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Bipolar disorder (BD) is a serious mood disorder associated with circadian rhythm abnormalities. Risk for BD is genetically encoded and overlaps with systems that maintain circadian rhythms. Lithium is an effective mood stabilizer treatment for BD, but only a minority of patients fully respond to monotherapy. Presently, we hypothesized that lithium-responsive BD patients (Li-R) would show characteristic differences in chronotype and cellular circadian rhythms compared to lithium non-responders (Li-NR). Selecting patients from a prospective, multi-center, clinical trial of lithium monotherapy, we examined morning vs. evening preference (chronotype) as a dimension of circadian rhythm function in 193 Li-R and Li-NR BD patients. From a subset of 59 patient donors, we measured circadian rhythms in skin fibroblasts longitudinally over 5 days using a bioluminescent reporter (Per2-luc). We then estimated circadian rhythm parameters (amplitude, period, phase) and the pharmacological effects of lithium on rhythms in cells from Li-R and Li-NR donors. Compared to Li-NRs, Li-Rs showed a difference in chronotype, with higher levels of morningness. Evening chronotype was associated with increased mood symptoms at baseline, including depression, mania, and insomnia. Cells from Li-Rs were more likely to exhibit a short circadian period, a linear relationship between period and phase, and period shortening effects of lithium. Common genetic variation in the IP 3 signaling pathway may account for some of the individual differences in the effects of lithium on cellular rhythms. We conclude that circadian rhythms may influence response to lithium in maintenance treatment of BD.

BackgroundBipolar disorder is a serious and common psychiatric disorder characterized by manic and depressive mood switches and a relapsing and remitting course. The cornerstone of clinical management is stabilization and prophylaxis using mood-stabilizing medications to reduce both manic and depressive symptoms. Lithium remains the gold standard of treatment with the strongest data for both efficacy and suicide prevention. However, many patients do not respond to this medication, and clinically there is a great need for tools to aid the clinician in selecting the correct treatment. Large genome wide association studies (GWAS) investigating retrospectively the effect of lithium response are in the pipeline; however, few large prospective studies on genetic predictors to of lithium response have yet been conducted. The purpose of this project is to identify genes that are associated with lithium response in a large prospective cohort of bipolar patients and to better understand the mechanism of action of lithium and the variation in the genome that influences clinical response.Methods/DesignThis study is an 11-site prospective non-randomized open trial of lithium designed to ascertain a cohort of 700 subjects with bipolar I disorder who experience protocol-defined relapse prevention as a result of treatment with lithium monotherapy. All patients will be diagnosed using the Diagnostic Interview for Genetic Studies (DIGS) and will then enter a 2-year follow-up period on lithium monotherapy if and when they exhibit a score of 1 (normal, not ill), 2 (minimally ill) or 3 (mildly ill) on the Clinical Global Impressions of Severity Scale for Bipolar Disorder (CGI-S-BP Overall Bipolar Illness) for 4 of the 5 preceding weeks. Lithium will be titrated as clinically appropriate, not to exceed serum levels of 1.2 mEq/L. The sample will be evaluated longitudinally using a wide range of clinical scales, cognitive assessments and laboratory tests. On relapse, patients will be discontinued or crossed-over to treatment with valproic acid (VPA) or treatment as usual (TAU). Relapse is defined as a DSM-IV manic, major depressive or mixed episode or if the treating physician decides a change in medication is clinically necessary. The sample will be genotyped for GWAS. The outcome for lithium response will be analyzed as a time to event, where the event is defined as clinical relapse, using a Cox Proportional Hazards model. Positive single nucleotide polymorphisms (SNPs) from past genetic retrospective studies of lithium response, the Consortium on Lithium Genetics (ConLiGen), will be tested in this prospective study sample; a meta-analysis of these samples will then be performed. Finally, neurons will be derived from pluripotent stem cells from lithium responders and non-responders and tested in vivo for response to lithium by gene expression studies. SNPs in genes identified in these cellular studies will also be tested for association to response.DiscussionLithium is an extraordinarily important therapeutic drug in the clinical mana...

Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.

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Using sensor data from devices such as smart-watches or mobile phones is very popular in both computer science and medical research. Such movement data can predict certain health states or performance outcomes.However, in order to increase reliability and replication of the research it is important to share data and results openly. In medicine, this is often difficult due to legal restrictions or to the fact that data collected from clinical trials is seen as very valuable and something that should be kept "in-house". In this paper, we therefore present PSYKOSE, a publicly shared dataset consisting of motor activity data collected from body sensors. The dataset contains data collected from patients with schizophrenia. Schizophrenia is a severe mental disorder characterized by psychotic symptoms like hallucinations and delusions, as well as symptoms of cognitive dysfunction and diminished motivation. In total, we have data from 22 patients with schizophrenia and 32 healthy control persons. For each person in the dataset, we provide sensor data collected over several days in a row. In addition to the sensor data, we also provide some demographic data and medical assessments during the observation period. The patients were assessed by medical experts from Haukeland University hospital. In addition to the data, we provide a baseline analysis and possible use-cases of the dataset.

Background Lithium is recommended as a first line treatment for bipolar disorders. However, only 30% of patients show an optimal outcome and variability in lithium response and tolerability is poorly understood. It remains difficult for clinicians to reliably predict which patients will benefit without recourse to a lengthy treatment trial. Greater precision in the early identification of individuals who are likely to respond to lithium is a significant unmet clinical need. Structure The H2020-funded Response to Lithium Network (R-LiNK; http://www.r-link.eu.com/) will undertake a prospective cohort study of over 300 individuals with bipolar-I-disorder who have agreed to commence a trial of lithium treatment following a recommendation by their treating clinician. The study aims to examine the early prediction of lithium response, non-response and tolerability by combining systematic clinical syndrome subtyping with examination of multi-modal biomarkers (or biosignatures), including omics, neuroimaging, and actigraphy, etc. Individuals will be followed up for 24 months and an independent panel will assess and classify each participants’ response to lithium according to predefined criteria that consider evidence of relapse, recurrence, remission, changes in illness activity or treatment failure (e.g. stopping lithium; new prescriptions of other mood stabilizers) and exposure to lithium. Novel elements of this study include the recruitment of a large, multinational, clinically representative sample specifically for the purpose of studying candidate biomarkers and biosignatures; the application of lithium-7 magnetic resonance imaging to explore the distribution of lithium in the brain; development of a digital phenotype (using actigraphy and ecological momentary assessment) to monitor daily variability in symptoms; and economic modelling of the cost-effectiveness of introducing biomarker tests for the customisation of lithium treatment into clinical practice. Also, study participants with sub-optimal medication adherence will be offered brief interventions (which can be delivered via a clinician or smartphone app) to enhance treatment engagement and to minimize confounding of lithium non-response with non-adherence. Conclusions The paper outlines the rationale, design and methodology of the first study being undertaken by the newly established R-LiNK collaboration and describes how the project may help to refine the clinical response phenotype and could translate into the personalization of lithium treatment.

Attention-deficit /hyperactivity disorder (ADHD) is a common neurodevelopmental syndrome characterized by age-inappropriate levels of motor activity, impulsivity and attention. The aim of the present study was to study diurnal variation of motor activity in adult ADHD patients, compared to healthy controls and clinical controls with mood and anxiety disorders. Wrist-worn actigraphs were used to record motor activity in a sample of 81 patients and 30 healthy controls. Time series from registrations in the morning and evening were analyzed using measures of variability, complexity and a newly developed method, the similarity algorithm, based on transforming time series into graphs. In healthy controls the evening registrations showed higher variability and lower complexity compared to morning registrations, however this was evident only in the female controls. In the two patient groups the same measures were not significantly different, with one exception, the graph measure bridges. This was the measure that most clearly separated morning and evening registrations and was significantly different both in healthy controls and in patients with a diagnosis of ADHD. These findings suggest that actigraph registrations, combined with mathematical methods based on graph theory, may be used to elucidate the mechanisms responsible for the diurnal regulation of motor activity.