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Bipolar disorder is a severe mental illness that results in significant morbidity and mortality. While pharmacotherapy is the primary treatment, adjunctive psychotherapy can improve outcomes. However, access to therapy is limited. Smartphones and other technologies can increase access to therapeutic strategies that enhance self-management while simultaneously augmenting care by providing adaptive delivery of content to users as well as alerts to providers to facilitate clinical care communication. Unfortunately, while adaptive interventions are being developed and tested to improve care, information describing the components of adaptive interventions is often not published in sufficient detail to facilitate replication and improvement of these interventions. To contribute to and support the improvement and dissemination of technology-based mental health interventions, we provide a detailed description of the expert system for adaptively delivering content and facilitating clinical care communication for LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder. Information from empirically supported psychotherapies for bipolar disorder, health psychology behavior change theories, and chronic disease self-management models was combined with user-centered design data and psychiatrist feedback to guide the development of the expert system. Decision points determining the timing of intervention option adaptation were selected to occur daily and weekly based on self-report data for medication adherence, sleep duration, routine, and wellness levels. These data were selected for use as the tailoring variables determining which intervention options to deliver when and to whom. Decision rules linking delivery of options and tailoring variable thresholds were developed based on existing literature regarding bipolar disorder clinical status and psychiatrist feedback. To address the need for treatment adaptation with varying clinical statuses, decision rules for a clinical status state machine were developed using self-reported wellness rating data. Clinical status from this state machine was incorporated into hierarchal decision tables that select content for delivery to users and alerts to providers. The majority of the adaptive content addresses sleep duration, medication adherence, managing signs and symptoms, building and utilizing support, and keeping a regular routine, as well as determinants underlying engagement in these target behaviors as follows: attitudes and perceptions, knowledge, support, evaluation, and planning. However, when problems with early warning signs, symptoms, and transitions to more acute clinical states are detected, the decision rules shift the adaptive content to focus on managing signs and symptoms, and engaging with psychiatric providers. Adaptive mental health technologies have the potential to enhance the self-management of mental health disorders. The need for individuals with bipolar disorder to engage in the management of multiple target behaviors and to address changes in clinical status highlights the importance of detailed reporting of adaptive intervention components to allow replication and improvement of adaptive mental health technologies for complex mental health problems.

Bipolar disorder is a severe mental illness characterized by recurrent episodes of depressed, elevated, and mixed mood states. The addition of psychotherapy to pharmacological management can decrease symptoms, lower relapse rates, and improve quality of life; however, access to psychotherapy is limited. Mental health technologies such as smartphone apps are being studied as a means to increase access to and enhance the effectiveness of adjunctive psychotherapies for bipolar disorder. Individuals with bipolar disorder find this intervention format acceptable, but our understanding of how people utilize and integrate these tools into their behavior change and maintenance processes remains limited. The objective of this study was to explore how individuals with bipolar disorder perceive and utilize a smartphone intervention for health behavior change and maintenance. Individuals with bipolar disorder were recruited via flyers placed at university-affiliated and private outpatient mental health practices to participate in a pilot study of LiveWell, a smartphone-based self-management intervention. At the end of the study, all participants completed in-depth qualitative exit interviews. The behavior change framework developed to organize the intervention design was used to deductively code behavioral targets and determinants involved in target engagement. Inductive coding was used to identify themes not captured by this framework. In terms of behavioral targets, participants emphasized the importance of managing mood episode-related signs and symptoms. They also discussed the importance of maintaining regular routines, sleep duration, and medication adherence. Participants emphasized that receiving support from a coach as well as seeking and receiving assistance from family, friends, and providers were important for managing behavioral targets and staying well. In terms of determinants, participants stressed the important role of monitoring for their behavior change and maintenance efforts. Monitoring facilitated self-awareness and reflection, which was considered valuable for staying well. Some participants also felt that the intervention facilitated learning information necessary for managing bipolar disorder but others felt that the information provided was too basic. In addition to addressing acceptability, satisfaction, and engagement, a person-based design of mental health technologies can be used to understand how people experience the impact of these technologies on their behavior change and maintenance efforts. This understanding may then be used to guide ongoing intervention development. The participants' perceptions aligned with the intervention's primary behavioral targets and use of a monitoring tool as a core intervention feature. Participant feedback further indicates that developing additional content and tools to address building and engaging social support may be an important avenue for improving LiveWell. A comprehensive behavior change framework to understand participant perceptions of their behavior change and maintenance efforts may help facilitate ongoing intervention development.

