this, we use data from an experiment focused on learning from prediction-error signals to derive a VS-specific HRF model (VS-HRF). We show that this new VS-HRF increases statistical power in model comparison. Our result is of particular relevance to studies comparing computational models of learning and/or decision making in the VS, and for connectivity analyses, where the use of an (even slightly) inaccurate HRF model can lead to erroneous conclusions. More broadly, our study highlights the importance of the choice of HRF model in determining the significance of the results obtained in classical univariate fMRI analysis.
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