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van den Brink B, Jongkind M, Delespaul P, Braam AW, Schaap-Jonker H, Giltay EJ. Experience sampling of suicidality, religiosity and spirituality in depression: Network analyses using dynamic time warping. J Affect Disord 2024; 360:354-363. [PMID: 38815764 DOI: 10.1016/j.jad.2024.05.139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 04/20/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND Suicidality is a clinically important and multifaceted phenomenon, frequently present in depressed subjects. Religiosity and spirituality (R/S) can have an attenuating as well as a reinforcing effect on suicidality. METHODS From two Dutch mental health care settings, a sample of 31 depressed and in- and outpatients with suicidal ideation, self-identifying as being religious or spiritual, was selected by convenience sampling. Using an experience sampling method (ESM) mobile application, during six days (mean of 42 assessments per subject), the association between symptoms of depression, suicidality, and specific positive-supportive affective R/S and positive psychology variables. For 28 participants symptom network plots on a group level, and on an individual level, were analyzed using dynamic time warping (DTW). RESULTS Participants were on average 35.7 years old, and 65 % were women. In the group-level undirected network, R/S variables were linked to positive psychology variables via a bridge function of inner peace. Changes in the experience of inner peace and enjoying a physical activity preceded changes of several other symptoms. A network dynamic appeared with a dense cluster of 'positive psychology' items. LIMITATIONS Only a limited number of R/S variables were included. CONCLUSION The results of this study suggest that religiosity and spirituality function as meaningful factors in depression and suicidality in religiously or spiritually engaged persons. Experienced inner peace has a positive association with reasons to live. Experience sampling method data can be effectively analyzed using dynamic time warping. Exploring individual religious or spiritual engagement can prove important in treating suicidality and depression.
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Affiliation(s)
- Bart van den Brink
- Center for Research and Innovation in Christian Mental Health Care (Kicg), Hoevelaken, the Netherlands; Department of Emergency Psychiatry, GGz Centraal, Amersfoort, the Netherlands.
| | - Matthias Jongkind
- Jongkind Clinical Psychology and Psychiatry, Utrecht, the Netherlands
| | | | - Arjan W Braam
- Department of Humanist Chaplaincy Studies for a Plural Society, University of Humanistic Studies, the Netherlands; Department of Emergency Psychiatry and Department of Residency Training, Altrecht Mental Health Care, Utrecht, the Netherlands.
| | - Hanneke Schaap-Jonker
- Center for Research and Innovation in Christian Mental Health Care (Kicg), Hoevelaken, the Netherlands; Faculty of Religion and Theology, Vrije Universiteit, Amsterdam, the Netherlands.
| | - Erik J Giltay
- Leiden University Medical Center, Leiden, the Netherlands; Health Campus The Hague, Department of Public Health & Primary Care, Leiden University Medical Center, Leiden, the Netherlands.
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van der Slot AJC, Bertens AS, Trompet S, Mooijaart SP, Gussekloo J, van den Bos F, Giltay EJ. Temporal dynamics of depressive symptoms and cognitive decline in the oldest old: dynamic time warp analysis of the Leiden 85-plus study. Age Ageing 2024; 53:afae130. [PMID: 38952188 PMCID: PMC11217552 DOI: 10.1093/ageing/afae130] [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: 12/04/2023] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND The prevalence of depressive symptoms and cognitive decline increases with age. We investigated their temporal dynamics in individuals aged 85 and older across a 5-year follow-up period. METHODS Participants were selected from the Leiden 85-plus study and were eligible if at least three follow-up measurements were available (325 of 599 participants). Depressive symptoms were assessed at baseline and at yearly assessments during a follow-up period of up to 5 years, using the 15-item Geriatric Depression Scale (GDS-15). Cognitive decline was measured through various tests, including the Mini Mental State Exam, Stroop test, Letter Digit Coding test and immediate and delayed recall. A novel method, dynamic time warping analysis, was employed to model their temporal dynamics within individuals, in undirected and directed time-lag analyses, to ascertain whether depressive symptoms precede cognitive decline in group-level aggregated results or vice versa. RESULTS The 325 participants were all 85 years of age at baseline; 68% were female, and 45% received intermediate to higher education. Depressive symptoms and cognitive functioning significantly covaried in time, and directed analyses showed that depressive symptoms preceded most of the constituents of cognitive impairment in the oldest old. Of the GDS-15 symptoms, those with the strongest outstrength, indicating changes in these symptoms preceded subsequent changes in other symptoms, were worthlessness, hopelessness, low happiness, dropping activities/interests, and low satisfaction with life (all P's < 0.01). CONCLUSION Depressive symptoms preceded cognitive impairment in a population based sample of the oldest old.
