The past decade has been marked with a proliferation of community detection algorithms that aim to organize nodes (e.g., individuals, brain regions, variables) into modular structures that indicate subgroups, clusters, or communities. Motivated by the emergence of big data across many fields of inquiry, these methodological developments have primarily focused on the detection of communities of nodes from matrices that are very large. However, it remains unknown if the algorithms can reliably detect communities in smaller graph sizes (i.e., 1000 nodes and fewer) which are commonly used in brain research. More importantly, these algorithms have predominantly been tested only on binary or sparse count matrices and it remains unclear the degree to which the algorithms can recover community structure for different types of matrices, such as the often used cross-correlation matrices representing functional connectivity across predefined brain regions. Of the publicly available approaches for weighted graphs that can detect communities in graph sizes of at least 1000, prior research has demonstrated that Newman's spectral approach (i.e., Leading Eigenvalue), Walktrap, Fast Modularity, the Louvain method (i.e., multilevel community method), Label Propagation, and Infomap all recover communities exceptionally well in certain circumstances. The purpose of the present Monte Carlo simulation study is to test these methods across a large number of conditions, including varied graph sizes and types of matrix (sparse count, correlation, and reflected Euclidean distance), to identify which algorithm is optimal for specific types of data matrices. The results indicate that when the data are in the form of sparse count networks (such as those seen in diffusion tensor imaging), Label Propagation and Walktrap surfaced as the most reliable methods for community detection. For dense, weighted networks such as correlation matrices capturing functional connectivity, Walktrap consistently outperformed the other approaches for recovering communities.

Depression is characterized by a pattern of maladaptive emotion regulation. Recently, researchers have begun to focus on associations between depression and two positive affect regulation strategies: savoring and dampening. Savoring, or upregulation of positive affect, is positively associated with well-being and negatively associated with depression, whereas dampening, or downregulation of positive affect, is positively associated with depression, anhedonia, and negative affect. To date, no research has examined whether savoring or dampening can affect neurophysiological reactivity to reward, which previous research has shown is associated with symptoms of depression. Here, we examined associations between psychophysiological reward processing-primarily captured by the Reward Positivity (RewP), an eventrelated potential (ERP) deflection elicited by feedback indicating reward (vs. nonreward)-positive affect regulation strategies, and symptoms of depression. One hundred undergraduates completed questionnaires assessing affect, emotion regulation, and depressive symptoms and completed a computerized guessing task, once before and again after being randomly assigned to emotion-regulation strategy conditions. Results indicate that (a) the relationship between RewP amplitude and depressive symptoms may, in part, depend upon positive affect regulation strategies and (b) the RewP elicited by reward appears sensitive to a savoring intervention. These findings suggest that mitigating depressive symptoms in emerging adults may depend on both top-down (i.e., savoring) and bottom-up (i.e., RewP) forms of positive affect regulation and have important implications for clinical prevention and intervention efforts for depressive symptoms and disorder.

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Objective: Alcohol Use Disorder (AUD) has traditionally been viewed as a chronic, progressive, relapsing disorder (Jellinek, 1960; National Institute on Drug Abuse, 2018). However, little is known about the course of individual AUD criteria. To the extent that individual symptoms represent the focus of some treatments (e.g., withdrawal, craving), understanding the course of specific symptoms, and individual differences in symptom course, can inform treatment efforts and future research directions.Method: The current study examined 34,653 participants form Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC; Grant, Moore, & Kaplan, 2003; Grant, Kaplan, and Stinson, 2005), using logistic regression to analyze the extent to which AUD symptom course is predicted by heavy alcohol consumption, family history of alcoholism, and lifetime diagnosis of Conduct Disorder. Results: The course of all AUD symptoms was significantly influenced by all four external criteria, with the magnitude of the prediction varying across different symptoms and different aspects of course. Conclusion: The strength of the relationship appeared to be related to the theoretical proximity of a given predictor to AUD symptomatology, with heavy drinking being the strongest and family history of AUD being the weakest. The course of all AUD symptoms was strongly associated with the prevalence of the given symptom in the overall sample. Future work should include examining the interchangeability of AUD symptoms and considering heavy alcohol consumption as a criterion for AUD diagnosis.