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Jakobsen P, Côté-Allard U, Riegler MA, Stabell LA, Stautland A, Nordgreen T, Torresen J, Fasmer OB, Oedegaard KJ. Early warning signals observed in motor activity preceding mood state change in bipolar disorder. Bipolar Disord 2024. [PMID: 38639725 DOI: 10.1111/bdi.13430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
INTRODUCTION Alterations in motor activity are well-established symptoms of bipolar disorder, and time series of motor activity can be considered complex dynamical systems. In such systems, early warning signals (EWS) occur in a critical transition period preceding a sudden shift (tipping point) in the system. EWS are statistical observations occurring due to a system's declining ability to maintain homeostasis when approaching a tipping point. The aim was to identify critical transition periods preceding bipolar mood state changes. METHODS Participants with a validated bipolar diagnosis were included to a one-year follow-up study, with repeated assessments of the participants' mood. Motor activity was recorded continuously by a wrist-worn actigraph. Participants assessed to have relapsed during follow-up were analyzed. Recognized EWS features were extracted from the motor activity data and analyzed by an unsupervised change point detection algorithm, capable of processing multi-dimensional data and developed to identify when the statistical property of a time series changes. RESULTS Of 49 participants, four depressive and four hypomanic/manic relapses among six individuals occurred, recording actigraphy for 23.8 ± 0.2 h/day, for 39.8 ± 4.6 days. The algorithm detected change points in the time series and identified critical transition periods spanning 13.5 ± 7.2 days. For depressions 11.4 ± 1.8, and hypomania/mania 15.6 ± 10.2 days. CONCLUSION The change point detection algorithm seems capable of recognizing impending mood episodes in continuous flowing data streams. Hence, we present an innovative method for forecasting approaching relapses to improve the clinical management of bipolar disorder.
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Affiliation(s)
- Petter Jakobsen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | | | | | - Lena Antonsen Stabell
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Andrea Stautland
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Tine Nordgreen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Jim Torresen
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole Bernt Fasmer
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Ketil Joachim Oedegaard
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Marciano L, Vocaj E, Bekalu MA, La Tona A, Rocchi G, Viswanath K. The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review. J Med Internet Res 2023; 25:e45540. [PMID: 37725422 PMCID: PMC10548333 DOI: 10.2196/45540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/12/2023] [Accepted: 07/06/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Improving mental health in youth is a major concern. Future approaches to monitor and intervene in youth mental health problems should rely on mobile tools that allow for the daily monitoring of mental health both actively (eg, using ecological momentary assessments [EMAs]) and passively (eg, digital phenotyping) by capturing individuals' data. OBJECTIVE This umbrella review aims to (1) report the main characteristics of existing reviews on mental health and young people, including mobile approaches to mental health; (2) describe EMAs and trace data and the mental health conditions investigated; (3) report the main results; and (4) outline promises, limitations, and directions for future research. METHODS A systematic literature search was carried out in 9 scientific databases (Communication & Mass Media Complete, Psychology and Behavioral Sciences Collection, PsycINFO, CINAHL, ERIC, MEDLINE, the ProQuest Sociology Database, Web of Science, and PubMed) on January 30, 2022, coupled with a hand search and updated in July 2022. We included (systematic) reviews of EMAs and trace data in the context of mental health, with a specific focus on young populations, including children, adolescents, and young adults. The quality of the included reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist. RESULTS After the screening process, 30 reviews (published between 2016 and 2022) were included in this umbrella review, of which 21 (70%) were systematic reviews and 9 (30%) were narrative reviews. The included systematic reviews focused on symptoms of depression (5/21, 24%); bipolar disorders, schizophrenia, or psychosis (6/21, 29%); general ill-being (5/21, 24%); cognitive abilities (2/21, 9.5%); well-being (1/21, 5%); personality (1/21, 5%); and suicidal thoughts (1/21, 5%). Of the 21 systematic reviews, 15 (71%) summarized studies that used mobile apps for tracing, 2 (10%) summarized studies that used them for intervention, and 4 (19%) summarized studies that used them for both intervention and tracing. Mobile tools used in the systematic reviews were smartphones only (8/21, 38%), smartphones and wearable devices (6/21, 29%), and smartphones with other tools (7/21, 33%). In total, 29% (6/21) of the systematic reviews focused on EMAs, including ecological momentary interventions; 33% (7/21) focused on trace data; and 38% (8/21) focused on both. Narrative reviews mainly focused on the discussion of issues related to digital phenotyping, existing theoretical frameworks used, new opportunities, and practical examples. CONCLUSIONS EMAs and trace data in the context of mental health assessments and interventions are promising tools. Opportunities (eg, using mobile approaches in low- and middle-income countries, integration of multimodal data, and improving self-efficacy and self-awareness on mental health) and limitations (eg, absence of theoretical frameworks, difficulty in assessing the reliability and effectiveness of such approaches, and need to appropriately assess the quality of the studies) were further discussed. TRIAL REGISTRATION PROSPERO CRD42022347717; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347717.