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Affiliation(s)
- Abe J C van der Slot
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Anne Suzanne Bertens
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Mental Health Care Rivierduinen, Old Age Psychiatry Outpatient Clinic, Leiden, The Netherlands
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center 2333 ZA Leiden, The Netherlands
- LUMC Center for Medicine for Older People, Leiden University Medical Center, Leiden, The Netherlands
| | - Simon P Mooijaart
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center 2333 ZA Leiden, The Netherlands
- LUMC Center for Medicine for Older People, Leiden University Medical Center, Leiden, The Netherlands
| | - Jacobijn Gussekloo
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center 2333 ZA Leiden, The Netherlands
- LUMC Center for Medicine for Older People, Leiden University Medical Center, Leiden, The Netherlands
| | - Frederiek van den Bos
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center 2333 ZA Leiden, The Netherlands
- LUMC Center for Medicine for Older People, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik J Giltay
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Health Campus The Hague, Department of Public Health and Primary Care, Leiden University Medical Center, The Hague, The Netherlands
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Schumacher L, Burger J, Echterhoff J, Kriston L. Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections. MULTIVARIATE BEHAVIORAL RESEARCH 2024; 59:663-676. [PMID: 38733300 DOI: 10.1080/00273171.2024.2335401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.
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Affiliation(s)
- Lea Schumacher
- Department of Medical Psychology, University Medical Center Hamburg-Eppendorf
| | - Julian Burger
- Department of Psychological Methods, University of Amsterdam
- University Medical Center Groningen, University of Groningen
- Centre for Urban Mental Health, University of Amsterdam
| | - Jette Echterhoff
- Department of Medical Psychology, University Medical Center Hamburg-Eppendorf
| | - Levente Kriston
- Department of Medical Psychology, University Medical Center Hamburg-Eppendorf
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de Beurs D, Giltay EJ, Nuij C, O'Connor R, de Winter RFP, Kerkhof A, van Ballegooijen W, Riper H. Symptoms of a feather flock together? An exploratory secondary dynamic time warp analysis of 11 single case time series of suicidal ideation and related symptoms. Behav Res Ther 2024; 178:104572. [PMID: 38833835 DOI: 10.1016/j.brat.2024.104572] [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: 02/29/2024] [Revised: 05/09/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024]
Abstract
Suicidal ideation fluctuates over time, as does its related risk factors. Little is known about the difference or similarities of the temporal patterns. The current exploratory secondary analysis examines which risk symptoms have similar time dynamics using a mathematical algorithm called dynamic time warping (DTW). Ecological momentary assessment data was used of 11 depressed psychiatric outpatients with suicidal ideation who answered three daytime surveys at semi-random sampling points for a period of three to six months. Patients with 45 assessments or more were included. Results revealed significant inter-individual variability in symptom dynamics and clustering, with certain symptoms often clustering due to similar temporal patterns, notably feeling sad, hopelessness, feeling stuck, and worrying. The directed network analyses shed light on the temporal order, highlighting entrapment and worrying as symptoms strongly related to suicide ideation. Still, all patients also showed unique directed networks. While for some patients changes in entrapment directly preceded change in suicide ideation, the reverse temporal ordering was also found. Relatedly, within some patients, perceived burdensomeness played a pivotal role, whereas in others it was unconnected to other symptoms. The study underscores the individualized nature of symptom dynamics and challenges linear models of progression, advocating for personalized treatment strategies.
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Affiliation(s)
- Derek de Beurs
- Department of Clinical Psychology, University of Amsterdam, Amsterdam, the Netherlands.
| | - Erik J Giltay
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands.
| | - Chani Nuij
- Faculty of Behavioral and Movement Sciences, Department of Clinical, Neuro- and Developmental Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | - Rory O'Connor
- Suicidal Behavior Research Laboratory, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Remco F P de Winter
- Mental Health Institution GGZ Rivierduinen, the Netherlands; MHeNs School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Ad Kerkhof
- Faculty of Behavioral and Movement Sciences, Department of Clinical, Neuro- and Developmental Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | - Wouter van Ballegooijen
- Faculty of Behavioral and Movement Sciences, Department of Clinical, Neuro- and Developmental Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | - Heleen Riper
- Faculty of Behavioral and Movement Sciences, Department of Clinical, Neuro- and Developmental Psychology, Vrije Universiteit Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
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5
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Ebrahimi OV, Borsboom D, Hoekstra RHA, Epskamp S, Ostinelli EG, Bastiaansen JA, Cipriani A. Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder. Br J Psychiatry 2024; 224:157-163. [PMID: 38584324 PMCID: PMC11039556 DOI: 10.1192/bjp.2024.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 12/14/2023] [Accepted: 01/16/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. AIMS To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. METHOD Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. RESULTS Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. CONCLUSIONS The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.
