Discover mental health最新文献

The present study aimed to describe adolescents' self-reported emotional and behavioural strengths and difficulties, as well as their insecurity feeling. In addition, the aim was to examine the association with background characteristics, and explore the association between strengths and difficulties and insecurity factors. The study was conducted among 114 secondary school pupils in Finland, using an online questionnaire. Adolescents' emotional and behavioural difficulties and strengths, were mostly classified as normal. Strengths and Difficulties Questionnaire total score as well as internal and external score, were inversely associated with insecurity factors. Girls had significantly higher prosocial behavior compared to boys (P = 0.0007). The age of adolescents was found to be related to their internal difficulties (P = 0.02) and prosocial behavior (P = 0.01). Adolescent's perception of their family relations as poor was associated with external difficulties (P = 0.04). The current results, can be helpful for mental health professionals who work with adolescents in order to implement appropriate and needs specific mental health promotion interventions at individual but also community level. Finally, more research is needed to validate measures for insecurity. This will support mental health professionals in their clinical practice by providing them with all the important factors needed to support adolescents.

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Adverse influences during pregnancy are associated with a range of unfavorable outcomes for the developing offspring. Maternal psychosocial stress, exposure to infections and nutritional imbalances are known risk factors for neurodevelopmental derangements and according psychiatric and neurological manifestations later in offspring life. In this context, the maternal immune activation (MIA) model has been extensively used in preclinical research to study how stimulation of the maternal immune system during gestation derails the tightly coordinated sequence of fetal neurodevelopment. The ensuing consequence of MIA for offspring brain structure and function are majorly manifested in behavioral and cognitive abnormalities, phenotypically presenting during the periods of adolescence and adulthood. These observations have been interpreted within the framework of the "double-hit-hypothesis" suggesting that an elevated risk for neurodevelopmental disorders results from an individual being subjected to two adverse environmental influences at distinct periods of life, jointly leading to the emergence of pathology. The early postnatal period, during which the caregiving parent is the major determinant of the newborn´s environment, constitutes a window of vulnerability to external stimuli. Considering that MIA not only affects the developing fetus, but also impinges on the mother´s brain, which is in a state of heightened malleability during pregnancy, the impact of MIA on maternal brain function and behavior postpartum may importantly contribute to the detrimental consequences for her progeny. Here we review current information on the interaction between the prenatal and postnatal maternal environments in the modulation of offspring development and their relevance for the pathophysiology of the MIA model.

Background: Questions of equitable access to primary mental healthcare (PMHC) for older persons in India have been examined mostly in terms of the coverage of services, although perceptions of mental health and old age and social norms at the community level should be considered in the shaping of PMHC approaches. The present qualitative study, therefore, examined how social perceptions and norms of mental health in old age are and should be considered in the design and implementation of primary healthcare approaches in India.

Methods: A secondary thematic analysis of semi-structured interviews with key stakeholders (n = 14) of PMHC in India was conducted.

Results: Four key themes emerged from the analysis, in which social perceptions and norms were discussed: (1) family participation and low threshold access to mental healthcare, (2) the position of community health workers as an important pillar of old age and mental health-sensitive community-based care, (3) the role of social cohesion and traditional values in fostering a positive and supportive community environment for old age mental health, and (4) the empowerment of communities, families and older persons through mental health education.

Conclusions: PMHC, with its focus on mental health promotion, could be an important anchor for combatting negative attitudes about mental health and old age. The findings presented in this study can inform age-sensitive policies and programmes for mental health in India and could inform future research on the subject.

Health care coverage is an important factor in receipt of behavioral healthcare. This study uses data from the New York City Community Health Survey to examine how sexual minority status impacts the relationship between depression status and having health care coverage. Approximately 10% of the sample (n = 9571; 47% 45+ years old; 35% White Non-Hispanic; 7% sexual minority) reported probable depression and low health care coverage. Compared to heterosexual participants, a greater proportion of sexual minority participants had low health care coverage (17% vs. 9%) and probable depression (19% vs. 9%). Logistic regression examining the association between probable depression status and health care coverage showed that those with probable depression have odds of low health care coverage that are were 3.08 times those who did not have probable depression; this relationship was not modified by sexual orientation. Continued research to understand the interplay of health care coverage, mental health, and sexual orientation is needed.

Introduction: Volunteering has reported health benefits. However, little is known in Portugal about the views of mental health professionals and volunteers on volunteering in mental health care.

Methods: A qualitative secondary analysis of data from six focus groups with 28 participants was conducted in order to explore and compare the perspectives on volunteering in mental health of two stakeholders: mental health professionals and volunteers in Portugal.

