Patterns of emotion-network dynamics are orthogonal to mood disorder status: An experience sampling investigation.

Individuals differ markedly in how they experience the ebb and flow of emotions. In this study, we used daily experience sampling to examine whether these differences reflect the nature and presence of mood disorders or whether they can better be characterized as distinct dynamic emotion profiles that cut-across diagnostic boundaries. We followed 105 individuals in 2019-2020 with diagnoses of major depression, remitted major depression, bipolar disorder, or no history of disorder, over 14 days (n = 6,543 experience-sampling assessments). We applied group iterative multiple model estimation, using both diagnosis-based and data-driven methods to investigate similarities in unfolding within-person emotion-network time-courses. Results did not support diagnosis-based subgroupings but rather revealed two significant data-driven subgroups based on dynamic emotion patterns. These data-driven subgroups did not significantly differ in terms of clinical features or demographics, but did differ on key emotion metrics-instability, granularity, and inertia. These data-driven subgroupings, agnostic to diagnostic status, provide insights into the nature of idiographic emotion-network dynamics that cut-across clinical diagnostic divisions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multi-unit relations among neural, self-report, and behavioral correlates of emotion regulation in comorbid depression and obesity.

Depression is a leading cause of disability and is commonly comorbid with obesity. Emotion regulation is impaired in both depression and obesity. In this study, we aimed to explicate multi-unit relations among brain connectivity, behavior, and self-reported trait measures related to emotion regulation in a comorbid depressed and obese sample (N = 77). Brain connectivity was quantified as fractional anisotropy (FA) of the uncinate fasciculi, a white matter tract implicated in emotion regulation and in depression. Use of emotion regulation strategies was assessed using the Emotion Regulation Questionnaire (ERQ). We additionally measured reaction times to identifying negative emotions, a behavioral index of depression-related emotion processing biases. We found that greater right uncinate fasciculus FA was related to greater usage of suppression (r = 0.27, p = 0.022), and to faster reaction times to identifying negative emotions, particularly sadness (r = -0.30, p = 0.010) and fear (r = -0.35, p = 0.003). These findings suggest that FA of the right uncinate fasciculus corresponds to maladaptive emotion regulation strategies and emotion processing biases that are relevant to co-occurring depression and obesity. Interventions that consider these multi-unit associations may prove to be useful for subtyping and improving clinical outcomes for comorbid depression and obesity.

Neural detection of socially valued community members.

As people form social groups, they benefit from being able to detect socially valuable community members-individuals who act prosocially, support others, and form strong relationships. Multidisciplinary evidence demonstrates that people indeed track others' social value, but the mechanisms through which such detection occurs remain unclear. Here, we combine social network and neuroimaging analyses to examine this process. We mapped social networks in two freshman dormitories (n = 97), identifying how often individuals were nominated as socially valuable (i.e., sources of friendship, empathy, and support) by their peers. Next, we scanned a subset of dorm members ("perceivers"; n = 50) as they passively viewed photos of their dormmates ("targets"). Perceiver brain activity in regions associated with mentalizing and value computation differentiated between highly valued targets and other community members but did not differentiate between targets with middle versus low levels of social value. Cross-validation analysis revealed that brain activity from novel perceivers could be used to accurately predict whether targets viewed by those perceivers were high in social value or not. These results held even after controlling for perceivers' own ratings of closeness to targets, and even though perceivers were not directed to focus on targets' social value. Overall, these findings demonstrate that individuals spontaneously monitor people identified as sources of strong connection in the broader community.

The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model.

Precision medicine models for personalizing achieving sustained behavior change are largely outside of current clinical practice. Yet, changing self-regulatory behaviors is fundamental to the self-management of complex lifestyle-related chronic conditions such as depression and obesity - two top contributors to the global burden of disease and disability. To optimize treatments and address these burdens, behavior change and self-regulation must be better understood in relation to their neurobiological underpinnings. Here, we present the conceptual framework and protocol for a novel study, "Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes (ENGAGE)". The ENGAGE study integrates neuroscience with behavioral science to better understand the self-regulation related mechanisms of behavior change for improving mood and weight outcomes among adults with comorbid depression and obesity. We collect assays of three self-regulation targets (emotion, cognition, and self-reflection) in multiple settings: neuroimaging and behavioral lab-based measures, virtual reality, and passive smartphone sampling. By connecting human neuroscience and behavioral science in this manner within the ENGAGE study, we develop a prototype for elucidating the underlying self-regulation mechanisms of behavior change outcomes and their application in optimizing intervention strategies for multiple chronic diseases.