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Affiliation(s)
- Omid V. Ebrahimi
- Department of Experimental Psychology, University of Oxford, Oxford, UK; and Department of Psychology , University of Oslo, Oslo, Norway
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Ria H. A. Hoekstra
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore, Singapore, Singapore
| | - Edoardo G. Ostinelli
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Jojanneke A. Bastiaansen
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; and Friesland Mental Health Care Services, Leeuwarden, The Netherlands
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
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Li K, Thindwa D, Weinberger DM, Pitzer VE. The role of viral interference in shaping RSV epidemics following the 2009 H1N1 influenza pandemic. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.25.24303336. [PMID: 38464193 PMCID: PMC10925368 DOI: 10.1101/2024.02.25.24303336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Respiratory syncytial virus (RSV) primarily affects infants, young children, and older adults, with seasonal outbreaks in the United States (US) peaking around December or January. Despite the limited implementation of non-pharmaceutical interventions, disrupted RSV activity was observed in different countries following the 2009 influenza pandemic, suggesting possible viral interference from influenza. Although interactions between the influenza A/H1N1 pandemic virus and RSV have been demonstrated at an individual level, it remains unclear whether the disruption of RSV activity at the population level can be attributed to viral interference. In this work, we first evaluated changes in the timing and intensity of RSV activity across 10 regions of the US in the years following the 2009 influenza pandemic using dynamic time warping. We observed a reduction in RSV activity following the pandemic, which was associated with intensity of influenza activity in the region. We then developed an age-stratified, two-pathogen model to examine various hypotheses regarding viral interference mechanisms. Based on our model estimates, we identified three mechanisms through which influenza infections could interfere with RSV: 1) reducing susceptibility to RSV coinfection; 2) shortening the RSV infectious period in coinfected individuals; and 3) reducing RSV infectivity in coinfection. Our study offers statistical support for the occurrence of atypical RSV seasons following the 2009 influenza pandemic. Our work also offers new insights into the mechanisms of viral interference that contribute to disruptions in RSV epidemics and provides a model-fitting framework that enables the analysis of new surveillance data for studying viral interference at the population level.
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Affiliation(s)
- Ke Li
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Deus Thindwa
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Daniel M Weinberger
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
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Mesbah R, Koenders MA, Spijker AT, de Leeuw M, van Hemert AM, Giltay EJ. Dynamic time warp analysis of individual symptom trajectories in individuals with bipolar disorder. Bipolar Disord 2024; 26:44-57. [PMID: 37269209 DOI: 10.1111/bdi.13340] [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: 06/04/2023]
Abstract
BACKGROUND Manic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic Time Warp (DTW) is an algorithm that may capture symptom interactions from panel data with sparse observations over time. METHODS The Young Mania Rating Scale and Quick Inventory of Depressive Symptomatology were repeatedly assessed in 141 individuals with BD, with on average 5.5 assessments per subject every 3-6 months. Dynamic Time Warp calculated the distance between each of the 27 × 27 pairs of standardized symptom scores. The changing profile of standardized symptom scores of BD participants was analyzed in individual subjects, yielding symptom dimensions in aggregated group-level analyses. Using an asymmetric time-window, symptom changes that preceded other symptom changes (i.e., Granger causality) yielded a directed network. RESULTS The mean age of the BD participants was 40.1 (SD 13.5) years old, and 60% were female participants. Idiographic symptom networks were highly variable between subjects. Yet, nomothetic analyses showed five symptom dimensions: core (hypo)mania (6 items), dysphoric mania (5 items), lethargy (7 items), somatic/suicidality (6 items), and sleep (3 items). Symptoms of the "Lethargy" dimension showed the highest out-strength, and its changes preceded those of "somatic/suicidality," while changes in "core (hypo)mania" preceded those of "dysphoric mania." CONCLUSION Dynamic Time Warp may help to capture meaningful BD symptom interactions from panel data with sparse observations. It may increase insight into the temporal dynamics of symptoms, as those with high out-strength (rather than high in-strength) could be promising targets for intervention.
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Affiliation(s)
- R Mesbah
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
- Mental Health Care PsyQ Kralingen, Department of Mood Disorders, Rotterdam, The Netherlands
| | - M A Koenders
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
- Faculty of Social Sciences, Leiden University, Institute of Psychology, Leiden, The Netherlands
| | - A T Spijker
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
- Mental Health Care Rivierduinen, Leiden, The Netherlands
| | - M de Leeuw
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
- Mental Health Care Rivierduinen, Bipolar Disorder Outpatient Clinic, Leiden, The Netherlands
| | - A M van Hemert
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
| | - E J Giltay
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
- Health Campus The Hague, Leiden University, The Hague, The Netherlands
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Bonanno GA, Chen S, Bagrodia R, Galatzer-Levy IR. Resilience and Disaster: Flexible Adaptation in the Face of Uncertain Threat. Annu Rev Psychol 2024; 75:573-599. [PMID: 37566760 DOI: 10.1146/annurev-psych-011123-024224] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2023]
Abstract
Disasters cause sweeping damage, hardship, and loss of life. In this article, we first consider the dominant psychological approach to disasters and its narrow focus on psychopathology (e.g., posttraumatic stress disorder). We then review research on a broader approach that has identified heterogeneous, highly replicable trajectories of outcome, the most common being stable mental health or resilience. We review trajectory research for different types of disasters, including the COVID-19 pandemic. Next, we consider correlates of the resilience trajectory and note their paradoxically limited ability to predict future resilient outcomes. Research using machine learning algorithms improved prediction but has not yet illuminated the mechanism behind resilient adaptation. To that end, we propose a more direct psychological explanation for resilience based on research on the motivational and mechanistic components of regulatory flexibility. Finally, we consider how future research might leverage new computational approaches to better capture regulatory flexibility in real time.