Bipolar disorder is a severe mental illness with high morbidity and mortality rates. Even with pharmacological treatment, frequent recurrence of episodes, long episode durations, and persistent interepisode symptoms are common and disruptive. Combining psychotherapy with pharmacotherapy improves outcomes; however, many individuals with bipolar disorder do not receive psychotherapy. Mental health technologies can increase access to self-management strategies derived from empirically supported bipolar disorder psychotherapies while also enhancing treatment by delivering real-time assessments, personalized feedback, and provider alerts. In addition, mental health technologies provide a platform for self-report, app use, and behavioral data collection to advance understanding of the longitudinal course of bipolar disorder, which can then be used to support ongoing improvement of treatment. A description of the theoretical and empirically supported framework, design, and protocol for a randomized controlled trial (RCT) of LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder, is provided to facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar disorder. The goal of the trial is to determine the effectiveness of LiveWell for reducing relapse risk and symptom burden as well as improving quality of life (QOL) while simultaneously clarifying behavioral targets involved in staying well and better characterizing the course of bipolar disorder and treatment response. The study is a single-blind RCT (n=205; 2:3 ratio of usual care vs usual care plus LiveWell). The primary outcome is the time to relapse. Secondary outcomes are percentage time symptomatic, symptom severity, and QOL. Longitudinal changes in target behaviors proposed to mediate the primary and secondary outcomes will also be determined, and their relationships with the outcomes will be assessed. A database of clinical status, symptom severity, real-time self-report, behavioral sensor, app use, and personalized content will be created to better predict treatment response and relapse risk. Recruitment and screening began in March 2017 and ended in April 2019. Follow-up ended in April 2020. The results of this study are expected to be published in 2022. This study will examine whether LiveWell reduces relapse risk and symptom burden and improves QOL for individuals with bipolar disorder by increasing access to empirically supported self-management strategies. The role of selected target behaviors (medication adherence, sleep duration, routine, and management of signs and symptoms) in these outcomes will also be examined. Simultaneously, a database will be created to initiate the development of algorithms to personalize and improve treatment for bipolar disorder. In addition, we hope that this description of the theoretical and empirically supported framework, intervention design, and study protocol for the RCT of LiveWell will facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar and other mental health disorders. ClinicalTrials.gov NCT03088462; https://www.clinicaltrials.gov/ct2/show/NCT03088462. DERR1-10.2196/30710.

Bipolar disorder is a serious mental illness that results in significant morbidity and mortality. Pharmacotherapy is the primary treatment for bipolar disorder; however, adjunctive psychotherapy can help individuals use self-management strategies to improve outcomes. Yet access to this therapy is limited. Smartphones and other technologies have the potential to increase access to therapeutic strategies that enhance self-management while simultaneously providing real-time user feedback and provider alerts to augment care. This paper describes the user-centered development of LiveWell, a smartphone-based self-management intervention for bipolar disorder, to contribute to and support the ongoing improvement and dissemination of technology-based mental health interventions. Individuals with bipolar disorder first participated in a field trial of a simple smartphone app for self-monitoring of behavioral targets. To develop a complete technology-based intervention for bipolar disorder, this field trial was followed by design sessions, usability testing, and a pilot study of a smartphone-based self-management intervention for bipolar disorder. Throughout all phases of development, intervention revisions were made based on user feedback. The core of the LiveWell intervention consists of a daily self-monitoring tool, the Daily Check-in. This self-monitoring tool underwent multiple revisions during the user-centered development process. Daily Check-in mood and thought rating scales were collapsed into a single wellness rating scale to accommodate user development of personalized scale anchors. These anchors are meant to assist users in identifying early warning signs and symptoms of impending episodes to take action based on personalized plans. When users identified personal anchors for the wellness scale, the anchors most commonly reflected behavioral signs and symptoms (40%), followed by cognitive (25%), mood (15%), physical (10%), and motivational (7%) signs and symptoms. Changes to the Daily Check-in were also made to help users distinguish between getting adequate sleep and keeping a regular routine. At the end of the pilot study, users reported that the Daily Check-in made them more aware of early warning signs and symptoms and how much they were sleeping. Users also reported that they liked personalizing their anchors and plans and felt this process was useful. Users experienced some difficulties with developing, tracking, and achieving target goals. Users also did not consistently follow up with app recommendations to contact providers when Daily Check-in data suggested they needed additional assistance. As a result, the human support roles for the technology were expanded beyond app use support to include support for self-management and clinical care communication. The development of these human support roles was aided by feedback on the technology's usability from the users and the coaches who provided the human support. User input guided the development of intervention content, technology, and coaching support for LiveWell. Users valued the provision of monitoring tools and the ability to personalize plans for staying well, supporting the role of monitoring and personalization as important features of digital mental health technologies. Users also valued human support of the technology in the form of a coach, and user difficulties with aspects of self-management and care-provider communication led to an expansion of the coach's support roles. Obtaining feedback from both users and coaches played an important role in the development of both the LiveWell technology and human support. Attention to all stakeholders involved in the use of mental health technologies is essential for optimizing intervention development.

