Sleep, Health, and Aging

As people grow older, getting a good night's sleep remains essential to maintaining good health. Insomnia is a common complaint in older adults, and although occasional sleep complaints may not be associated with age, chronic sleep difficulties are experienced more often by older adults than by younger adults

Interoception and inflammation in psychiatric disorders

Despite a historical focus on neurally-mediated interoceptive signaling mechanisms, humoral (and even cellular) signals also play an important role in communicating bodily physiological state to the brain. These signaling pathways can perturb neuronal structure, chemistry and function leading to discrete changes in behavior. They are also increasingly implicated in the pathophysiology of psychiatric disorders. The importance of these humoral signaling pathways is perhaps most powerfully illustrated in the context of infection and inflammation. Here we provide an overview of how immune activation of neural and humoral interoceptive mechanisms interact to mediate discrete changes in brain and behavior and highlight how activation of these pathways at specific points in neural development may predispose to psychiatric disorder. As our mechanistic understanding of these interoceptive pathways continues to emerge it is revealing novel therapeutic targets, potentially heralding an exciting new era of immunotherapies in psychiatry

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring

Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well-established. Furthermore, parameters such as intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions

More than a feeling: A unified view of stress measurement for population science.

Stress can influence health throughout the lifespan, yet there is little agreement about what types and aspects of stress matter most for human health and disease. This is in part because "stress" is not a monolithic concept but rather, an emergent process that involves interactions between individual and environmental factors, historical and current events, allostatic states, and psychological and physiological reactivity. Many of these processes alone have been labeled as "stress." Stress science would be further advanced if researchers adopted a common conceptual model that incorporates epidemiological, affective, and psychophysiological perspectives, with more precise language for describing stress measures. We articulate an integrative working model, highlighting how stressor exposures across the life course influence habitual responding and stress reactivity, and how health behaviors interact with stress. We offer a Stress Typology articulating timescales for stress measurement - acute, event-based, daily, and chronic - and more precise language for dimensions of stress measurement

Research Advances: January 2014

The VA has a comprehensive research agenda to help the newest generation of Veterans -- those returning from operations Enduring Freedom, Iraqi Freedom, and New Dawn. In addition to exploring new treatments for traumatic brain injury and other complex blast-related injuries, VA researchers are examining ways to improve the delivery of health care services for these Veterans and promote their reintegration back into their families, communities, and workplaces.This publication reviews recent advances in research about Veterans' health and well-being

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders

Inflammation and Sleep as Risk Factors for Psychological Distress During Adolescence. The influence of low-grade inflammation and sleep duration on psychological distress in girls and boys aged 15-18 years. The Fit Futures study

Background: Onset of depression and psychological distress increase dramatically during adolescence. In adults, research has indicated that low-grade inflammation and short sleep duration are risk factors for depression. Less research has been conducted on these risk factors in healthy adolescents. Methods: This thesis explores associations between two respective exposures 1) five inflammatory markers and 2) sleep duration and psychological distress in upper-secondary school students from the Fit Futures, a prospective study with data from two time-points. Cross-sectional and prospective regression analyses were conducted to explore associations between the exposures and psychological distress. Additionally, we explored the moderating effects from body-fat percentage, physical activity, and sleep duration on the associations between inflammatory markers and psychological distress. We used change scores to explore whether changes in sleep duration were associated with changes in psychological distress. Results: The overall results showed no cross-sectional associations between inflammatory markers and psychological distress in neither girls nor boys. In prospective analyses, there were found predictive value from CRP and TGF-α on psychological distress in boys. Further in boys, there was found interaction effects indicating that body fat percentage and physical activity moderated the effects from CRP on psychological distress, and that sleep duration moderated the effect from TWEAK on psychological distress. Regarding sleep duration as exposure, we found that increases in sleep duration predicted decreases in psychological distress in both girls and boys. Conclusion: CRP and TGF-α had predictive value on psychological distress two years later in boys. We found significant effect-modifications in boys indicating that interventions to promote mental health during adolescence should focus on decreasing body fat percentage and increasing physical activity. Further, our results suggest that decreased sleep duration is a risk factor for increased psychological distress. Future studies should examine causality between the risk factors and psychological distress.Bakgrunn: Depresjon og psykiske plager (symptomer på angst og depresjon) øker drastisk i ungdomsalderen. Blant voksne er kronisk inflammasjon og kort søvnvarighet risikofaktorer for depresjon. Mindre forskning har blitt gjort på disse risikofaktorene blant frisk ungdom, hvor funnene har variert og det i liten grad vært justert for konfundering. Det er viktig å studere disse risikofaktorene for å få kunnskap relevant for forebygging av psykiske plager og depresjon. Metoder: Denne doktorgraden utforsker sammenhenger mellom to eksponeringer 1) fem inflammasjonsmarkører og 2) søvnvarighet og utfallet psykiske plager hos elever på videregående skole fra Fit Futures studien, en prospektiv studie med data fra to måletidspunkter. Regresjonsanalyser gjort på tverrsnittsdata og prospektive data ble gjort for å utforske sammenhengene mellom eksponeringene og psykiske plager. I tillegg undersøkte vi om sammenhengen mellom inflammasjonsmarkører og psykiske plager ble moderert av fettprosent, fysisk aktivitet og søvnvarighet. Vi brukte endringsskårer for å utforske om endringer i søvnvarighet hang sammen med endringer i psykiske plager. Resultater: Resultatene viste ingen tverrsnitt-sammenhenger mellom inflammasjonsmarkører og psykiske plager hos verken jenter eller gutter. I de prospektive analysene fant vi at CRP og TGF-α predikerte psykiske plager hos gutter. Vi fant også interaksjonseffekter som tyder på at fettprosent og fysisk aktivitet modererte effektene fra CRP på psykiske plager, og at søvn modererte effekten fra TWEAK på psykiske plager. Når det gjelder søvnvarighet, så fant vi at endringer i søvnvarighet predikerte endringer i psykiske plager hos både gutter og jenter. Økning i søvnvarighet predikerte reduksjon i psykiske plager for både jenter og gutter. Konklusjon: Denne doktorgraden utforsket to risikofaktorer hos frisk ungdom, og inkluderte viktige konfundere. CRP og TGF-α predikerte psykiske plager to år senere hos gutter. Vi fant signifikante moderasjonseffekter hos gutter, som tyder på at intervensjoner for å promotere psykisk helse blant ungdom bør ta sikte på å redusere fettprosent og øke fysisk aktivitet. Videre tyder resultatene på at redusert søvnvarighet er en risikofaktor for psykiske plager. Derfor bør intervensjoner for å promotere psykisk helse blant ungdom vurdere å sette søkelys på økt søvnvarighet. Resultatene kan indikere at kronisk inflammasjon og kort søvnvarighet er risikofaktorer for psykiske plager hos ungdom, på samme måte som tidligere studier har vist blant voksne. Fremtidige studier bør utforske kausaliteten mellom risikofaktorene og psykiske plager