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Affiliation(s)
- George A Bonanno
- Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, USA; , ,
| | - Shuquan Chen
- Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, USA; , ,
| | - Rohini Bagrodia
- Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, USA; , ,
| | - Isaac R Galatzer-Levy
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY, USA;
- Google LLC, Mountain View, California
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Koning ASCAM, Booij SH, Meijer OC, Riese H, Giltay EJ. Temporal associations between salivary cortisol and emotions in clinically depressed individuals and matched controls: A dynamic time warp analysis. Psychoneuroendocrinology 2023; 158:106394. [PMID: 37774658 DOI: 10.1016/j.psyneuen.2023.106394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 10/01/2023]
Abstract
Depression can be understood as a complex dynamic system where depressive symptoms interact with one another. Cortisol is suggested to play a major role in the pathophysiology of depression, but knowledge on the temporal interplay between cortisol and depressive symptoms is scarce. We aimed to analyze the temporal connectivity between salivary cortisol and momentary affective states in depressed individuals and controls. Thirty pair-matched depressed and non-depressed participants completed questionnaires on momentary positive (PA) and negative (NA) affect and collected saliva three times a day for 30 days. The association between cortisol and affect was analyzed by dynamic time warp (DTW) analyses. These analyses involved lag-1 backward to lag-1 forward undirected analyses and lag-0 and lag-1 forward directed analyses. Large inter- and intra-individual variability in the networks were found. At the group level, with undirected analysis PA and NA were connected in the networks in depressed individuals and in controls. Directed analyses indicated that increases in cortisol preceded specific NA items in controls, but tended to follow upon specific affect items increase in depressed individuals. To conclude, at group level, changes in cortisol levels in individuals diagnosed with a depression may be a result of changes in affect, rather than a cause.
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Affiliation(s)
- Anne-Sophie C A M Koning
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sanne H Booij
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Groningen, The Netherlands
| | - Onno C Meijer
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Harriëtte Riese
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Groningen, The Netherlands
| | - Erik J Giltay
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands; Collaborative Antwerp Psychiatric Research Institute (CAPRI), Department of Biomedical Sciences, University of Antwerp, Belgium.
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De Schuyteneer E, Giltay E, Vansteelandt K, Obbels J, Van den Eynde L, Verspecht S, Verledens C, Hebbrecht K, Sienaert P. Electroconvulsive therapy improves somatic symptoms before mood in patients with depression: A directed network analysis. Brain Stimul 2023; 16:1677-1683. [PMID: 37952571 DOI: 10.1016/j.brs.2023.11.004] [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: 08/02/2023] [Revised: 10/24/2023] [Accepted: 11/07/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND The recent network perspective of depression conceptualizes depression as a dynamic network of causally related symptoms, that contrasts with the traditional view of depression as a discrete latent entity that causes all symptoms. Electroconvulsive therapy (ECT) is an effective treatment for severe depression, but little is known about the temporal trajectories of symptom improvement during a course of ECT. OBJECTIVE To gain insight into the dynamics of depressive symptoms in individuals treated with ECT. METHODS The Quick Inventory of Depressive Symptomatology (QIDS) was used to assess symptoms twice a week in 68 participants with a unipolar or bipolar depression treated with ECT, with an average of 12 assessments per participant. Dynamic time warping (DTW) was used to analyze individual time series data, which were subsequently aggregated to calculate a directed symptom network and the in- and out-strength for each symptom. RESULTS Participants had a mean age of 49.6 (SD = 12.8) and 60% were female. Somatic symptoms (e.g., decreased weight) and suicidal ideation showed the highest out-strength values, indicating that their improvement tended to precede improvements in mood symptoms, which showed high in-strength. Sad mood had the highest in-strength, and thus appeared to be the last symptom to improve during ECT treatment (p < 0.001). CONCLUSION This study addresses a gap in the existing literature on ECT, by first analysing the temporal trajectories of symptoms within individual patients and subsequently aggregating them to the group level. The results show that somatic symptoms tend to improve before mood symptoms during ECT.
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Affiliation(s)
- Emma De Schuyteneer
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium; Department of Neurosciences, Mind Body Research, KU Leuven, Leuven, Belgium
| | - Erik Giltay
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands; Department of Public Health and Primary Care, Health Campus the Hague, Leiden University Medical Center, The Hague, the Netherlands.
| | - Kristof Vansteelandt
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Jasmien Obbels
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Liese Van den Eynde
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Shauni Verspecht
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Chelsea Verledens
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Kaat Hebbrecht
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Pascal Sienaert
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
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Wenzel J, Dreschke N, Hanssen E, Rosen M, Ilankovic A, Kambeitz J, Fett AK, Kambeitz-Ilankovic L. Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment. Eur Arch Psychiatry Clin Neurosci 2023:10.1007/s00406-023-01668-w. [PMID: 37715784 DOI: 10.1007/s00406-023-01668-w] [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] [Received: 01/27/2023] [Accepted: 07/31/2023] [Indexed: 09/18/2023]
Abstract
Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can distinguish groups on the continuum of genetic risk toward psychotic illness and identify individuals with need for extended healthcare. Individuals with psychotic disorder (PD, N = 55), healthy individuals (HC, N = 25) and HC with first-degree relatives with psychosis (RE, N = 20) were assessed at two sites over 7 days using EMA. Cluster analysis determined subgroups based on similarities in longitudinal trajectories of psychotic symptom ratings in EMA, agnostic of study group assignment. Psychotic symptom ratings were calculated as average of items related to hallucinations and paranoid ideas. Prior to EMA we assessed symptoms using the Positive and Negative Syndrome Scale (PANSS) and the Community Assessment of Psychic Experience (CAPE) to characterize the EMA subgroups. We identified two clusters with distinct longitudinal EMA characteristics. Cluster 1 (NPD = 12, NRE = 1, NHC = 2) showed higher mean EMA symptom ratings as compared to cluster 2 (NPD = 43, NRE = 19, NHC = 23) (p < 0.001). Cluster 1 showed a higher burden on negative (p < 0.05) and positive (p < 0.05) psychotic symptoms in cross-sectional PANSS and CAPE ratings than cluster 2. Findings indicate a separation of PD with high symptom burden (cluster 1) from PD with healthy-like rating patterns grouping together with HC and RE (cluster 2). Individuals in cluster 1 might particularly profit from exchange with a clinician underlining the idea of EMA as clinical monitoring tool.
