ArticleJournal of psychopathology and clinical science2024
Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.
Article in Journal of psychopathology and clinical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Relations among daily symptoms of depression.British journal of psychology (London, England : 1953) · 2026Article
- Neuropathophysiological changes associated with mirtazapine treatment response in major depressive disorder: insights into bottom-up dopaminergic pathways and prefrontal control networks.European archives of psychiatry and clinical neuroscience · 2026Article
- The dynamics of loneliness: Longitudinal associations of anhedonia and dampening in a clinical sample of depressed individuals.Journal of affective disorders · 2026Article
- Individual-Level Modeling of Depressive Symptom Severity Using Smartphone and Wearable Data: Time-Aware 1-Year Study With Feature-Group Contributions.JMIR formative research · 2026Article
- From Traditional Inspection to Quantitative Imaging: Tongue and Facial Color Features for Automated Machine Learning-Driven Depression and Schizophrenia Classification.Behavioral sciences (Basel, Switzerland) · 2026Article
- Predicting weekly instability in depressive symptoms among individuals diagnosed with Major Depressive Disorder using deep learning and passively-collected movement data.Journal of affective disorders reports · 2026Article
- Distinguishing Chronic and Non-Chronic Depression: A Clinical Profile and Symptom Networks Approach.Psychotherapy and psychosomatics · 2026Article
- "I wanna just not go, because maybe I'm sad" - views on depression from the perspective of people with aphasia, their care partners, and speech-language pathologists.Disability and rehabilitation · 2026Article
- Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning.JMIR mHealth and uHealth · 2026Article
- An investigation into in-sample and out-of-sample model selection for nonstationary autoregressive models.The British journal of mathematical and statistical psychology · 2026Article
- Interindividual differences in digital phenotypes of major depressive disorder: A passive sensing study using smartphone and wearable sensor data.Behaviour research and therapy · 2026Article
- Trends in Adolescent Depression and Suicide Risk Screening and Symptom Monitoring in a Large Primary Care Network.Academic pediatrics · 2026Article
- Clinical Heterogeneity of Major Depressive Disorder: The Role of Trauma, Dissociation, and Sleep.Journal of clinical medicine · 2026Article
- Interpretable Feature Selection and Hybrid Deep Learning Models for Depressive Symptoms Prediction from Wearable Device Data.Journal of medical systems · 2026Article
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Article
- From anxiety to depression: A longitudinal investigation into the role of anhedonia.Journal of affective disorders · 2025Article
- Acute suicidal ideation in context: highlighting sentiment-based markers through the diary entries of a clinically depressed sample.BMC psychiatry · 2025Article
- Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder.Journal of psychopathology and clinical science · 2025Article
- Observational
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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