Results: Four main themes arose: the nature of the volunteering relationship; volunteering has multiple aims; technology has potential for volunteering; and volunteering has its challenges. Although there were mostly commonalities between their views, some variability suggested that different stakeholders may consider different aspects of volunteering differently. Overall, stakeholders called for structured recruitment and support, training, defining boundaries and fighting the stigma of mental illness.

Conclusion: Despite the lack of volunteering tradition in mental health care in Portugal, volunteering programmes were perceived as an important resource for patients with mental illness.

Popular culture and medical lore have long postulated a connection between full moon and exacerbations of psychiatric disorders. We wanted to empirically analyze the hypothesis that suicides are increased during the period around full moons. We analyzed pre-COVID suicides from the Marion County Coroner's Office (n = 776), and show that deaths by suicide are significantly increased during the week of the full moon (p = 0.037), with older individuals (age ≥ 55) showing a stronger effect (p = 0.019). We also examined in our dataset which hour of the day (3-4 pm, p = 0.035), and which month of the year (September, p = 0.09) show the most deaths by suicide. We had blood samples on a subset of the subjects (n = 45), which enabled us to look at possible molecular mechanisms. We tested a list of top blood biomarkers for suicidality (n = 154) from previous studies of ours ⁷, to assess which of them are predictive. The biomarkers for suicidality that are predictive of death by suicide during full moon, peak hour of day, and peak month of year, respectively, compared to outside of those periods, appear to be enriched in circadian clock genes. For full moon it is AHCYL2, ACSM3, AK2, and RBM3. For peak hour it is GSK3B, AK2, and PRKCB. For peak month it is TBL1XR1 and PRKCI. Half of these genes are modulated in expression by lithium and by valproate in opposite direction to suicidality, and all of them are modulated by depression and alcohol in the same direction as suicidality. These data suggest that there are temporal effects on suicidality, possibly mediated by biological clocks, pointing to changes in ambient light (timing and intensity) as a therapeutically addressable target to decrease suicidality, that can be coupled with psychiatric pharmacological and addiction treatment preventive interventions.

Background: Adult hippocampal neurogenesis (AHN) is a clinically significant neural phenomenon. Understanding its molecular regulation would be important. In this regard, most studies have focused on transcriptional regulators (TRs), epigenetic modifiers, or non-coding RNAs. RNA-binding proteins (RBPs) have emerged as dominant molecular regulators. It would be significant to understand the potential cross-talk between RBPs and TRs, which could influence AHN.

Methods: The present study employed computational analyses to identify RBPs and TRs regulating AHN, followed by the analysis of their interaction networks and detection of hub proteins. Next, the potential mutual regulation of hub TRs and RBPs was analyzed. Additionally, hippocampal genes differentially expressed upon exercise were analyzed for potential regulation by the identified TRs and RBPs.

Results: 105 TRs and 26 RBPs were found to influence AHN, which could also form interactive networks. Polycomb complex proteins were among the TR network hubs, while HNRNP and SRSF family members were among the hub RBPs. Further, the polycomb complex proteins and SRSF1 could have a mutual regulatory relationship, suggesting a cross-talk between epigenetic/transcriptional and post-transcriptional regulatory pathways. A number of exercise-induced hippocampal genes were also found to be potential targets of the identified TRs and RBPs.

Conclusion: SRSF1 may influence post-transcriptional stability, localization, and alternative splicing patterns of polycomb complex transcripts, and the polycomb proteins may in turn epigenetically influence the SRSF1. Further experimental validation of these regulatory loops/networks could provide novel insights into the molecular regulation of AHN, and unravel new targets for disease-treatment.

Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings.

There is a crisis in mental health care, with more people suffering from psychiatric disorders than resources that are available for treatment, even though spending is substantial. Millions who suffer from addiction, psychosis, depression and suicidality are either untreated or inadequately treated and organized psychiatry is unable to reach them. Possibly as reflection of under-treatment of psychiatric disorders, the rates of suicide have risen: from 1999 through 2014, the age-adjusted suicide rate in the US increased 24%, from 10.5 to 13.0 per 100,000. Assessment of psychiatric symptoms in ongoing outpatient settings is costly, inadequate and unable to detect clinical changes over time. One's digital phenotype is assessed through footprints left over as result of our interface with technology, including automated assessments of quantity and quality of social media activity, patterns and speed of device usage, and physiological data that is automatically collected, such as location, quantity and type of movement, heart rate, and sleep patterns. The use of digital footprints has been advocated for large-scale data collection that can facilitate psychiatric research in naturalistic settings. We highlight recent papers in Discover Mental Health addressing digital approaches to mental health and we also advance here the concept that digital footprints are ready for clinical use. However, before that happens there needs to be discussion on the appropriate boundaries between care that is driven by signals from digital footprints and the rights to privacy and self-determination.