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Affiliation(s)
- Julian Wenzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Nils Dreschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Esther Hanssen
- Hersencentrum Mental Health Institute, Amsterdam, The Netherlands
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Andrej Ilankovic
- Department of Psychiatry, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Anne-Kathrin Fett
- Department of Psychology, City, University of London, London, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
- Faculty of Psychology and Educational Sciences, Department of Psychology, Ludwig-Maximilian University, Munich, Germany
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12
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Kraus B, Zinbarg R, Braga RM, Nusslock R, Mittal VA, Gratton C. Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry. Neurosci Biobehav Rev 2023; 152:105259. [PMID: 37268180 PMCID: PMC10527506 DOI: 10.1016/j.neubiorev.2023.105259] [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] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.
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Affiliation(s)
- Brian Kraus
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Richard Zinbarg
- Department of Psychology, Northwestern University, Evanston, IL, USA; The Family Institute at Northwestern University, Evanston, IL, USA
| | - Rodrigo M Braga
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Chicago, IL, USA; Northwestern University, Department of Psychiatry, Chicago, IL, USA; Northwestern University, Medical Social Sciences, Chicago, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychology, Florida State University, Tallahassee, FL, USA; Program in Neuroscience, Florida State University, Tallahassee, FL, USA
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13
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van der Does FH, Nagamine M, van der Wee NJ, Chiba T, Edo N, Kitano M, Vermetten E, Giltay EJ. PTSD Symptom dynamics after the great east japan earthquake: mapping the temporal structure using Dynamic Time Warping. Eur J Psychotraumatol 2023; 14:2241732. [PMID: 37560810 PMCID: PMC10416748 DOI: 10.1080/20008066.2023.2241732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 06/06/2023] [Accepted: 06/13/2023] [Indexed: 08/11/2023] Open
Abstract
Background: After the Great East Japan Earthquake [GEJE], approximately 70,000 Japan Ground Self Defense Force [JGSDF] personnel were deployed, risking Post-Traumatic Stress Disorder [PTSD]. The network approach to psychopathology suggests that symptoms may cause and exacerbate each other, resulting in the emergence and maintenance of disorders, including PTSD. It is therefore important to further explore the temporal interplay between symptoms. Most studies assessing the factor structure of the Impact of Event Scale-Revised [IES-R] have used cross-sectional designs. In this study, the structure of the IES-R was re-evaluated while incorporating the temporal interplay between symptoms.Methods: Using Dynamic Time Warping [DTW] the distances between PTSD symptoms on the IES-R were modelled in 1120 JGSDF personnel. Highly correlated symptoms were clustered at the group level using Distatis three-way principal component analyses of the distance matrices. The resulting clusters were compared to the original three subscales of the IES-R using a Confirmatory Factor Analysis (CFA).Results: The DTW analysis yielded four symptom clusters: Intrusion (five items), Hyperarousal (six items), Avoidance (six items), and Dissociation (five items). CFA yielded better fit estimates for this four-factor solution (RMSEA = 0.084, CFI = 0.918, TLI = 0.906), compared to the original three subscales of the IES-R (RMSEA = 0.103, CFI = 0.873, TLI = 0.858).Conclusions: DTW offers a new method of modelling the temporal relationships between symptoms. It yielded four IES-R symptom clusters, which may facilitate understanding of PTSD as a complex dynamic system.
