ArticleTranslational psychiatry2016
Data-driven classification of bipolar I disorder from longitudinal course of mood.
Article in Translational psychiatry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- The dynamic influence of exercise on emotional state among people with a history of mood disorder.Psychiatry research · 2026Article
- The Dynamics of Mood in Bipolar Disorder: How Mathematical Models Help Phenotype Individuals, Forecast Mood, and Clarify Underlying Mechanisms.Current psychiatry reports · 2025Review
- Day-to-day variability in sleep and activity predict the onset of a hypomanic episode in patients with bipolar disorder.Journal of affective disorders · 2025Observational
- The real-world association between digital markers of circadian disruption and mental health risks.NPJ digital medicine · 2024Article
- Capturing crisis dynamics: a novel personalized approach using multilevel hidden Markov modeling.Frontiers in psychiatry · 2024Article
- Specificity of affective dynamics of bipolar and major depressive disorder.Brain and behavior · 2023Article
- Mobile Acceptance and Commitment Therapy in Bipolar Disorder: Microrandomized Trial.JMIR mental health · 2023Article
- Limited time-specific and longitudinal effects of depressive and manic symptoms on cognition in bipolar spectrum disorders.Acta psychiatrica Scandinavica · 2022Article
- A critical evaluation of dynamical systems models of bipolar disorder.Translational psychiatry · 2022Review
- Strategies and foundations for scientific discovery in longitudinal studies of bipolar disorder.Bipolar disorders · 2022Review
- A digital self-report survey of mood for bipolar disorder.Bipolar disorders · 2021Article
- Dynamics of data-driven microstates in bipolar disorder.Journal of psychiatric research · 2021Article
- A Lifespan Model of Interference Resolution and Inhibitory Control: Risk for Depression and Changes with Illness Progression.Neuropsychology review · 2020Review
- Digital solutions for shaping mood and behavior among individuals with mood disorders.Current opinion in systems biology · 2020Article
- A clinical staging model for bipolar disorder: longitudinal approach.Translational psychiatry · 2020Article
- Touchscreen typing pattern analysis for remote detection of the depressive tendency.Scientific reports · 2019Article
- Identification and initial validation of empirically derived bipolar symptom states from a large longitudinal dataset: an application of hidden Markov modeling to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study.Psychological medicine · 2019Article
- Pathways to Neuroprediction: Opportunities and challenges to prediction of treatment response in depression.Current behavioral neuroscience reports · 2018Article
- Testing frameworks for personalizing bipolar disorder.Translational psychiatry · 2018Article
- Cohort Profile: The Heinz C. Prechter Longitudinal Study of Bipolar Disorder.International journal of epidemiology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Diagnostic and Statistical Manual of Mental Disorder (DSM) classification of bipolar disorder defines categories to reflect common understanding of mood symptoms rather than scientific evidence. This work aimed to determine whether bipolar I can be objectively classified from longitudinal mood data and whether resulting classes have clinical associations. Bayesian nonparametric hierarchical models with latent classes and patient-specific models of mood are fit to data from Longitudinal Interval Follow-up Evaluations (LIFE) of bipolar I patients (N=209). Classes are tested for clinical associations. No classes are justified using the time course of DSM-IV mood states. Three classes are justified using the course of subsyndromal mood symptoms. Classes differed in attempted suicides (P=0.017), disability status (P=0.012) and chronicity of affective symptoms (P=0.009). Thus, bipolar I disorder can be objectively classified from mood course, and individuals in the resulting classes share clinical features. Data-driven classification from mood course could be used to enrich sample populations for pharmacological and etiological studies.
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