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Affiliation(s)
- Laura Marciano
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
| | - Emanuela Vocaj
- Lombard School of Cognitive-Neuropsychological Psychotherapy, Pavia, Italy
| | - Mesfin A Bekalu
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
| | - Antonino La Tona
- Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo, Bergamo, Italy
| | - Giulia Rocchi
- Department of Dynamic, Clinical Psychology and Health Studies, Sapienza University, Rome, Italy
| | - Kasisomayajula Viswanath
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
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Kark SM, Worthington MA, Christie RH, Masino AJ. Opportunities for digital health technology: identifying unmet needs for bipolar misdiagnosis and depression care management. Front Digit Health 2023; 5:1221754. [PMID: 37771820 PMCID: PMC10523347 DOI: 10.3389/fdgth.2023.1221754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/22/2023] [Indexed: 09/30/2023] Open
Abstract
Introduction Digital health technologies (DHTs) driven by artificial intelligence applications, particularly those including predictive models derived with machine learning methods, have garnered substantial attention and financial investment in recent years. Yet, there is little evidence of widespread adoption and scant proof of gains in patient health outcomes. One factor of this paradox is the disconnect between DHT developers and digital health ecosystem stakeholders, which can result in developing technologies that are highly sophisticated but clinically irrelevant. Here, we aimed to uncover challenges faced by psychiatrists treating patients with major depressive disorder (MDD). Specifically, we focused on challenges psychiatrists raised about bipolar disorder (BD) misdiagnosis. Methods We conducted semi-structured interviews with 10 United States-based psychiatrists. We applied text and thematic analysis to the resulting interview transcripts. Results Three main themes emerged: (1) BD is often misdiagnosed, (2) information crucial to evaluating BD is often occluded from clinical observation, and (3) BD misdiagnosis has important treatment implications. Discussion Using upstream stakeholder engagement methods, we were able to identify a narrow, unforeseen, and clinically relevant problem. We propose an organizing framework for development of digital tools based upon clinician-identified unmet need.
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Affiliation(s)
| | | | | | - Aaron J. Masino
- AiCure, New York, NY, United States
- Department of Biostatistics, Epidemiology, and Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Hickie IB, Merikangas KR, Carpenter JS, Iorfino F, Scott EM, Scott J, Crouse JJ. Does circadian dysrhythmia drive the switch into high- or low-activation states in bipolar I disorder? Bipolar Disord 2023; 25:191-199. [PMID: 36661342 PMCID: PMC10947388 DOI: 10.1111/bdi.13304] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVES Emerging evidence suggests a role of circadian dysrhythmia in the switch between "activation" states (i.e., objective motor activity and subjective energy) in bipolar I disorder. METHODS We examined the evidence with respect to four relevant questions: (1) Are natural or environmental exposures that can disrupt circadian rhythms also related to the switch into high-/low-activation states? (2) Are circadian dysrhythmias (e.g., altered rest/activity rhythms) associated with the switch into activation states in bipolar disorder? (3) Do interventions that affect the circadian system also affect activation states? (4) Are associations between circadian dysrhythmias and activation states influenced by other "third" factors? RESULTS Factors that naturally or experimentally alter circadian rhythms (e.g., light exposure) have been shown to relate to activation states; however future studies need to measure circadian rhythms contemporaneously with these natural/experimental factors. Actigraphic measures of circadian dysrhythmias are associated prospectively with the switch into high- or low-activation states, and more studies are needed to establish the most relevant prognostic actigraphy metrics in bipolar disorder. Interventions that can affect the circadian system (e.g., light therapy, lithium) can also reduce the switch into high-/low-activation states. Whether circadian rhythms mediate these clinical effects is an unknown but valuable question. The influence of age, sex, and other confounders on these associations needs to be better characterised. CONCLUSION Based on the reviewed evidence, our view is that circadian dysrhythmia is a plausible driver of transitions into high- and low-activation states and deserves prioritisation in research in bipolar disorders.
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Affiliation(s)
- Ian B. Hickie
- Youth Mental Health and Technology Team, Brain and Mind Centre, Faculty of Medicine and HealthUniversity of SydneyNew South WalesSydneyAustralia
| | - Kathleen R. Merikangas
- Genetic Epidemiology Research Branch, Division of Intramural Research ProgramNational Institute of Mental HealthBethesdaMarylandUSA
| | - Joanne S. Carpenter
- Youth Mental Health and Technology Team, Brain and Mind Centre, Faculty of Medicine and HealthUniversity of SydneyNew South WalesSydneyAustralia
| | - Frank Iorfino
- Youth Mental Health and Technology Team, Brain and Mind Centre, Faculty of Medicine and HealthUniversity of SydneyNew South WalesSydneyAustralia
| | - Elizabeth M. Scott
- Youth Mental Health and Technology Team, Brain and Mind Centre, Faculty of Medicine and HealthUniversity of SydneyNew South WalesSydneyAustralia
| | - Jan Scott
- Institute of NeuroscienceNewcastle UniversityNewcastle upon TyneUK
- Norwegian University of Science and TechnologyTrondheimNorway
- Université de ParisParisFrance
| | - Jacob J. Crouse
- Youth Mental Health and Technology Team, Brain and Mind Centre, Faculty of Medicine and HealthUniversity of SydneyNew South WalesSydneyAustralia
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5
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Origins and consequences of mood flexibility: a computational perspective. Neurosci Biobehav Rev 2023; 147:105084. [PMID: 36764635 DOI: 10.1016/j.neubiorev.2023.105084] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/21/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023]
Abstract
A stable and neutral mood (euthymia) is commended by both economic and clinical perspectives, because it enables rational decisions and avoids mental illnesses. Here we suggest, on the contrary, that a flexible mood responsive to life events may be more adaptive for natural selection, because it can help adjust the behavior to fluctuations in the environment. In our model (dubbed MAGNETO), mood represents a global expected value that biases decisions to forage for a particular reward. When flexible, mood is updated every time an action is taken, by aggregating incurred costs and obtained rewards. Model simulations show that, across a large range of parameters, flexible agents outperform cold agents (with stable neutral mood), particularly when rewards and costs are correlated in time, as naturally occurring across seasons. However, with more extreme parameters, simulations generate short manic episodes marked by incessant foraging and lasting depressive episodes marked by persistent inaction. The MAGNETO model therefore accounts for both the function of mood fluctuations and the emergence of mood disorders.
