Early adversity has been consistently linked to mental health outcomes, but the underlying pathways remain unclear. One previous study found an association between early adversity and trait emotional awareness (EA), which has itself been linked to health outcomes, but links to mental health were not explicitly examined. The aim of the current study was to test the hypothesis that the association between early adversity and health can be partially accounted for by differences in EA within a large student sample (n = 196). Participants completed measures of early adversity, EA, and current emotional functioning (i.e., depression, anxiety, somatization, positive/negative affect). Bayesian analyses found the most evidence for models with an interaction between sex and early adversity in predicting emotional functioning - revealing the expected negative relationship between early adversity and EA in females, but a positive relationship in males. Early adversity, but not EA, was associated with depression, anxiety, and implicit negative affect. Only explicit positive affect was associated with both early adversity and EA, and EA partially mediated the negative association between early adversity and positive affect. These results provide limited support for EA as a mediating pathway for the effects of early adversity on mental health.

UNASSIGNED: Sexual victimization (SV) is common among college women, with approximately half of those who have experienced SV meeting criteria for posttraumatic stress disorder (PTSD) within a year. Both SV and PTSD are associated with alcohol misuse among college women, often explained by the self-medication hypothesis. Existing literature focuses on overall PTSD severity rather than potential day-to-day fluctuations in specific symptoms, which might play a crucial role in understanding alcohol misuse risk. Studies also examine only same-day or next-day associations between PTSD and drinking, neglecting the potential for longer-term changes.
UNASSIGNED: This study explores the short-term longitudinal stability and time-lagged predictive dynamics of PTSD symptoms, affect, and drinking behavior among 174 female college heavy episodic drinkers over four weeks. Participants were categorized into three groups: those with a history of SV and PTSD (n = 77), women with SV but without PTSD (n = 59), and women without prior trauma history (n = 38) to be able to examine differences by trauma exposure, and PTSD. We compared the longitudinal stability of PTSD symptom networks, affect (arousal, positive affect, and negative affect), and drinking behavior across groups. Support vector regression determined which PTSD symptom networks and affect best predict drinking behavior at specific time lags within a 0-7 day range.
UNASSIGNED: The PTSD group showed higher longitudinal stability for PTSD symptom networks (adjusted ps <.049) and arousal (adjusted ps <.048), but lower stability for negative affect (adjusted p =.013) and drinking behavior, including alcohol cravings (adjusted p =.019) and consumption (adjusted ps =.012), compared to the comparison groups. This suggests individuals with PTSD have more stable symptoms and arousal levels but greater fluctuations in negative affect and alcohol-related behaviors. Secondary analysis revealed PTSD symptom networks optimally predicted alcohol cravings with a three-day time lag (r=.88, p <.001) and consumption with a four-day time lag (r=.82, p <.001).
UNASSIGNED: These findings challenge assumptions regarding immediate effects of PTSD and affect on drinking behavior and underscore the need for therapeutic approaches that consider longer-range effects. Future research should expand on these findings by incorporating longer-range assessments and exploring a broader range of symptom interactions.

Although mindreading is an important prerequisite for successful social interactions, the underlying mechanisms are still matter of debate. It is unclear, for example, if inferring others\' and own mental states are distinct processes or are based on a common mechanism. Using an affect-induction experimental set-up with an acoustic heart rate feedback that addresses affective mindreading in self and others, we investigated if non-autistic study participants relied on similar information for self- and other-directed mindreading. We assumed that due to altered mindreading capacities in autism, mainly individuals with low autistic traits would focus on additional sensory cues, such as heart rate, to infer their own and their gambling partner\'s affective states. Our analyses showed that the interpretation of a heart rate signal differed in self- and other-directed mindreading trials. This effect was modulated by autistic traits suggesting that individuals with higher autistic traits might not have interpreted the heart rate feedback for gambling partner ratings and differentiated less between self- and other-directed mindreading trials. We discuss these results in the context of a common mechanism underlying self- and other-directed mindreading and hypothesize that the weighting of internal and external sensory information might contribute to how we make sense of our and others\' mental states.