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Affiliation(s)
| | - Masanori Nagamine
- Division of Behavioral Science, National Defense Medical College Research Institute, Saitama, Japan
| | - Nic J.A. van der Wee
- Department of Psychiatry, Leiden University Medical Center (LUMC),Leiden, the Netherlands
| | - Toshinori Chiba
- Department of Psychiatry, Japan Self-Defense Force Hanshin Hospital, Kawanishi, Japan
| | - Naoki Edo
- Division of Behavioral Science, National Defense Medical College Research Institute, Saitama, Japan
| | - Masato Kitano
- Division of Behavioral Science, National Defense Medical College Research Institute, Saitama, Japan
| | - Eric Vermetten
- Department of Psychiatry, Leiden University Medical Center (LUMC),Leiden, the Netherlands
| | - Erik J. Giltay
- Department of Psychiatry, Leiden University Medical Center (LUMC),Leiden, the Netherlands
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Health Campus The Hague, Leiden University, The Hague, the Netherlands
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14
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Eating disorder psychopathology dimensions based on individual co-occurrence patterns of symptoms over time: a dynamic time warp analysis in a large naturalistic patient cohort. Eat Weight Disord 2022; 27:3649-3663. [PMID: 36469226 DOI: 10.1007/s40519-022-01504-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Most of the network approaches in eating disorders found the highest degree of centrality for symptoms related to weight and shape concerns. However, longitudinal analyses are scarce and may increase our insight of the complex characteristics and dynamics over time. In the current study, an alternative non-linear method to perform longitudinal network analyses, the dynamic time warp approach, was used to examine whether robust dimensions of eating disorder psychopathology symptoms could be found based on the individual dynamic interplay of eating disorder symptoms co-occurrence patterns in time. METHODS The study sample included a naturalistic cohort of patients (N = 255) with all eating disorder subtypes who were assessed with the eating disorder examination questionnaire (EDE-Q) at a minimum of four times during treatment. Dynamic time warp analyses yielded distance matrices within each individual patient, which were subsequently aggregated into symptom networks and dimensions at the group level. RESULTS Aggregation of the individual distance matrices at the group level yielded four robust symptom dimensions: 1. restraint/rules, 2. secret eating/fasting, 3. worries/preoccupation, and 4. weight and shape concern. The items 'fear of weight gain' and 'guilt' were bridge symptoms between the dimensions 1, 3 and 4. CONCLUSION Dynamic time warp could capture the within-person dynamics of eating disorder symptoms. Sumscores of the four dimensions could be used to follow patients over time. This approach could be applied in the future to visualize eating disorder symptom dynamics and signal the central symptoms within an individual and groups of patients. LEVEL OF EVIDENCE Level III: evidence obtained from well-designed cohort or case-control analytic studies. .
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15
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Zhou J, Fan A, Zhou X, Pao C, Xiao L, Feng Y, Xi R, Chen Y, Huang Q, Dong B, Zhou J. Interrelationships between childhood maltreatment, depressive symptoms, functional impairment, and quality of life in patients with major depressive disorder: A network analysis approach. CHILD ABUSE & NEGLECT 2022; 132:105787. [PMID: 35917751 DOI: 10.1016/j.chiabu.2022.105787] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/22/2022] [Accepted: 07/11/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Childhood maltreatment continues to pose a great challenge to psychiatry. Although there is growing evidence demonstrating that childhood maltreatment is an important risk factor for depressive disorders, it remains to be elucidated which specific symptoms occur after exposure to different kinds of childhood maltreatment, and whether certain pathways may account for these associations. PARTICIPANTS AND SETTINGS A total of 203 adult patients (18-53 years old) with MDD, diagnosed by Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria, were recruited from the outpatient clinic of Beijing Anding Hospital, Capital Medical University. METHODS Childhood maltreatment, depressive symptoms, functional impairment, and quality of life were evaluated by the Childhood Trauma Questionnaire - Short Form (CTQ-SF), 17-item Hamilton Depression Rating Scale (HAMD-17), Sheehan Disability Scale (SDS), and Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF). Undirected network analysis was used to explore the most relevant connections between them. Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association. RESULTS In network analysis, the strongest edges were a positive correlation between emotional abuse and suicidal behavior as well as a negative association between emotional neglect and age of onset. In DAG analysis, emotional abuse emerged as the most pivotal network node, triggering both suicidal behaviors and depression symptoms. CONCLUSIONS Emotional abuse appears to be an extremely harmful form of childhood maltreatment in the clinical presentation of depression. This study has promise in informing the clinical intervention of depression.
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Affiliation(s)
- Jia Zhou
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, China
| | - Anyuyang Fan
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xinyi Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Christine Pao
- Mental Health and Behavioral Science Service, Bruce W. Carter VA Medical Center, Miami, FL, United States
| | - Le Xiao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuan Feng
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Rui Xi
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Institute of Mental Health, Beijing, China
| | - Yun Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Institute of Mental Health, Beijing, China
| | - Qingzhi Huang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Institute of Mental Health, Beijing, China
| | - Bin Dong
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, China.
| | - Jingjing Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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16
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Qian Y, Solano MJ, Kreindler D. Grouping of mood symptoms by time series dynamics. J Affect Disord 2022; 309:186-192. [PMID: 35461820 DOI: 10.1016/j.jad.2022.04.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/12/2022] [Accepted: 04/16/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Understanding how symptoms of mood disorders vary over time in relation to each other is potentially valuable for diagnosis and predicting episodes of illness. In this paper, we characterize the degree of similarity of time series of different mood disorder symptoms. METHODS We collected 32,215 mood disorder symptom questionnaires, administered twice-daily over 18 months to (n = 19) subjects with rapidly cycling bipolar disorder and (n = 20) healthy control subjects, using visual analog scales to rate 11 sets of symptom severity ratings plus a control item. We used Dynamic Time Warping to calculate similarity ratings between all within-subject pairs of severity ratings followed by Exploratory Factor Analysis (EFA) to identify latent factors of symptom time series across all subjects. RESULTS Two latent factors were identified: one with depression and anxiety; and a second, with concentration, energy, irritability, fatigue, appetite, euphoria/elation and overall mood. Restlessness, racing thoughts, and the control item (daily hours of daylight) did not cluster with any of the others. LIMITATIONS Limited sample size dictated that we pool bipolar and healthy patients and use an iterative EFA procedure. CONCLUSION This analysis suggests that, in a pooled sample of individuals with bipolar disorder and in healthy controls, severity ratings of overall depression and overall anxiety vary jointly as one dynamic factor, while some but not all other DSM mood symptoms vary jointly along with overall mood rating as a second dynamic factor. Further investigation may determine if these findings can simplify subjective symptom reporting in mood-monitoring studies.
