Smartphone-based interventions in bipolar disorder : Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force

Background: The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established. Objectives: To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators. Methods: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668). Results: The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators. Conclusion: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.Peer reviewe

Phenomenology of bipolar disorder not otherwise specified in youth: a comparison of clinical characteristics across the spectrum of manic symptoms

Controversy surrounds the diagnostic categorization of children with episodic moods that cause impairment, but do not meet DSM-IV criteria for bipolar I (BD-I) or bipolar II (BD-II) disorder. This study aims to characterize the degree to which these children, who meet criteria for bipolar disorder not otherwise specified (BD-NOS), are similar to those with full syndromal BD, versus those with no bipolar spectrum diagnosis (no BSD)

Abnormal deactivation of the inferior frontal gyrus during implicit emotion processing in youth with bipolar disorder: Attenuated by medication

Previous neuroimaging studies of youth with bipolar disorder(BD) have identified abnormalities in emotion regulation circuitry. Using data from the Longitudinal Assessment of Manic Symptoms Cohort (a clinical sample recruited for behavioral and emotional dysregulation), we examined the impact of BD and medication on activation in these regions. Functional neuroimaging data were obtained from 15 youth with BD who currently were unmedicated with a mood stabilizer or antipsychotic (U-BD), 19 youth with medicated BD (M-BD), a non-bipolar clinical sample with high rates of disruptive behavioral disorders (non-BD, n=59), and 29 healthy controls (HC) while they were shown task-irrelevant morphing emotional faces and shapes. Whole brain analysis was used to identify clusters that showed differential activation to emotion vs. shapes across group. To assess pair-wise comparisons and potential confounders, mean activation data were extracted only from clusters within regions previously implicated in emotion regulation (including amygdala and ventral prefrontal regions). A cluster in the right inferior frontal gyrus (IFG) showed group differences to emotion vs. shapes (159 voxels, corrected p<.05). Within this cluster, U-BD youth showed decreased activation relative to HC (p=.007) and non-BD (p=.004) youth. M-BD also showed decreased activation in this cluster relative to HC and non-BD youth, but these differences were attenuated. Results were specific to negative emotions, and not found with happy faces. IFG findings were not explained by other medications (e.g. stimulants) or diagnoses. Compared to both HC and a non-BD sample, U-BD is associated with abnormally decreased right IFG activation to negative emotions

Polygenic scores and onset of major mood or psychotic disorders among offspring of affected parents

Objective: Family history is an established risk factor for mental illness. The authors sought to investigate whether polygenic scores (PGSs) can complement family history to improve identification of risk for major mood and psychotic disorders. Methods: Eight cohorts were combined to create a sample of 1,884 participants ages 2–36 years, including 1,339 offspring of parents with mood or psychotic disorders, who were prospectively assessed with diagnostic interviews over an average of 5.1 years. PGSs were constructed for depression, bipolar disorder, anxiety, attention deficit hyperactivity disorder (ADHD), schizophrenia, neuroticism, subjective well-being, p factor, and height (as a negative control). Cox regression was used to test associations between PGSs, family history of major mental illness, and onsets of major mood and psychotic disorders. Results: There were 435 onsets of major mood and psychotic disorders across follow-up. PGSs for neuroticism (hazard ratio=1.23, 95% CI=1.12–1.36), schizophrenia (hazard ratio=1.15, 95% CI=1.04–1.26), depression (hazard ratio=1.11, 95% CI=1.01–1.22), ADHD (hazard ratio=1.10, 95% CI=1.00–1.21), subjective well-being (hazard ratio=0.90, 95% CI=0.82–0.99), and p factor (hazard ratio=1.14, 95% CI=1.04–1.26) were associated with onsets. After controlling for family history, neuroticism PGS remained significantly positively associated (hazard ratio=1.19, 95% CI=1.08–1.31) and subjective well-being PGS remained significantly negatively associated (hazard ratio=0.89, 95% CI=0.81–0.98) with onsets. Conclusions: Neuroticism and subjective well-being PGSs capture risk of major mood and psychotic disorders that is independent of family history, whereas PGSs for psychiatric illness provide limited predictive power when family history is known. Neuroticism and subjective well-being PGSs may complement family history in the early identification of persons at elevated risk

ENVIRONMENTAL HEALTH PERSPECTIVES ENVIRONMENTAL HEALTH PERSPECTIVES Association between Manganese Exposure through Drinking Water and Infant Mortality in Bangladesh Association between Manganese Exposure through Drinking Water and Infant Mortality in Bang

Abstract Introductio

Effects of medication on neuroimaging findings in bipolar disorder: an updated review [Review]

OBJECTIVE: Neuroimaging is an important tool for better understanding the neurobiological underpinnings of bipolar disorder (BD). However, potential study participants are often receiving psychotropic medications which can possibly confound imaging data. To better interpret the results of neuroimaging studies in BD, it is important to understand the impact of medications on structural magnetic resonance imaging (sMRI), functional MRI (fMRI), and diffusion tensor imaging (DTI). METHODS: To better understand the impact of medications on imaging data, we conducted a literature review and searched MEDLINE for papers that included the key words bipolar disorder and fMRI, sMRI, or DTI. The search was limited to papers that assessed medication effects and had not been included in a previous review by Phillips et al. (Medication effects in neuroimaging studies of bipolar disorder. Am J Psychiatry 2008; 165: 313-320). This search yielded 74 sMRI studies, 46 fMRI studies, and 15 DTI studies. RESULTS: Medication appeared to influence many sMRI studies, but had limited impact on fMRI and DTI findings. From the structural studies, the most robust finding (20/45 studies) was that lithium was associated with increased volumes in areas important for mood regulation, while antipsychotic agents and anticonvulsants were generally not. Regarding secondary analysis of the medication effects of fMRI and DTI studies, few showed significant effects of medication, although rigorous analyses were typically not possible when the majority of subjects were medicated. Medication effects were more frequently observed in longitudinal studies designed to assess the impact of particular medications on the blood oxygen level-dependent (BOLD) signal. With a few exceptions, the observed effects were normalizing, meaning that the medicated individuals with BD were more similar than their unmedicated counterparts to healthy subjects. CONCLUSIONS: The effects of psychotropic medications, when present, are predominantly normalizing and thus do not seem to provide an alternative explanation for differences in volume, white matter tracts, or BOLD signal between BD participants and healthy subjects. However, the normalizing effects of medication could obfuscate differences between BD and healthy subjects, and thus might lead to type II errors