BACKGROUND: Poor mood states pose the most frequent mental health, creating a considerable burden to global public health. Sedentary behavior is an essential factor affecting mood states, however, previous measures to reduce sedentary time in Chinese young adults have focused only on increasing physical activity (PA). Sedentary, PA, and sleep make up a person\'s day from the standpoint of time use. It is not known whether reallocating sedentary time to different types of PA (e.g. daily PA and structured PA) or sleep during an epidemic has an effect on mood states. Therefore, this study aimed to examine the association between replacing sedentary time with different types of PA or sleep during the pandemic and the mood states of Chinese young adults and to further examine whether this association varies across sleep populations and units of replacement time.
METHODS: 3,579 young adults aged 18 to 25 years living in China and self-isolating at home during the COVID-19 outbreak were invited to complete an online questionnaire between February from 23 to 29, 2020. Subjects\' PA, sedentary time, and mood states were assessed using the International Physical Activity Questionnaire and the Chinese version of the Profile of Mood States, respectively. Participants also reported sleep duration and some sociodemographic characteristics. Participants were divided into short sleepers (< 7 h/d), normal sleepers (7-9 h/d), and long sleepers (> 9 h/d) based upon their reported sleep duration. Relevant data were analyzed using Pearson correlation analysis and isotemporal substitution model (ISM).
RESULTS: Sedentary time was negatively associated with mood states in Chinese young adults during the pandemic (r = 0.140) and correlated strongest among short sleepers (r = 0.203). Substitution of sedentary time with structured PA was associated with good mood states (β=-0.28, 95% CI: -0.49, -0.08). Additionally, substituting sedentary time with daily PA (e.g. occupational PA, household PA) was also associated with good mood states among normal sleepers (β=-0.24, 95% CI: -0.46, -0.02). The substitution of sedentary time with sleep could bring mood benefits (β=-0.35, 95% CI: -0.47, -0.23). This benefit was particularly prominent among short sleepers. Furthermore, for long sleepers, replacing sedentary time with sleep time also resulted in significant mood benefits (β=-0.41, 95% CI: -0.69, -0.12). The longer the duration of replacing sedentary behavior with different types of PA or sleep, the greater the mood benefits.
CONCLUSIONS: A reallocation of as little as 10 min/day of sedentary time to different types of PA or sleep is beneficial for the mood states of young adults. The longer the reallocation, the greater the benefit. Our results demonstrate a feasible and practical behavior alternative for improving mood states of Chinese young adults.

UNASSIGNED: Dysregulated reward processing and mood instability are core features of bipolar disorder that have largely been considered separately, with contradictory findings. We sought to test a mechanistic account that emphasizes an excessive tendency in bipolar disorder to enter recursive cycles in which reward perception is biased by signals that the environment may be changing for the better or worse.
UNASSIGNED: Participants completed a probabilistic reward task with functional magnetic resonance imaging. Using an influential computational model, we ascertained whether participants with bipolar disorder (n = 21) showed greater striatal tracking of momentum-biased reward prediction errors (RPEs) than matched control participants (n = 21). We conducted psychophysiological interaction analyses to quantify the degree to which each group modulated functional connectivity between the ventral striatum and left anterior insula in response to fluctuations in momentum.
UNASSIGNED: In participants with bipolar disorder, but not control participants, the momentum-biased RPE model accounted for significant additional variance in striatal activity beyond a standard model of veridical RPEs. Compared with control participants, participants with bipolar disorder exhibited lower insular-striatal functional connectivity modulated by momentum-biased RPEs, an effect that was more pronounced as a function of current manic symptoms.
UNASSIGNED: Consistent with existing theory, we found evidence that bipolar disorder is associated with a tendency for momentum to excessively bias striatal tracking of RPEs. We identified impaired insular-striatal connectivity as a possible locus for this propensity. We argue that computational psychiatric approaches that examine momentary shifts in reward and mood dynamics have strong potential for yielding new mechanistic insights and intervention targets.
Bipolar disorder is characterized by extremes in mood and dysregulated reward processing. Moningka and Mason evaluated a neurocomputational model in which mood disturbances arise from an excessive tendency for momentum over recent experiences to bias reward perception. Using model-based functional MRI, the authors found that in contrast to matched control participants, participants with bipolar disorder exhibited fluctuations in reward-related neural responses that are modulated by momentum. They discuss this excessive neural tracking of momentum as one of the mechanisms that may underlie the propensity to enter recursive mood cycles in bipolar disorder.