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Affiliation(s)
- Yuxin Qian
- Applied Mathematics Program, University of California Los Angeles, Los Angeles, California, USA
| | - Maria José Solano
- Mathematics and Computer Science Program, McGill University, Montreal, Quebec, Canada
| | - David Kreindler
- Division of Child and Youth Mental Health, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada, M5T 1R8; Centre for Mobile Computing in Mental Health, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada, M4N 3M5; Division of Youth Psychiatry, Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada, M4N 3M5.
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17
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Abstract
Psychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis which does not reflect the heterogeneous symptom experiences of many patients. The objective of this study was to better understand therapeutic change, by illustrating how symptoms evolve and interact during psychotherapy. Data from 113,608 patients from psychological therapy services who completed depression and anxiety symptom measures across three to six therapy sessions were analysed. A panel graphical vector-autoregression model was estimated in a model development sample (N = 68,165) and generalizability was tested in a confirmatory model, fitted to a separate (hold-out) sample of patients (N = 45,443). The model displayed an excellent fit and replicated in the confirmatory holdout sample. First, we found that nearly all symptoms were statistically related to each other (i.e. dense connectivity), indicating that no one symptom or association drives change. Second, the structure of symptom interrelations which emerged did not change across sessions. These findings provide a dynamic view of the process of symptom change during psychotherapy and give rise to several causal hypotheses relating to structure, mechanism, and process.
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18
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Schreuder MJ, Wigman JTW, Groen RN, Weinans E, Wichers M, Hartman CA. Anticipating the direction of symptom progression using critical slowing down: a proof-of-concept study. BMC Psychiatry 2022; 22:49. [PMID: 35062917 PMCID: PMC8781362 DOI: 10.1186/s12888-022-03686-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND As complex dynamic systems approach a transition, their dynamics change. This process, called critical slowing down (CSD), may precede transitions in psychopathology as well. This study investigated whether CSD may also indicate the direction of future symptom transitions, i.e., whether they involve an increase or decrease in symptoms. METHODS In study 1, a patient with a history of major depression monitored their mental states ten times a day for almost eight months. Study 2 used data from the TRAILS TRANS-ID study, where 122 young adults at increased risk of psychopathology (mean age 23.64±0.67 years, 56.6% males) monitored their mental states daily for six consecutive months. Symptom transitions were inferred from semi-structured diagnostic interviews. In both studies, CSD direction was estimated using moving-window principal component analyses. RESULTS In study 1, CSD was directed towards an increase in negative mental states. In study 2, the CSD direction matched the direction of symptom shifts in 34 individuals. The accuracy of the indicator was higher in subsets of individuals with larger absolute symptom transitions. The indicator's accuracy exceeded chance levels in sensitivity analyses (accuracy 22.92% vs. 11.76%, z=-2.04, P=.02) but not in main analyses (accuracy 27.87% vs. 20.63%, z=-1.32, P=.09). CONCLUSIONS The CSD direction may predict whether upcoming symptom transitions involve remission or worsening. However, this may only hold for specific individuals, namely those with large symptom transitions. Future research is needed to replicate these findings and to delineate for whom CSD reliably forecasts the direction of impending symptom transitions.
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Affiliation(s)
- Marieke J Schreuder
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University of Groningen, University Medical Center Groningen, Internal Postal Code: CC72, Triade Building Entrance 24, Hanzeplein 1, Groningen, 9713, GZ, The Netherlands.
| | - Johanna T W Wigman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University of Groningen, University Medical Center Groningen, Internal Postal Code: CC72, Triade Building Entrance 24, Hanzeplein 1, Groningen, 9713, GZ, The Netherlands
| | - Robin N Groen
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University of Groningen, University Medical Center Groningen, Internal Postal Code: CC72, Triade Building Entrance 24, Hanzeplein 1, Groningen, 9713, GZ, The Netherlands
| | - Els Weinans
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marieke Wichers
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University of Groningen, University Medical Center Groningen, Internal Postal Code: CC72, Triade Building Entrance 24, Hanzeplein 1, Groningen, 9713, GZ, The Netherlands
| | - Catharina A Hartman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University of Groningen, University Medical Center Groningen, Internal Postal Code: CC72, Triade Building Entrance 24, Hanzeplein 1, Groningen, 9713, GZ, The Netherlands
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19
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Koenders M, Mesbah R, Spijker A, Boere E, de Leeuw M, van Hemert B, Giltay E. Effects of the COVID-19 pandemic in a preexisting longitudinal study of patients with recently diagnosed bipolar disorder: Indications for increases in manic symptoms. Brain Behav 2021; 11:e2326. [PMID: 34554650 PMCID: PMC8613426 DOI: 10.1002/brb3.2326] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/23/2021] [Accepted: 07/29/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic interfered in the daily lives of people and is assumed to adversely affect mental health. However, the effects on mood (in)stability of bipolar disorder (BD) patients and the comparison to pre-COVID-19 symptom severity levels are unknown. METHOD Between April and September, 2020, symptoms and well-being were assessed in the Bipolar Netherlands Cohort (BINCO) study of recently diagnosed patients with BD I and II. The questionnaire contained questions regarding manic and depressive symptoms (YMRS and ASRM, QIDS), worry (PSWQ), stress (PSS), loneliness, sleep, fear for COVID-19, positive coping, and substance use. As manic, depressive and stress symptoms levels were assessed pre-COVID-19, their trajectories during the lockdown restrictions were estimated using mixed models. RESULTS Of the 70 invited BD patients, 36 (51%) responded at least once (mean age of 36.7 years, 54% female, and 31% BD type 1) to the COVID-19 assessments. There was a significant increase (X2 = 17.06; p = .004) in (hypo)manic symptoms from baseline during the first COVID-19 wave, with a decrease thereafter. Fear of COVID-19 (X2 = 18.01; p = .003) and positive coping (X2 = 12.44; p = .03) were the highest at the start of the pandemic and decreased thereafter. Other scales including depression and stress symptoms did not vary significantly over time. CONCLUSION We found a meaningful increase in manic symptomatology from pre-COVID-19 into the initial phases of the pandemic in BD patients. These symptoms decreased along with fear of COVID-19 and positive coping during the following months when lockdown measures were eased.
