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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; 26:468-478. [PMID: 38639725 DOI: 10.1111/bdi.13430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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Lehnertz K. Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems. CHAOS (WOODBURY, N.Y.) 2024; 34:072102. [PMID: 38985967 DOI: 10.1063/5.0214733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/21/2024] [Indexed: 07/12/2024]
Abstract
Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions, and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.
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Wiltshire TJ, van Eijndhoven K, Halgas E, Gevers JMP. Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams. Top Cogn Sci 2024; 16:391-429. [PMID: 35261211 DOI: 10.1111/tops.12606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/02/2022] [Accepted: 02/04/2022] [Indexed: 11/26/2022]
Abstract
Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.
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Affiliation(s)
- Travis J Wiltshire
- Department of Cognitive Science and Artificial Intelligence, Tilburg University
| | | | - Elwira Halgas
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology
| | - Josette M P Gevers
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology
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Scheffer M, Bockting CL, Borsboom D, Cools R, Delecroix C, Hartmann JA, Kendler KS, van de Leemput I, van der Maas HLJ, van Nes E, Mattson M, McGorry PD, Nelson B. A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review. JAMA Psychiatry 2024; 81:624-630. [PMID: 38568618 DOI: 10.1001/jamapsychiatry.2024.0228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Importance Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry. Observations Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions. Conclusions and Relevance Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.
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Michaelis R, Schöller H, Popkirov S, Edelhäuser F, Kolenik T, Trinka E, Schiepek G. Psychological precursors of epileptic seizures. Epilepsia 2024; 65:e35-e40. [PMID: 38100099 DOI: 10.1111/epi.17865] [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: 08/24/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 01/14/2024]
Abstract
Psychological stress is the most commonly self-reported precursor of epileptic seizures. However, retrospective and prospective studies remain inconclusive in this regard. Here, we explored whether seizures would be preceded by significant changes in reported stressors or resource utilization. This study is based on high-frequency time series through daily online completion of personalized questionnaires of 9-24 items in epilepsy outpatients and compared responses 1-14 days before seizures with interictal time series. Fourteen patients (79% women, age = 23-64 years) completed daily questionnaires over a period of 87-898 days (median = 277 days = 9.2 months). A total of 4560 fully completed daily questionnaires were analyzed, 685 of which included reported seizure events. Statistically significant changes in preictal compared to interictal dynamics were found in 11 of 14 patients (79%) across 41 items (22% of all 187 items). In seven of 14 patients (50%), seizures were preceded by a significant mean increase of stressors and/or a significant mean decrease of resource utilization. This exploratory analysis of long-term prospective individual patient data on specific stressors and personal coping strategies generates the hypothesis that medium-term changes in psychological well-being may precede the occurrence of epileptic seizures in some patients.
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Affiliation(s)
- Rosa Michaelis
- Witten/Herdecke University, Herdecke, Germany
- Department of Neurology, University Hospital Knappschaftskrankenhaus Bochum, Ruhr University Bochum, Bochum, Germany
| | - Helmut Schöller
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria
- University Hospital of Psychiatry, Psychotherapy, and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Stoyan Popkirov
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Friedrich Edelhäuser
- Witten/Herdecke University, Herdecke, Germany
- Department of Early Rehabilitation, Gemeinschaftskrankenhaus Herdecke, Herdecke, Germany
| | - Tine Kolenik
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria
- University Hospital of Psychiatry, Psychotherapy, and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Neurointensive Care, and Neurorehabilitation, member of the European Reference Network EpiCARE, Christian Doppler University Hospital, Paracelsus Medical University, Center for Cognitive Neuroscience, Salzburg, Austria
- Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Center for Cognitive Neuroscience, Salzburg, Austria
| | - Günter Schiepek
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria
- University Hospital of Psychiatry, Psychotherapy, and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
- Faculty of Psychology and Educational Sciences, Ludwig Maximilian University of Munich, Munich, Germany
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Masuda N, Aihara K, MacLaren NG. Anticipating regime shifts by mixing early warning signals from different nodes. Nat Commun 2024; 15:1086. [PMID: 38316802 PMCID: PMC10844243 DOI: 10.1038/s41467-024-45476-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/25/2024] [Indexed: 02/07/2024] Open
Abstract
Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.
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Affiliation(s)
- Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, 14260-2900, USA.
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, NY, 14260-5030, USA.
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Bunkyo City, Japan
| | - Neil G MacLaren
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, 14260-2900, USA
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Heino MTJ, Proverbio D, Marchand G, Resnicow K, Hankonen N. Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation. Health Psychol Rev 2023; 17:655-672. [PMID: 36420691 PMCID: PMC10261543 DOI: 10.1080/17437199.2022.2146598] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022]
Abstract
Models and theories in behaviour change science are not in short supply, but they almost exclusively pertain to a particular facet of behaviour, such as automaticity or reasoned action, or to a single scale of observation such as individuals or communities. We present a highly generalisable conceptual model which is widely used in complex systems research from biology to physics, in an accessible form to behavioural scientists. The proposed model of attractor landscapes can be used to understand human behaviour change on different levels, from individuals to dyads, groups and societies. We use the model as a tool to present neglected ideas in contemporary behaviour change science, such as hysteresis and nonlinearity. The model of attractor landscapes can deepen understanding of well-known features of behaviour change (research), including short-livedness of intervention effects, problematicity of focusing on behavioural initiation while neglecting behavioural maintenance, continuum and stage models of behaviour change understood within a single accommodating framework, and the concept of resilience. We also demonstrate potential methods of analysis and outline avenues for future research.
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Affiliation(s)
| | | | | | - Kenneth Resnicow
- School of Public Health, University of Michigan. Rogel Cancer Center University of Michigan
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Dablander F, Heesterbeek H, Borsboom D, Drake JM. Overlapping timescales obscure early warning signals of the second COVID-19 wave. Proc Biol Sci 2022; 289:20211809. [PMID: 35135355 PMCID: PMC8825995 DOI: 10.1098/rspb.2021.1809] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/13/2022] [Indexed: 11/12/2022] Open
Abstract
Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.
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Affiliation(s)
- Fabian Dablander
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Hans Heesterbeek
- Department of Population Health Sciences, Utrecht University, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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