The role of human microbiota in mental health and the underlying mechanisms of psychobiotics, which can modulate mood and behavior through the microbiota-gut-brain axis, has been a focus of scientific scrutiny. This work presents a bibliometric analysis to uncover research trends and insights in psychobiotics literature. The Clarivate Analytics Web of Science database served as the source for articles and reviews on psychobiotics spanning the years 2012 to 2023. Bibliometric network visualization and graphing were conducted using VOSviewer, Microsoft Excel for Windows 10, and Datawrapper software. A total of 348 publications were included, and it has been determined that the number of publications and citations shows an increasing trend from 2012 to 2023. The most active authors on psychobiotics, in order, were Dinan TG, Cryan JF, and Tsai YC. The most active organizations have been identified as University College Cork, National Yang Ming Chiao Tung University, and Bened Biomedical Co. Ltd. The most active countries in psychobiotic research were China, Ireland, and United States of America, while the most active journals were Nutrients, International Journal of Molecular Sciences, and Probiotics and Antimicrobial Proteins. The most commonly used keywords were \"psychobiotics,\" \"probiotics,\" and \"gut-brain axis.\" This bibliometric analysis has revealed the growing academic interest in psychobiotics, indicating that the relationship between gut microbiota and mental health will increasingly be supported by scientific evidence in the years ahead.

Researchers, who play a crucial role in knowledge production, deal with various emotions in their challenging work environment. Their personality might affect how well they manage their emotions, but their moods could help counteract these effects. This study aims to investigate whether researchers\' moods influence the connection between their personality and emotional intelligence. 7,463 Spanish researchers replied to an online survey. Responses analysed through partial least squares structural equation modelling show significant positive relationships between the big five personality traits (openness to experience, conscientiousness, extraversion, agreeableness and emotional stability) and emotional intelligence. In addition, positive affect positively mediates the relationships between each of the personality traits and emotional intelligence, and negative affect mediates the same relationships but negatively. The importance of managing emotional states to regulate emotional experiences in the work of researchers is discussed.

BACKGROUND: Cardiovascular morbidity and mortality are high in people with serious mental illness (SMI). This problem is mediated, at least in part, by metabolic side effects of second-generation antipsychotics (SGAs) and by unhealthy lifestyle behaviors. We asked whether oral glucose tolerance testing (oGTT) or hemoglobin A1c (HbA1c) is superior in identifying people with SMI at high cardiometabolic risk and whether this risk is shaped by mood, cognition, or lifestyle habits.
METHODS: We evaluated 40 patients with schizophrenia, schizoaffective, or bipolar disorder receiving SGAs by oGTT, HbA1c, comprehensive metabolic and lipid panels, and CRP. Mood was assessed using the Patient Health Questionnaire (PHQ-9), and cognition was assessed using the Saint Louis University Mental Status examination. Diet was assessed using the UK Diabetes and Diet Questionnaire (UKDDQ), and physical activity was assessed using daily step counts.
RESULTS: Most patients had prediabetes (preDM) or diabetes mellitus (DM), 72.5% by oGTT, and 52.5% by HbA1c criteria. Pulse rates and insulin resistance indices (Homeostatic Model Assessment of Insulin Resistance, HOMA IR; Matsuda) were significantly different between patients classified as normal or with preDM/DM, using either oGTT or HbA1c criteria. Patients with preDM/DM by HbA1c but not oGTT criteria also had higher waist/hip ratios, triglyceride, and CRP levels (p<0.05). A strong negative correlation was found between average daily step counts and CRP levels (rho = -0.62, p<0.001). Higher UKDDQ scores, or unhealthier diet habits, were associated with higher fasting plasma glucose (rho = 0.28, p = 0.08), triglyceride levels (rho = 0.31, p = 0.05), and insulin resistance (HOMA IR: rho = 0.31, p = 0.06). Higher PHQ-9 scores correlated with lower 2h-oGTT glucose levels (rho = -0.37, p<0.05).
CONCLUSIONS: OGTT screening is superior to HbA1c screening in detecting preDM and DM early. Patients identified with preDM/DM by oGTT or HbA1c screening are insulin-resistant and have higher pulse rates. Abdominal obesity, unfavorable lipid profiles, and higher CRP levels were noted in patients screened by HbA1c, but not by oGTT. Low physical activity, low depression scores, and unhealthy diet habits were associated with higher CRP and higher glucose and triglyceride levels, respectively. Future studies should assess the impact of specifically tailored individual lifestyle counseling and medical management interventions in this high-risk population.