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Anmella G, Corponi F, Li BM, Mas A, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Garriga M, Agasi I, Bastidas A, Cavero M, Fernández-Plaza T, Arbelo N, Bioque M, García-Rizo C, Verdolini N, Madero S, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young AH, Vieta E, Vergari A, Hidalgo-Mazzei D. Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study. JMIR Mhealth Uhealth 2023; 11:e45405. [PMID: 36939345 DOI: 10.2196/45405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity alongside physiological alterations that wearables can capture. OBJECTIVE We explored whether physiological wearable data could predict: (aim 1) the severity of an acute affective episode at the intra-individual level, (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to the prior predictions, generalization across patients, and associations between affective symptoms and physiological data. METHODS We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded with a research-grade wearable (Empatica E4) across three consecutive timepoints (acute, response, and remission of episode). Euthymic patients and healthy controls (HC) were recorded during a single session (∼48 hours). Manic and depressive symptoms were assessed with standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), temperature (TEMP), blood volume pulse (BVP), heart rate (HR), and electrodermal activity (EDA). For data pre-processing, invalid physiological data were removed using a rule-based filter, channels were time-aligned at 1 second time units and then segmented window lengths of 32 seconds, since those parameters showed the best performances. We developed deep learning predictive models, assessed channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel fully automated method for analysis of physiological data from a research-grade wearable device, including a rule-based filter for invalid data and a viable supervised learning pipeline for time-series analyses. RESULTS 35 sessions (1,512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 HC (age 39.7±12.6; 31.6% female) were analyzed. (aim 1) The severity of mood episodes was predicted with moderate (62%-85%) accuracies. (aim 2) The polarity of episodes was predicted with moderate (70%) accuracy. The most relevant features for the former tasks were ACC, EDA, and HR. Kendall W showed fair agreement (0.383) in feature importance across classification tasks. Generalization of the former models were of overall low accuracy, with better results for the intra-individual models. "Increased motor activity" was associated with ACC (NMI>0.55), "aggressive behavior" with EDA (NMI=1.0), "insomnia" with ACC (NMI∼0.6), "motor inhibition" with ACC (NMI∼0.75), and "psychic anxiety" with EDA (NMI=0.52). CONCLUSIONS Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression respectively. These findings represent a promising pathway towards personalized psychiatry, in which physiological wearable data could allow early identification and intervention of mood episodes. CLINICALTRIAL
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Affiliation(s)
- Gerard Anmella
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Filippo Corponi
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Bryan M Li
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Ariadna Mas
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Miriam Sanabra
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Isabella Pacchiarotti
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Marc Valentí
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Iria Grande
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Antoni Benabarre
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anna Giménez-Palomo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Marina Garriga
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Isabel Agasi
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anna Bastidas
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Myriam Cavero
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | | | - Néstor Arbelo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Miquel Bioque
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Clemente García-Rizo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Norma Verdolini
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Santiago Madero
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Andrea Murru
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Silvia Amoretti
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anabel Martínez-Aran
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Victoria Ruiz
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Giovanna Fico
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Michele De Prisco
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Vincenzo Oliva
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Aleix Solanes
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Barcelona, ES
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Barcelona, ES
| | - Ludovic Samalin
- Department of Psychiatry, CHU Clermont-Ferrand, University of Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal (UMR 6602), Clermont-Ferrand, France., Clermont-Ferrand, FR
| | - Allan H Young
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom., London, GB
| | - Eduard Vieta
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Antonio Vergari
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Diego Hidalgo-Mazzei
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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Abstract
A fast-growing body of evidence from experience sampling studies suggests that affect dynamics are associated with well-being and health. But heterogeneity in experience sampling approaches impedes reproducibility and scientific progress. Leveraging a large dataset of 7016 individuals, each providing over 50 affect reports, we introduce an empirically derived framework to help researchers design well-powered and efficient experience sampling studies. Our research reveals three general principles. First, a sample of 200 participants and 20 observations per person yields sufficient power to detect medium-sized associations for most affect dynamic measures. Second, for trait- and time-independent variability measures of affect (e.g., SD), distant sampling study designs (i.e., a few daily measurements spread out over several weeks) lead to more accurate estimates than close sampling study designs (i.e., many daily measurements concentrated over a few days), although differences in accuracy across sampling methods were inconsistent and of little practical significance for temporally dependent affect dynamic measures (i.e., RMSSD, autocorrelation coefficient, TKEO, and PAC). Third, across all affect dynamics measures, sampling exclusively on specific days or time windows leads to little to no improvement over sampling at random times. Because the ideal sampling approach varies for each affect dynamics measure, we provide a companion R package, an online calculator ( https://sergiopirla.shinyapps.io/powerADapp ), and a series of benchmark effect sizes to help researchers address three fundamental hows of experience sampling: How many participants to recruit? How often to solicit them? And for how long?
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Affiliation(s)
- Sergio Pirla
- Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Maxime Taquet
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Jordi Quoidbach
- Universitat Ramon Llul, ESADE Business School, Barcelona, Spain
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Svensson M, Erhardt S, Hållmarker U, James S, Deierborg T. A physically active lifestyle is associated with lower long-term incidence of bipolar disorder in a population-based, large-scale study. Int J Bipolar Disord 2022; 10:26. [DOI: 10.1186/s40345-022-00272-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/27/2022] [Indexed: 11/05/2022] Open
Abstract
Abstract
Background
Physical activity has been proposed to be beneficial for the symptomatic control of bipolar disorder, but the duration of the effects, sex-specific mechanisms, and impact of exercise intensity are not known.
Method
With an observational study design, we followed skiers and age and sex-matched non-skiers from the general population to investigate if participation in a long-distance cross-country ski race (Vasaloppet) was associated with a lower risk of getting diagnosed with bipolar disorder. Using the Swedish population and patient registries, skiers in Vasaloppet and age and sex-matched non-skiers from the general population were analyzed for any diagnosis of bipolar disorder after participation in the race. Additionally, we used finishing time of the ski race as a proxy for intensity levels to investigate if exercise intensity impacts the risk of bipolar disorder among the physically active skiers.
Results
Previous participation in a long distance ski race (n = 197,685, median age 36 years, 38% women) was associated with a lower incidence of newly diagnosed bipolar compared to an age and sex-matched general population (n = 197,684) during the up to 21 years follow-up (adjusted hazard ratio, HR = 0.48). The finishing time of the race did not significantly impact the risk of bipolar disorder in men. Among women, high performance (measured as the finishing time to complete the race, a proxy for higher exercise dose) was associated with an increased risk of bipolar disorder compared to slower skiing women (HR = 2.07).
