Network Analysis of the Symptoms of Depressive Disorders Over the Course of Therapy: Changes in Centrality Measures
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objective Network analysis can be used in terms of a novel psychopathological approach for depressive syndrome. We aimed to estimate the successive network structures of depressive symptoms in patients with depressive disorder using data from the Clinical Research Center for Depression study.Methods We enrolled 1,152 South Korean adult patients with depressive disorders who were beginning treatment for first-onset or recurrent depressive episodes. We examined the network structure of the severities of the items on the Hamilton Depression Rating Scale (HAMD) at baseline and at weeks 2, 12, 25, and 52. The node strength centrality of all the HAMD items at baseline and at week 2, 12, 25, and 52 in terms of network analysis.Results In the severity networks, the anxiety (psychic) item was the most centrally situated in the initial period (baseline and week 2), while loss of weight was the most centrally situated item in the later period (weeks 25 and 52). In addition, the number of strong edges (i.e., edges representing strong correlations) increased in the late period compared to the initial period.Conclusion Our findings support a period-specific and symptom-focused therapeutic approach that can provide complementary information to the unidimensional total HAMD score.
Abstract
Objective Network analysis can be used in terms of a novel psychopathological approach for depressive syndrome. We aimed to estimate the successive network structures of depressive symptoms in patients with depressive disorder using data from the Clinical Research Center for Depression study.Methods We enrolled 1,152 South Korean adult patients with depressive disorders who were beginning treatment for first-onset or recurrent depressive episodes. We examined the network structure of the severities of the items on the Hamilton Depression Rating Scale (HAMD) at baseline and at weeks 2, 12, 25, and 52. The node strength centrality of all the HAMD items at baseline and at week 2, 12, 25, and 52 in terms of network analysis.Results In the severity networks, the anxiety (psychic) item was the most centrally situated in the initial period (baseline and week 2), while loss of weight was the most centrally situated item in the later period (weeks 25 and 52). In addition, the number of strong edges (i.e., edges representing strong correlations) increased in the late period compared to the initial period.Conclusion Our findings support a period-specific and symptom-focused therapeutic approach that can provide complementary information to the unidimensional total HAMD score.
참고문헌 (38)
- Østergaard SD, "The heterogeneity of the depressive syndrome : when numbers get serious", Acta Psychiatr Scand, 124, pp.495~496 (2011)
- Uher R, "Depression symptom dimensions as predictors of antidepressant treatment outcome : replicable evidence for interest-activity symptoms", Psychol Med, 42, pp.967~980 (2012)
- Seon-Cheol Park, "Contemporary Issues in Depressive Disorders", PSYCHIATRY INVESTIGATION, 16(9), pp.633~635 (2019), 대한신경정신의학회[DOI]
- Seon-Cheol Park, "Diagnostic Issues of Depressive Disorders from Kraepelinian Dualism to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition", PSYCHIATRY INVESTIGATION, 16(9), pp.636~644 (2019), 대한신경정신의학회[DOI]
- American Psychiatric Association, "Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5)" (2013), American Psychiatric Press
- Zimmerman M, "How many different ways do patients meet the diagnostic criteria for major depressive disorder?", Compr Psychiatry, 56, pp.29~34 (2015)
- Park SC, "How many different symptom combinations fulfill the diagnostic criteria for major depressive disorder? : results from the CRESCEND study", Nord J Psychiatry, 71, pp.217~222 (2016)
- Nunes A, "We need an operational framework for heterogeneity in psychiatry research", J Psychiatry Neurosci, 45, pp.3~6 (2020)