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Affiliation(s)
- Manja Koenders
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands.,Faculty of Social Sciences, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Rahele Mesbah
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Mood Disorders, Mental Health Care PsyQ Kralingen, Rotterdam, The Netherlands
| | - Annet Spijker
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands.,Parnassia Groep, Den Haag, The Netherlands
| | - Elvira Boere
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Mood Disorders, Mental Health Care PsyQ Kralingen, Rotterdam, The Netherlands
| | - Max de Leeuw
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands.,Mental Health Care Rivierduinen, Bipolar Disorder Outpatient Clinic, Leiden, The Netherlands
| | - Bert van Hemert
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
| | - Erik Giltay
- Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
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20
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Booij MM, van Noorden MS, van Vliet IM, Ottenheim NR, van der Wee NJA, Van Hemert AM, Giltay EJ. Dynamic time warp analysis of individual symptom trajectories in depressed patients treated with electroconvulsive therapy. J Affect Disord 2021; 293:435-443. [PMID: 34252687 DOI: 10.1016/j.jad.2021.06.068] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 06/20/2021] [Accepted: 06/27/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Although electroconvulsive therapy (ECT) effectively improves severity scores of depression, its effects on its individual symptoms has scarcely been studied. We aimed to study which depressive symptom trajectories dynamically cluster together in individuals as well as groups of patients during ECT using Dynamic Time Warp (DTW) analysis. METHODS We analysed the standardized weekly scores on the 25-item abbreviated version of the Comprehensive Psychopathological Rating Scale (CPRS) in depressed patients before and during their first six weeks of ECT treatment. DTW analysis was used to analyse the (dis)similarity of time series of items scores at the patient level (300 'DTW distances' per patient) as well as on the group level. Hierarchical cluster, network, and Distatis analyses yielded symptom dimensions. RESULTS We included 133 patients, 64.7% female, with an average age of 60.4 years (SD 15.1). Individual DTW distance matrices and networks revealed marked differences in hierarchical and network clusters among patients. Based on cluster analyses of the aggregated matrices, four symptom clusters emerged. In patients who reached remission, the average DTW distance between their symptoms was significantly smaller than non-remitters, reflecting denser symptom networks in remitters than non-remitters (p=0.04). LIMITATIONS The assessments were done only weekly during the first six weeks of ECT treatment. The use of individual items of the abbreviated CPRS may have led to measurement error as well as floor and ceiling effects. CONCLUSION DTW offers an efficient new approach to analyse symptom trajectories within individuals as well as groups of patients, aiding personalized medicine of psychopathology.
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Affiliation(s)
- Marijke M Booij
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands
| | | | - Irene M van Vliet
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands
| | | | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands
| | - Albert M Van Hemert
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands
| | - Erik J Giltay
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands.
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21
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Doornenbal BM, Bakx R. Self-rated health trajectories: A dynamic time warp analysis. Prev Med Rep 2021; 24:101510. [PMID: 34430192 PMCID: PMC8371205 DOI: 10.1016/j.pmedr.2021.101510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 11/18/2022] Open
Abstract
Self-rated health (SRH), individuals’ overall perception of their health, is a key predictor of health events. To target disease prevention efforts, it is important to understand how SRH develops over time. The goal of this short communication is to find prototypic SRH trajectories by applying dynamic time warping, a time series comparison technique initially developed for speech recognition. Revealing prototypic SRH trajectories can help direct disease prevention efforts towards trajectories that are more likely to result in adverse health events. Based on data from a Dutch representative sample of 2,154 individuals, our dynamic time warp analysis suggests that Dutch individuals do not typically show a steady growth or decline in SRH. Instead, we identified four relatively stable SRH trajectories that differed in average SRH. One of these trajectories is a path of consistent low SRH.
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Affiliation(s)
- Brian M. Doornenbal
- Leiden University Medical Center, the Netherlands
- Salut., the Netherlands
- Corresponding author at: Jansbuitensingel 7, 6811 AA Arnhem, the Netherlands.
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