Conclusions
Our results confirm that a physically active lifestyle is associated with a lower risk of developing bipolar disorder. Yet, to elucidate the direction of causality in this relationship requires complementary study designs. And the influence of physical performance level on the risk of bipolar disorder warrants further examinations among women.
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Nunes A, Scott K, Alda M. Lessons from ecology for understanding the heterogeneity of bipolar disorder. J Psychiatry Neurosci 2022; 47:E359-E365. [PMID: 36257674 PMCID: PMC9584152 DOI: 10.1503/jpn.220172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Abraham Nunes
- From the Department of Psychiatry, Dalhousie University, Halifax, NS (Nunes, Scott, Alda); and the Faculty of Computer Science, Dalhousie University, Halifax, NS (Nunes)
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Dim light melatonin patterns in unaffected offspring of parents with bipolar disorder: A case-control high-risk study. J Affect Disord 2022; 315:42-47. [PMID: 35878843 DOI: 10.1016/j.jad.2022.07.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/19/2022] [Accepted: 07/17/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Circadian dysregulation has long been thought to be a key component in the pathophysiology of bipolar disorder (BD). However, it remains unclear whether this dysregulation constitutes a risk factor, manifestation, or consequence of BD. This study aimed to compare dim light melatonin secretion patterns between unaffected offspring of parents with BD (OBD) and offspring of control parents (OCP). METHODS This case-control study included unaffected OBD (mean age 14.0 years; male 50.0 %) and age- and sex-matched OCP (mean age 13.0 years; male: 43.5 %). Seventeen saliva samples were collected in dim light conditions. Dim light melatonin onset (DLMO), phase angles, and area under the curve (AUC) were calculated. RESULTS 185 saliva samples from 12 OBD (n = 12) and 741 from OCP (n = 46) were collected. Unaffected OBD had a significant lower nocturnal melatonin level (14.8 ± 4.6 vs. 20.3 ± 11.7 pg/mL) and a smaller melatonin AUC within two hours after DLMO (35.5 ± 11.3 vs. 44.6 ± 18.1 pg/mL) but a significant larger phase angle between DLMO and sleep onset (2.2 ± 1.0 vs. 1.4 ± 1.2 h) than OCP. There was no significant between-group difference in DLMO. The graphic illustrations showed a considerably flattened melatonin secretion in unaffected OBD. LIMITATIONS The main limitations include lack of 24-h dim melatonin secretion measurement, large age range of participants, and small sample size. CONCLUSIONS These findings suggest that unaffected OBD already presented with circadian rhythm dysregulations. Future investigations are needed to clarify the role of abnormal melatonin secretion in the onset of BD.
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12
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Nunes A, Singh S, Allman J, Becker S, Ortiz A, Trappenberg T, Alda M. A critical evaluation of dynamical systems models of bipolar disorder. Transl Psychiatry 2022; 12:416. [PMID: 36171199 PMCID: PMC9519533 DOI: 10.1038/s41398-022-02194-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 12/02/2022] Open
Abstract
Bipolar disorder (BD) is a mood disorder involving recurring (hypo)manic and depressive episodes. The inherently temporal nature of BD has inspired its conceptualization using dynamical systems theory, which is a mathematical framework for understanding systems that evolve over time. In this paper, we provide a critical review of the dynamical systems models of BD. Owing to the heterogeneity of methodological and experimental designs in computational modeling, we designed a structured approach that parallels the appraisal of animal models by their face, predictive, and construct validity. This tool, the validity appraisal guide for computational models (VAG-CM), is not an absolute measure of validity, but rather a guide for a more objective appraisal of models in this review. We identified 26 studies published before November 18, 2021 that proposed generative dynamical systems models of time-varying signals in BD. Two raters independently applied the VAG-CM to the included studies, obtaining a mean Cohen's κ of 0.55 (95% CI [0.45, 0.64]) prior to establishing consensus ratings. Consensus VAG-CM ratings revealed three model/study clusters: data-driven models with face validity, theory-driven models with predictive validity, and theory-driven models lacking all forms of validity. We conclude that future modeling studies should employ a hybrid approach that first operationalizes BD features of interest using empirical data to achieve face validity, followed by explanations of those features using generative models with components that are homologous to physiological or psychological systems involved in BD, to achieve construct validity. Such models would be best developed alongside long-term prospective cohort studies involving a collection of multimodal time-series data. We also encourage future studies to extend, modify, and evaluate the VAG-CM approach for a wider breadth of computational modeling studies and psychiatric disorders.
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Affiliation(s)
- Abraham Nunes
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
| | - Selena Singh
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
| | - Jared Allman
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Suzanna Becker
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
| | - Abigail Ortiz
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Centre for Addiction & Mental Health, Toronto, ON, Canada
| | | | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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13
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McInnis MG, Andreassen OA, Andreazza AC, Alon U, Berk M, Brister T, Burdick KE, Cui D, Frye M, Leboyer M, Mitchell PB, Merikangas K, Nierenberg AA, Nurnberger JI, Pham D, Vieta E, Yatham LN, Young AH. Strategies and foundations for scientific discovery in longitudinal studies of bipolar disorder. Bipolar Disord 2022; 24:499-508. [PMID: 35244317 PMCID: PMC9440950 DOI: 10.1111/bdi.13198] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Bipolar disorder (BD) is a complex and dynamic condition with a typical onset in late adolescence or early adulthood followed by an episodic course with intervening periods of subthreshold symptoms or euthymia. It is complicated by the accumulation of comorbid medical and psychiatric disorders. The etiology of BD remains unknown and no reliable biological markers have yet been identified. This is likely due to lack of comprehensive ontological framework and, most importantly, the fact that most studies have been based on small nonrepresentative clinical samples with cross-sectional designs. We propose to establish large, global longitudinal cohorts of BD studied consistently in a multidimensional and multidisciplinary manner to determine etiology and help improve treatment. Herein we propose collection of a broad range of data that reflect the heterogenic phenotypic manifestations of BD that include dimensional and categorical measures of mood, neurocognitive, personality, behavior, sleep and circadian, life-story, and outcomes domains. In combination with genetic and biological information such an approach promotes the integrating and harmonizing of data within and across current ontology systems while supporting a paradigm shift that will facilitate discovery and become the basis for novel hypotheses.
