Neurophysiological responses to emotional faces predict dynamic fluctuations in affect in adolescents

The ability to accurately identify and interpret others' emotions is critical for social and emotional functioning during adolescence. Indeed, previous research has identified that laboratory-based indices of facial emotion recognition and engagement with emotional faces predict adolescent mood states. Whether socioemotional information processing relates to real-world affective dynamics using an ecologically sensitive approach, however, has rarely been assessed. In the present study, adolescents (N = 62; ages 13-18) completed a Facial Recognition Task, including happy, angry, and sad stimuli, while EEG data were acquired. Participants also provided ecological momentary assessment (EMA) data probing their current level of happiness, anger, and sadness for 1-week, resulting in indices of emotion (mean-level, inertia, instability). Analyses focused on relations between (1) accuracy for and (2) prolonged engagement with (LPP) emotional faces and EMA-reported emotions. Greater prolonged engagement with happy faces was related to less resistance to changes in happiness (i.e., less happiness inertia), whereas greater prolonged engagement with angry faces associated with more resistance to changes in anger (i.e., greater anger inertia). Results suggest that socioemotional processes captured by laboratory measures have real-world implications for adolescent affective states and highlight potentially actionable targets for novel treatment approaches (e.g., just-in-time interventions). Future studies should continue to assess relations among socioemotional informational processes and dynamic fluctuations in adolescent affective states.

Capturing mood dynamics through adolescent smartphone social communication

Most adolescents with depression remain undiagnosed and untreated-missed opportunities that are costly from both personal and public health perspectives. A promising approach to detecting adolescent depression in real-time and at a large scale is through their social communication on the smartphone (e.g., text messages, social media posts). Past research has shown that language from online social communication reliably indicates interindividual differences in depression. To move toward detecting the emergence of depression symptoms intraindividually, the present study tested whether sentiment (i.e., words connoting positive and negative affect) from smartphone social communication prospectively predicted daily mood fluctuations in 83 adolescents (Mage = 16.49, 73.5% female) with a wide range of depression severity. Participants completed daily mood ratings across a 90-day period, during which 354,278 messages were passively collected from social communication apps. Greater positive sentiment (i.e., more positive weighted composite valence score and a greater proportion of words expressing positive sentiment) predicted more positive next-day mood, controlling for previous-day mood. Moreover, greater proportions of positive and negative sentiment were, respectively, associated with lower anhedonia and greater dysphoria symptoms measured at baseline. Exploratory analyses of nonaffective linguistic features showed that greater use of social engagement words (e.g., friends and affiliation) and emojis (primarily consisting of hearts) predicted more positive changes in mood. Collectively, findings suggest that language from smartphone social communication can detect mood fluctuations in adolescents, laying the foundation for language-based tools to identify periods of heightened depression risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Electrophysiological evidence of mal-adaptation to error in remitted depression

Identifying risk markers for major depressive disorder (MDD) that persist into remission is key to address MDD's high rate of recurrence. Central to MDD recurrence are the disorder's negative information processing biases, such as heightened responses to errors, which may subsequently impair abilities to monitor performance and adjust behaviors based on environmental demands. However, little is known regarding the neurophysiological correlates of post-error adaptation in depression. The current study investigated event-related potentials (ERPs) and behavioral performance following errors from a flanker task in 58 participants with remitted MDD (rMDD) and 118 healthy controls (HC). Specifically, using trial-level data, we tested: (a) the impact of errors on response-locked ERPs of the current and post-error trials (error-related negativity [ERN] and correct response negativity [CRN]) and (b) longer-term adaptation to errors (ERN/CRN) over the course of the task. Compared to HC, rMDD participants showed a larger ERN to the current trial and smaller habituation in ERN over time. On trials immediately following errors, rMDD participants showed slower reaction times that were predicted by the previous-trial ERN amplitude but comparable accuracy to HC, suggesting a deficient ability to disengage from errors and/or a compensatory effort to mitigate accuracy decrements. Critically, this pattern of responding: (a) was concurrently associated with greater levels of anhedonia symptoms, more severe MDD history, and interpersonal impairment (but lower impairment in life activities) and (b) predicted more anhedonia symptoms at one-year follow-up. Collectively, a hyperactive performance monitoring system may be a useful risk marker for future MDD recurrence.