BACKGROUND: A recent meta-analysis revealed that vagally mediated heart rate variability (vmHRV; a biomarker of emotion regulation capacity) significantly decreases in the luteal phase of the menstrual cycle. As two follow-up studies suggest, these vmHRV decreases are driven primarily by increased luteal progesterone (P4). However, analyses also revealed significant interindividual differences in vmHRV reactivity to the cycle, which is in line with longstanding evidence for interindividual differences in mood sensitivity to the cycle. The present study begins to investigate whether these interindividual differences in vmHRV cyclicity can explain who is at higher risk of showing premenstrual emotional changes. We expected a greater degree of midluteal vmHRV decrease to be predictive of a greater premenstrual increase in negative affect.
METHODS: We conducted an observational study with a naturally cycling community sample (N = 31, M = 26.03 years). Over a span of six weeks, participants completed (a) daily ratings of negative affect and (b) counterbalanced lab visits in their ovulatory, midluteal, and perimenstrual phases. Lab visits were scheduled based on positive ovulation tests and included assessments of baseline vmHRV and salivary ovarian steroid levels.
RESULTS: In line with previous research, multilevel models suggest that most of the sample shows ovulatory-to-midluteal vmHRV decreases which, however, were not associated with premenstrual emotional changes. Interestingly, it was only the subgroup with luteal increases in vmHRV whose negative affect markedly worsened premenstrually and improved postmenstrually.
CONCLUSIONS: The present study begins to investigate cyclical changes in vmHRV as a potential biomarker of mood sensitivity to the menstrual cycle. The results demonstrate a higher level of complexity in these associations than initially expected, given that only atypical midluteal increases in vmHRV are associated with greater premenstrual negative affect. Potential underlying mechanisms are discussed, among those the possibility that luteal vmHRV increases index compensatory efforts to regulate emotion in those with greater premenstrual negative affect. However, future studies with larger and clinical samples and more granular vmHRV assessments should build on these findings and further explore associations between vmHRV cyclicity and menstrually related mood changes.

Individuals\' sensitivity to climate hazards is a central component of their vulnerability to climate change. In this paper, we introduce and outline the utility of a new intraindividual variability construct, affective sensitivity to air pollution (ASAP)-defined as the extent to which an individual\'s affective states fluctuate in accordance with daily changes in air quality. As such, ASAP pushes beyond examination of differences in individuals\' exposures to air pollution to examination of differences in individuals\' sensitivities to air pollution. Building on known associations between air pollution exposure and adverse mental health outcomes, we empirically illustrate how application of Bayesian multilevel models to intensive repeated measures data obtained in an experience sampling study (N = 150) over one year can be used to examine whether and how individuals\' daily affective states fluctuate with the daily concentrations of outdoor air pollution in their county. Results indicate construct viability, as we found substantial interindividual differences in ASAP for both affect arousal and affect valence. This suggests that repeated measures of individuals\' day-to-day affect provides a new way of measuring their sensitivity to climate change. In addition to contributing to discourse around climate vulnerability, the intraindividual variability construct and methodology proposed here can help better integrate affect and mental health in climate adaptation policies, plans, and programs.