- Insel TR, "Next-generation treatments for mental disorders", Sci Transl Med, 4, pp.155~ (2012)
- McNally RJ, "Mental disorders as causal systems : a network approach to posttraumatic stress disorder", Clin Psychol Sci, 3, pp.836~849 (2015)
- Young G, "Causality in psychiatry : a hybrid symptom network construct model", Front Psychiatry, 6, pp.164~ (2015)
- Borsboom D, "Network analysis : an integrative approach to the structure of psychopathology", Annu Rev Clin Psychol, 9, pp.91~121 (2013)
- Fried EI, "Measuring depression over time. . . or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression", Psychol Assess, 28, pp.1354~1367 (2016)
- Guyon H, "Modeling psychological attributes in psychology-an epistemological discussion : network analysis vs. latent variable", Front Psychol, 8, pp.798~ (2017)
- Kukla A, "Methods of Theoretical Psychology" (2001), A Bradford Book
- Kendler KS, "What kinds of things are psychiatric disorders?", Psychol Med, 41, pp.1143~1150 (2011)
- Cramer AO, "Comorbidity : a network perspective", Behav Brain Sci, 33, pp.137~150 (2010)
- Wichers M, "Micro-level affect dynamics in psychopathology viewed from complex dynamical system theory", Emot Rev, 7, pp.362~367 (2015)
- Borsboom D, "A network theory of mental disorders", World Psychiatry, 16, pp.5~13 (2017)
- Forbes MK, "The network approach to psychopathology : promise versus reality", World Psychiatry, 18, pp.272~273 (2019)
- Park SC, "Reduced awareness of surroundings is the most central domain in the network structure of posttraumatic stress disorder symptoms", Nord J Psychiatry, 74, pp.344~353 (2020)
- Fried EI, "What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis", J Affect Disord, 189, pp.314~329 (2016)
- Kendler KS, "The centrality of DSM and non-DSM depressive symptoms in Han Chinese women with major depression", J Affect Disord, 227, pp.739~744 (2018)
- Park SC, "The centrality of depression and anxiety symptoms in major depressive disorder determined using a network analysis", J Affect Disord, 271, pp.19~26 (2020)
- Park SC, "Network analysis of the depressive symptom profiles in Asian patients with depressive disorders : Findings from the Research on Asian Psychotropic Prescription Patterns for Antidepressants(REAP-AD)", Psychiatry Clin Neurosci, 74, pp.344~353 (2020)
- Hamiton MA, "A rating scale for depression", J Neurol Neurosurg Psychiatry, 23, pp.56~62 (1960)
- 이중서, "한국판 Hamilton 우울증 평가 척도의 신뢰도, 타당도 연구", 신경정신의학, 44(4), pp.456~465 (2005), 대한신경정신의학회
- American Psychiatric Association, "Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV)" (1994), American Psychiatric Press
- First MB, "User’s Guide for the Structured Clinical Interview for DSM-IV Axis I Disorders Research Version (SCID-I)" (1996), New York State Psychiatric Institute, Biometrics Research
- Epskamp S, "Estimating psychological networks and their accuracy : a tutorial paper", Behav Res Methods, 50, pp.195~212 (2018)
- Tibshirani R, "Regression shrinkage and selection via the lasso", J R Stat Soc Ser B, 58, pp.267~288 (1996)
- Chen J, "Extended Bayesian information criteria for model selection with large model species", Biometrika, 95, pp.759~771 (2008)
- van Borkulo C, "Association of symptom network structure with the course of depression", JAMA Psychiatry, 72, pp.1219~1226 (2015)
- Cohen J, "Statistical Power Analysis for the Behavioral Sciences (Revised Edition)" (1977), Academic Press
- Madhoo M, "Network analysis of the Quick Inventory of Depressive Symptomatology : reanalysis of the STAR*D clinical trial", Eur Neuropsychopharmacol, 26, pp.1768~1774 (2016)
- Reilly TJ, "Psychometric properties of the16-item quick inventory of depressive symptomatology : a systematic review and meta-analysis", J Psychiatr Res, 60, pp.132~140 (2015)
- Hilland E, "Exploring the links between specific depression symptoms and brain structures", Psychiatry Clin Neurosci, 74, pp.220~221 (2020)
- Roca P, "Does mindfulness change the mind? A novel psychonectome perspective based on network analysis", PLoS One, 14, pp.e0219793~ (2019)