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Affiliation(s)
| | - Ole A. Andreassen
- NORMENT CentreUniversity of Oslo and Oslo University HospitalOsloNorway
| | - Ana C. Andreazza
- Department of Pharmacology & ToxicologyTemerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
| | | | - Michael Berk
- Deakin UniversityIMPACT – the Institute for Mental and Physical Health and Clinical TranslationSchool of MedicineBarwon HealthGeelongAustralia
- OrygenThe National Centre of Excellence in Youth Mental HealthCentre for Youth Mental HealthFlorey Institute for Neuroscience and Mental Health and the Department of PsychiatryThe University of MelbourneMelbourneAustralia
| | - Teri Brister
- National Alliance on Mental IllnessArlingtonVirginiaUSA
| | | | - Donghong Cui
- Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghai Mental Health CenterShangaiChina
| | | | - Marion Leboyer
- Département de psychiatrieUniversité Paris Est Creteil (UPEC)AP‐HPHôpitaux Universitaires H. MondorDMU IMPACTINSERM, translational NeuropsychiatryFondation FondaMentalCreteilFrance
| | | | - Kathleen Merikangas
- Intramural Research ProgramNational Institute of Mental HealthBethesdaMarylandUSA
| | | | | | - Daniel Pham
- Milken InstituteCenter for Strategic PhilanthopyWashingtonDistrict of ColumbiaUSA
| | - Eduard Vieta
- Bipolar and Depressive disorders UnitHospital ClinicInstitute of NeuroscienceUniversity of BarcelonaIDIBAPSCIBERSAMBarcelonaCataloniaSpain
| | | | - Allan H. Young
- Department of Psychological MedicineInstitute of Psychiatry, Psychology and NeuroscienceKing’s College London & South London and Maudsley NHS Foundation TrustBethlem Royal HospitalBeckenhamKentUK
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14
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Stapp EK, Cui L, Guo W, Paksarian D, Merikangas KR. Comorbidity and familial aggregation of back/neck pain in the NIMH Family Study of Affective Spectrum Disorders. J Psychosom Res 2022; 158:110927. [PMID: 35526400 DOI: 10.1016/j.jpsychores.2022.110927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/26/2022] [Accepted: 04/26/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVE Back pain is associated with substantial Global Burden of Disease and is highly comorbid with mood and anxiety symptoms and syndromes. However, mechanisms underlying this association have not been well-elucidated. Here we apply data from the NIMH Family Study of Affective Spectrum Disorders to investigate the comorbidity, familial aggregation, and cross-aggregation of back/neck pain with mood disorder subtypes. METHODS The sample includes 519 probands and 560 interviewed first-degree relatives. Lifetime DSM-IV Bipolar I, Bipolar II, and Major Depressive Disorder [MDD] were derived from semi-structured diagnostic interviews. Lifetime history of back or neck pain and its age of onset were self-reported retrospectively. Familial aggregation and cross-aggregation were estimated via mixed effects models in probands and interviewed first-degree relatives, while heritability and co-heritability (endophenotypic ranking value [ERV]) were estimated using full pedigrees. RESULTS Over 45% of participants endorsed a history of back/neck pain. Back/neck pain was familial (adjusted odds ratio [aOR] 1.5, p = 0.04; h2 = 0.24, p = 0.009). Back/neck pain in probands was associated with MDD in relatives (aOR 1.5, p = 0.04; ERV = 0.17, p = 0.024), but not with bipolar disorder. Onset of back/neck pain occurred earlier in those with bipolar disorder compared to controls. CONCLUSION Findings suggest common familial risk factors underlying back/neck pain with MDD, whereas there was within-individual comorbidity of bipolar with back/neck pain. Future studies that identify common factors that lead to either back/neck pain or MDD can inform prevention and interventions.
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Affiliation(s)
- Emma K Stapp
- Genetic Epidemiology Branch, Intramural Research Program, NIMH, Bethesda, MD, USA
| | - Lihong Cui
- Genetic Epidemiology Branch, Intramural Research Program, NIMH, Bethesda, MD, USA
| | - Wei Guo
- Genetic Epidemiology Branch, Intramural Research Program, NIMH, Bethesda, MD, USA
| | - Diana Paksarian
- Genetic Epidemiology Branch, Intramural Research Program, NIMH, Bethesda, MD, USA
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15
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Meyrel M, Scott J, Etain B. Chronotypes and circadian rest-activity rhythms in bipolar disorders: a meta-analysis of self- and observer rating scales. Bipolar Disord 2022; 24:286-297. [PMID: 34486201 DOI: 10.1111/bdi.13122] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 07/13/2021] [Accepted: 08/28/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Chronobiological models postulate that abnormalities in circadian rest/activity rhythms (CRAR) are core phenomena of bipolar disorders (BDs). We undertook a meta-analysis of published studies to determine whether self- or observer ratings of CRAR differentiate BD cases from comparators (typically healthy controls [HCs]). METHOD We undertook systematic searches of four databases to identify studies for inclusion in random effects meta-analyses and meta-regression analyses. Effect sizes (ES) for pooled analyses of self- and observer ratings were expressed as standardized mean differences with 95% confidence intervals (CIs). RESULTS The 30 studies meeting eligibility criteria included 2840 cases and 3573 controls. Compared with HC, BD cases showed greater eveningness (ES: 0.33; 95% CI: 0.12-0.54), lower flexibility of rhythms (ES: 0.36; 95% CI: 0.06-0.67), lower amplitude of rhythms (ES: 0.55; 95% CI: 0.39-0.70) and more disturbances across a range of CRAR (ES of 0.78-1.12 for general and social activities, sleep and eating patterns). Between study heterogeneity was high (I2 > 70%) and evidence indicated a potential publication bias for studies using the Biological Rhythms Interview of Assessment in Neuropsychiatry. Meta-regression analyses suggested significantly larger ES were observed in studies using observer ratings or including BD cases with higher levels of depressive symptoms. CONCLUSION This meta-analysis demonstrates that BD is associated with higher levels of self- or observer-rated CRAR disturbances compared with controls. However, further studies should examine the respective performance of individual instruments when used alone or in combination, to clarify their applicability and utility in clinical practice.