Anterior cingulate cortex (ACC) and striatum are part of the emotional neural circuitry implicated in major depressive disorder (MDD). Music is often used for emotion regulation, and pleasurable music listening activates the dopaminergic system in the brain, including the ACC. The present study uses functional MRI (fMRI) and an emotional nonmusical and musical stimuli paradigm to examine how neural processing of emotionally provocative auditory stimuli is altered within the ACC and striatum in depression. Nineteen MDD and 20 never-depressed (ND) control participants listened to standardized positive and negative emotional musical and nonmusical stimuli during fMRI scanning and gave subjective ratings of valence and arousal following scanning. ND participants exhibited greater activation to positive versus negative stimuli in ventral ACC. When compared with ND participants, MDD participants showed a different pattern of activation in ACC. In the rostral part of the ACC, ND participants showed greater activation for positive information, while MDD participants showed greater activation to negative information. In dorsal ACC, the pattern of activation distinguished between the types of stimuli, with ND participants showing greater activation to music compared to nonmusical stimuli, while MDD participants showed greater activation to nonmusical stimuli, with the greatest response to negative nonmusical stimuli. No group differences were found in striatum. These results suggest that people with depression may process emotional auditory stimuli differently based on both the type of stimulation and the emotional content of that stimulation. This raises the possibility that music may be useful in retraining ACC function, potentially leading to more effective and targeted treatments.

Michael Breckwoldt and colleagues have developed a new approach to follow the mitochondrial redox potential of neurons with high spatio-temporal resolution. This multiparametric in vivo imaging approach is based on the transgenic expression of a biosensor for glutathione redox potential in neuronal mitochondria, with utility demonstrated in mouse models of amyotrophic lateral sclerosis and spinal cord injury. It should prove useful for studying mitochondrial pathology in neurological disease models. Mitochondrial redox signals have a central role in neuronal physiology and disease. Here we describe a new optical approach to measure fast redox signals with single-organelle resolution in living mice that express genetically encoded redox biosensors in their neuronal mitochondria. Moreover, we demonstrate how parallel measurements with several biosensors can integrate these redox signals into a comprehensive characterization of mitochondrial function. This approach revealed that axonal mitochondria undergo spontaneous 'contractions' that are accompanied by reversible redox changes. These contractions are amplified by neuronal activity and acute or chronic neuronal insults. Multiparametric imaging reveals that contractions constitute respiratory chain–dependent episodes of depolarization coinciding with matrix alkalinization, followed by uncoupling. In contrast, permanent mitochondrial damage after spinal cord injury depends on calcium influx and mitochondrial permeability transition. Thus, our approach allows us to identify heterogeneity among physiological and pathological redox signals, correlate such signals to functional and structural organelle dynamics and dissect the underlying mechanisms.

The embryonic extracellular matrix (ECM) undergoes transition to mature ECM as development progresses, yet few mechanisms ensuring ECM proteostasis during this period are known. Fibrillin microfibrils are macromolecular ECM complexes serving structural and regulatory roles. In mice, Fbn1 and Fbn2, encoding the major microfibrillar components, are strongly expressed during embryogenesis, but fibrillin-1 is the major component observed in adult tissue microfibrils. Here, analysis of Adamts6 and Adamts10 mutant mouse embryos, lacking these homologous secreted metalloproteases individually and in combination, along with in vitro analysis of microfibrils, measurement of ADAMTS6-fibrillin affinities and N-terminomics discovery of ADAMTS6-cleaved sites, identifies a proteostatic mechanism contributing to postnatal fibrillin-2 reduction and fibrillin-1 dominance. The lack of ADAMTS6, alone and in combination with ADAMTS10 led to excess fibrillin-2 in perichondrium, with impaired skeletal development defined by a drastic reduction of aggrecan and cartilage link protein, impaired BMP signaling in cartilage, and increased GDF5 sequestration in fibrillin-2-rich tissue. Although ADAMTS6 cleaves fibrillin-1 and fibrillin-2 as well as fibronectin, which provides the initial scaffold for microfibril assembly, primacy of the protease-substrate relationship between ADAMTS6 and fibrillin-2 was unequivocally established by reversal of the defects in Adamts6 -/- embryos by genetic reduction of Fbn2 , but not Fbn1 .

