The thrill of victory: Savoring positive affect, psychophysiological reward processing, and symptoms of depression.

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 event-related 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. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Monte Carlo Evaluation of Weighted Community Detection Algorithms.

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.