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Affiliation(s)
- Manon Meyrel
- Université de Paris, Paris, France.,Département de Psychiatrie et de Médecine Addictologique, APHP.Nord, DMU Neurosciences, GHU Lariboisière - Saint Louis - Fernand Widal, Paris, France
| | - Jan Scott
- Université de Paris, Paris, France.,Département de Psychiatrie et de Médecine Addictologique, APHP.Nord, DMU Neurosciences, GHU Lariboisière - Saint Louis - Fernand Widal, Paris, France.,Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Bruno Etain
- Université de Paris, Paris, France.,Département de Psychiatrie et de Médecine Addictologique, APHP.Nord, DMU Neurosciences, GHU Lariboisière - Saint Louis - Fernand Widal, Paris, France.,INSERM UMRS-1144, Université de Paris, Paris, France
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16
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Bos FM, Schreuder MJ, George SV, Doornbos B, Bruggeman R, van der Krieke L, Haarman BCM, Wichers M, Snippe E. Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals. Int J Bipolar Disord 2022; 10:12. [PMID: 35397076 PMCID: PMC8994809 DOI: 10.1186/s40345-022-00258-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 03/01/2022] [Indexed: 11/30/2022] Open
Abstract
Background In bipolar disorder treatment, accurate episode prediction is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. The present study investigated whether EWS can anticipate manic and depressive transitions in individual patients with bipolar disorder. Methods Twenty bipolar type I/II patients (with ≥ 2 episodes in the previous year) participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months (Mean = 491 observations per person). Transitions were determined by weekly completed questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA states. Positive and negative predictive values were calculated to determine clinical utility. Results Eleven patients reported 1–2 transitions. The presence of EWS increased the probability of impending depressive and manic transitions from 32-36% to 46–48% (autocorrelation) and 29–41% (standard deviation). However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The momentary states that indicated nearby transitions most accurately (predictive values: 65–100%) were full of ideas, worry, and agitation. Large individual differences in the utility of EWS were found. Conclusions EWS show theoretical promise in anticipating manic and depressive transitions in bipolar disorder, but the level of false positives and negatives, as well as the heterogeneity within and between individuals and preprocessing methods currently limit clinical utility. Supplementary Information The online version contains supplementary material available at 10.1186/s40345-022-00258-4.
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Affiliation(s)
- Fionneke M Bos
- Department of Psychiatry, Rob Giel Research Center, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands. .,Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Marieke J Schreuder
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sandip V George
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Computer Science , University College London , London, United Kingdom
| | - Bennard Doornbos
- Lentis Research, Lentis Psychiatric Institute, Groningen, The Netherlands
| | - Richard Bruggeman
- Department of Psychiatry, Rob Giel Research Center, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Lian van der Krieke
- Department of Psychiatry, Rob Giel Research Center, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.,Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bartholomeus C M Haarman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marieke Wichers
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Evelien Snippe
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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17
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Brickman HM, Fristad MA. Psychosocial Treatments for Bipolar Disorder in Children and Adolescents. Annu Rev Clin Psychol 2022; 18:291-327. [PMID: 35216522 DOI: 10.1146/annurev-clinpsy-072220-021237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Evidence suggests that adjunctive psychosocial intervention for the treatment of pediatric bipolar spectrum disorders (BPSDs) is effective, feasible, and highly accepted as both an acute and maintenance treatment for youth with BPSD diagnoses as well as a preventive treatment for high-risk youth who are either asymptomatic or exhibit subsyndromal mood symptoms. Here, we provide a comprehensive review of all known evidence-based interventions, including detailed descriptions of treatment targets and core components, results of clinical trials, and updated research on mediators and moderators of treatment efficacy. Treatments are presented systematically according to level of empirical support (i.e., well established, probably efficacious, possibly efficacious, experimental, or questionable); upcoming and ongoing trials are included when possible. In line with a staging approach, preventive interventions are presented separately. Recommendations for best practices based on age, stage, and additional evidence-based child and family factors shown to affect treatment outcomes are provided. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Haley M Brickman
- Big Lots Behavioral Health Services and Division of Child and Family Psychiatry, Nationwide Children's Hospital, Columbus, Ohio; ,
| | - Mary A Fristad
- Big Lots Behavioral Health Services and Division of Child and Family Psychiatry, Nationwide Children's Hospital, Columbus, Ohio; ,
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18
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Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder. PLoS One 2022; 17:e0262232. [PMID: 35061801 PMCID: PMC8782466 DOI: 10.1371/journal.pone.0262232] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 12/20/2021] [Indexed: 02/07/2023] Open
Abstract
Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually in motor activity. The aim of this study was to characterize differences in motor activity when comparing manic patients to their euthymic selves. Motor activity was collected from 16 bipolar inpatients in mania and remission. 24-h recordings and 2-h time series in the morning and evening were analyzed for mean activity, variability and complexity. Lastly, the recordings were analyzed with the similarity graph algorithm and graph theory concepts such as edges, bridges, connected components and cliques. The similarity graph measures fluctuations in activity reasonably comparable to both variability and complexity measures. However, direct comparisons are difficult as most graph measures reveal variability in constricted time windows. Compared to sample entropy, the similarity graph is less sensitive to outliers. The little-understood estimate Bridges is possibly revealing underlying dynamics in the time series. When compared to euthymia, over the duration of approximately one circadian cycle, the manic state presented reduced variability, displayed by decreased standard deviation (p = 0.013) and augmented complexity shown by increased sample entropy (p = 0.025). During mania there were also fewer edges (p = 0.039) and more bridges (p = 0.026). Similar significant changes in variability and complexity were observed in the 2-h morning and evening sequences, mainly in the estimates of the similarity graph algorithm. Finally, augmented complexity was present in morning samples during mania, displayed by increased sample entropy (p = 0.015). In conclusion, the motor activity of mania is characterized by altered complexity and variability when compared within-subject to euthymia.