Substantial amounts of secondary organic aerosol (SOA) can be formed from isoprene epoxydiols (IEPOX), which are oxidation products of isoprene mainly under low-NO conditions. Total IEPOX-SOA, which may include SOA formed from other parallel isoprene oxidation pathways, was quantified by applying positive matrix factorization (PMF) to aerosol mass spectrometer (AMS) measurements. The IEPOX-SOA fractions of organic aerosol (OA) in multiple field studies across several continents are summarized here and show consistent patterns with the concentration of gas-phase IEPOX simulated by the GEOS-Chem chemical transport model. During the Southern Oxidant and Aerosol Study (SOAS), 78 % of PMF-resolved IEPOX-SOA is accounted by the measured IEPOX-SOA molecular tracers (2-methyltetrols, C5-Triols, and IEPOX-derived organosulfate and its dimers), making it the highest level of molecular identification of an ambient SOA component to our knowledge. An enhanced signal at C5H6O+ (m/z 82) is found in PMF-resolved IEPOX-SOA spectra. To investigate the suitability of this ion as a tracer for IEPOX-SOA, we examine fC5H6O (fC5H6O= C5H6O+/OA) across multiple field, chamber, and source data sets. A background of ~ 1.7 ± 0.1 ‰ (‰ = parts per thousand) is observed in studies strongly influenced by urban, biomass-burning, and other anthropogenic primary organic aerosol (POA). Higher background values of 3.1 ± 0.6 ‰ are found in studies strongly influenced by monoterpene emissions. The average laboratory monoterpene SOA value (5.5 ± 2.0 ‰) is 4 times lower than the average for IEPOX-SOA (22 ± 7 ‰), which leaves some room to separate both contributions to OA. Locations strongly influenced by isoprene emissions under low-NO levels had higher fC5H6O (~ 6.5 ± 2.2 ‰ on average) than other sites, consistent with the expected IEPOX-SOA formation in those studies. fC5H6O in IEPOX-SOA is always elevated (12–40 ‰) but varies substantially between locations, which is shown to reflect large variations in its detailed molecular composition. The low fC5H6O (< 3 ‰) reported in non-IEPOX-derived isoprene-SOA from chamber studies indicates that this tracer ion is specifically enhanced from IEPOX-SOA, and is not a tracer for all SOA from isoprene. We introduce a graphical diagnostic to study the presence and aging of IEPOX-SOA as a triangle plot of fCO2 vs. fC5H6O. Finally, we develop a simplified method to estimate ambient IEPOX-SOA mass concentrations, which is shown to perform well compared to the full PMF method. The uncertainty of the tracer method is up to a factor of ~ 2, if the fC5H6O of the local IEPOX-SOA is not available. When only unit mass-resolution data are available, as with the aerosol chemical speciation monitor (ACSM), all methods may perform less well because of increased interferences from other ions at m/z 82. This study clarifies the strengths and limitations of the different AMS methods for detection of IEPOX-SOA and will enable improved characterization of this OA component.

The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides ( APOB ), adiponectin ( ADIPOQ ) and low-density lipoprotein cholesterol ( LDLR and RGAG1 ) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results. Low read depth sequencing of whole genomes and high read depth exomes of nearly 10,000 extensively phenotyped individuals are combined to help characterize novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with lipid-related traits; in addition to describing population structure and providing functional annotation of rare and low-frequency variants the authors use the data to estimate the benefits of sequencing for association studies. Genome variation in health and disease This paper, combining data and initial findings from the different arms of the UK10K project, describes insights from low-read-depth sequencing of whole genomes or high-read-depth exome sequencing of nearly 10,000 individuals sampled from a range of disease collections, as well as participants from healthy population based cohorts. The authors characterize novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with lipid-related traits. In addition to describing population structure and providing functional annotation of rare and low frequency variants, they use the data to estimate the benefits of sequencing for association studies.

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes 1 . Large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here we identify the non-coding RNA RNU4-2 as a syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome 2 . We identify an 18 base pair region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 115 individuals with NDD. Most individuals (77.4%) have the same highly recurrent single base insertion (n.64_65insT). In 54 individuals in whom it could be determined, the de novo variants were all on the maternal allele. We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to RNU4-1 and other U4 homologues. Using RNA sequencing, we show how 5′ splice-site use is systematically disrupted in individuals with RNU4-2 variants, consistent with the known role of this region during spliceosome activation. Finally, we estimate that variants in this 18 base pair region explain 0.4% of individuals with NDD. This work underscores the importance of non-coding genes in rare disorders and will provide a diagnosis to thousands of individuals with NDD worldwide. The non-coding RNA RNU4-2 , which is highly expressed in the developing human brain, is identified as a syndromic neurodevelopmental disorder gene, and, using RNA sequencing, 5′ splice-site use is shown to be systematically disrupted in individuals with RNU4-2 variants.