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19
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Panchal P, de Queiroz Campos G, Goldman DA, Auerbach RP, Merikangas KR, Swartz HA, Sankar A, Blumberg HP. Toward a Digital Future in Bipolar Disorder Assessment: A Systematic Review of Disruptions in the Rest-Activity Cycle as Measured by Actigraphy. Front Psychiatry 2022; 13:780726. [PMID: 35677875 PMCID: PMC9167949 DOI: 10.3389/fpsyt.2022.780726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Disruptions in rest and activity patterns are core features of bipolar disorder (BD). However, previous methods have been limited in fully characterizing the patterns. There is still a need to capture dysfunction in daily activity as well as rest patterns in order to more holistically understand the nature of 24-h rhythms in BD. Recent developments in the standardization, processing, and analyses of wearable digital actigraphy devices are advancing longitudinal investigation of rest-activity patterns in real time. The current systematic review aimed to summarize the literature on actigraphy measures of rest-activity patterns in BD to inform the future use of this technology. METHODS A comprehensive systematic review using PRISMA guidelines was conducted through PubMed, MEDLINE, PsycINFO, and EMBASE databases, for papers published up to February 2021. Relevant articles utilizing actigraphy measures were extracted and summarized. These papers contributed to three research areas addressed, pertaining to the nature of rest-activity patterns in BD, and the effects of therapeutic interventions on these patterns. RESULTS Seventy articles were included. BD was associated with longer sleep onset latency and duration, particularly during depressive episodes and with predictive value for worsening of future manic symptoms. Lower overall daily activity was also associated with BD, especially during depressive episodes, while more variable activity patterns within a day were seen in mania. A small number of studies linked these disruptions with differential patterns of brain functioning and cognitive impairments, as well as more adverse outcomes including increased suicide risk. The stabilizing effect of therapeutic options, including pharmacotherapies and chronotherapies, on activity patterns was supported. CONCLUSION The use of actigraphy provides valuable information about rest-activity patterns in BD. Although results suggest that variability in rhythms over time may be a specific feature of BD, definitive conclusions are limited by the small number of studies assessing longitudinal changes over days. Thus, there is an urgent need to extend this work to examine patterns of rhythmicity and regularity in BD. Actigraphy research holds great promise to identify a much-needed specific phenotypic marker for BD that will aid in the development of improved detection, treatment, and prevention options.
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Affiliation(s)
- Priyanka Panchal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.,Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | | | - Danielle A Goldman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.,Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
| | - Randy P Auerbach
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Kathleen R Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, MD, United States
| | - Holly A Swartz
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Anjali Sankar
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.,Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Hilary P Blumberg
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.,Department of Radiology and Biomedical Imaging, and the Child Study Center, Yale School of Medicine, New Haven, CT, United States
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20
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Passos IC, Ballester P, Rabelo-da-Ponte FD, Kapczinski F. Precision Psychiatry: The Future Is Now. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2022; 67:21-25. [PMID: 33757313 PMCID: PMC8807995 DOI: 10.1177/0706743721998044] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Affiliation(s)
- Ives Cavalcante Passos
- Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Rio Grande do Sul, Brazil.,Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, Rio Grande do Sul, Brazil.,Department of Psychiatry, School of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, 28124Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Pedro Ballester
- Neuroscience Graduate Program, 3710McMaster University, Hamilton, Ontario, Canada
| | - Francisco Diego Rabelo-da-Ponte
- Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Rio Grande do Sul, Brazil.,Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, Rio Grande do Sul, Brazil.,Department of Psychiatry, School of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, 28124Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Flavio Kapczinski
- Department of Psychiatry and Behavioural Neurosciences, 3710McMaster University, Hamilton, Ontario, Canada
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21
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Klein A, Clucas J, Krishnakumar A, Ghosh SS, Van Auken W, Thonet B, Sabram I, Acuna N, Keshavan A, Rossiter H, Xiao Y, Semenuta S, Badioli A, Konishcheva K, Abraham SA, Alexander LM, Merikangas KR, Swendsen J, Lindner AB, Milham MP. Remote Digital Psychiatry for Mobile Mental Health Assessment and Therapy: MindLogger Platform Development Study. J Med Internet Res 2021; 23:e22369. [PMID: 34762054 PMCID: PMC8663601 DOI: 10.2196/22369] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/03/2021] [Accepted: 03/16/2021] [Indexed: 01/23/2023] Open
Abstract
Background Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. Objective This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. Methods We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform’s configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. Results Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users’ data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser–based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. Conclusions We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health–related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population.
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Affiliation(s)
- Arno Klein
- MATTER Lab, Child Mind Institute, New York, NY, United States
| | - Jon Clucas
- MATTER Lab, Child Mind Institute, New York, NY, United States.,Computational Neuroimaging Lab, Child Mind Institute, New York, NY, United States
| | - Anirudh Krishnakumar
- MATTER Lab, Child Mind Institute, New York, NY, United States.,Université de Paris and INSERM U1284 SEED unit, Centre for Research and Interdisciplinarity (CRI), Paris, France.,ETH Library Lab, ETH Zurich and Citizen Science Centre, Zurich, Switzerland
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States.,Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Cambridge, MA, United States
| | | | - Benjamin Thonet
- MATTER Lab, Child Mind Institute, New York, NY, United States.,Université de Paris and INSERM U1284 SEED unit, Centre for Research and Interdisciplinarity (CRI), Paris, France
| | - Ihor Sabram
- MATTER Lab, Child Mind Institute, New York, NY, United States
| | - Nino Acuna
- MATTER Lab, Child Mind Institute, New York, NY, United States
| | - Anisha Keshavan
- MATTER Lab, Child Mind Institute, New York, NY, United States.,Octave Bioscience, Menlo Park, CA, United States
| | - Henry Rossiter
- Computational Engineering, University of Texas at Austin, Austin, TX, United States
| | - Yao Xiao
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
| | - Sergey Semenuta
- MATTER Lab, Child Mind Institute, New York, NY, United States
| | | | - Kseniia Konishcheva
- MATTER Lab, Child Mind Institute, New York, NY, United States.,Université de Paris and INSERM U1284 SEED unit, Centre for Research and Interdisciplinarity (CRI), Paris, France
| | - Sanu Ann Abraham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Lindsay M Alexander
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
| | | | - Joel Swendsen
- National Center for Scientific Research, University of Bordeaux, EPHE PSL University, Bordeaux, France
| | - Ariel B Lindner
- Université de Paris and INSERM U1284 SEED unit, Centre for Research and Interdisciplinarity (CRI), Paris, France
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
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22
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Moon E, Yang M, Seon Q, Linnaranta O. Relevance of Objective Measures in Psychiatric Disorders-Rest-Activity Rhythm and Psychophysiological Measures. Curr Psychiatry Rep 2021; 23:85. [PMID: 34714422 PMCID: PMC8556205 DOI: 10.1007/s11920-021-01291-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/08/2021] [Indexed: 12/28/2022]
Abstract
PURPOSE OF REVIEW We present a review of recent methods of objective measurement in psychiatry and psychology with a focus on home monitoring and its utility in guiding treatment. RECENT FINDINGS For individualized diagnostics and treatment of insomnia, actigraphy can generate clinically useful graphical presentations of sleep timing and patterns. Psychophysiological measures may complement psychometrics by tracking parallel changes in physiological responses and emotional functioning, especially during therapy for trauma symptoms and emotion regulation. It seems that rather than defining universal cut-offs, an individualised range of variability could characterize treatment response. Wearable actigraphy and psychophysiological sensors are promising devices to provide biofeedback and guide treatment. Use of feasible and reliable technology during experimental and clinical procedures may necessitate defining healthy and abnormal responses in different populations and pathological states. We present a "call for action" towards further collaborative work to enable large scale use of objective measures.
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Affiliation(s)
- Eunsoo Moon
- Department of Psychiatry, Pusan National University School of Medicine, Yangsan, Republic of Korea
- Department of Psychiatry and Biomedical Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Michelle Yang
- Interdisciplinary Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - Quinta Seon
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Outi Linnaranta
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Mental Health Unit, Finnish Institute for Health and Welfare, P.O. Box 30, 00271, Helsinki, Finland.
- Douglas Centre for Sleep and Biological Rhythms, Douglas Mental Health University Institute, 6875 LaSalle Boulevard, Montreal, QC, H4H 1R3, Canada.
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23
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Crouse JJ, Carpenter JS, Song YJC, Hockey SJ, Naismith SL, Grunstein RR, Scott EM, Merikangas KR, Scott J, Hickie IB. Circadian rhythm sleep-wake disturbances and depression in young people: implications for prevention and early intervention. Lancet Psychiatry 2021; 8:813-823. [PMID: 34419186 DOI: 10.1016/s2215-0366(21)00034-1] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 12/19/2022]
Abstract
A rate-limiting step in the prevention and early intervention of depressive disorders in young people is our insufficient understanding of causal mechanisms. One plausible pathophysiological pathway is disturbance in the 24 h sleep-wake cycle and the underlying circadian system. Abnormalities in circadian rhythms are well documented in adults with various depressive disorders and have been linked to core clinical features, including unstable mood, daytime fatigue, non-restorative sleep, reduced motor activity, somatic symptoms, and appetite and weight change. In this Review, we summarise four areas of research: basic circadian biology and animal models of circadian disturbances; developmental changes in circadian rhythms during adolescence and implications for the emergence of adolescent-onset depressive syndromes; community and clinical studies linking 24 h sleep-wake cycle disturbances and depressive disorders; and clinical trials of circadian-based treatments. We present recommendations based on a highly personalised, early intervention model for circadian-linked depression in young people.
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Affiliation(s)
- Jacob J Crouse
- Youth Mental Health and Technology Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.
| | - Joanne S Carpenter
- Youth Mental Health and Technology Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Yun Ju C Song
- Youth Mental Health and Technology Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Samuel J Hockey
- Youth Mental Health and Technology Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Sharon L Naismith
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Ronald R Grunstein
- Woolcock Institute of Medical Research, Sleep and Circadian Research Group, Sydney, NSW, Australia
| | - Elizabeth M Scott
- St Vincent's and Mater Clinical School, The University of Notre Dame, Sydney, NSW, Australia
| | - Kathleen R Merikangas
- Genetic Epidemiology Research Branch, Division of Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Jan Scott
- Academic Psychiatry, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Ian B Hickie
- Youth Mental Health and Technology Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
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24
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Camacho E, Brady RO, Lizano P, Keshavan M, Torous J. Advancing translational research through the interface of digital phenotyping and neuroimaging: A narrative review. Biomark Neuropsychiatry 2021. [DOI: 10.1016/j.bionps.2021.100032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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25
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Ressler KJ, Williams LM. Big data in psychiatry: multiomics, neuroimaging, computational modeling, and digital phenotyping. Neuropsychopharmacology 2021; 46:1-2. [PMID: 32919403 PMCID: PMC7689454 DOI: 10.1038/s41386-020-00862-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 09/03/2020] [Indexed: 12/23/2022]
Affiliation(s)
- Kerry J Ressler
- McLean Hospital and Harvard Medical School, Belmont, MA, 02478, USA.
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