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Brusco M, Steinley D, Watts AL. Improving the Walktrap Algorithm Using K-Means Clustering. MULTIVARIATE BEHAVIORAL RESEARCH 2024; 59:266-288. [PMID: 38361218 PMCID: PMC11014777 DOI: 10.1080/00273171.2023.2254767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K-means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.
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2
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Brusco MJ, Steinley D, Watts AL. A comparison of logistic regression methods for Ising model estimation. Behav Res Methods 2023; 55:3566-3584. [PMID: 36266525 DOI: 10.3758/s13428-022-01976-4] [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] [Accepted: 09/05/2022] [Indexed: 11/08/2022]
Abstract
The Ising model has received significant attention in network psychometrics during the past decade. A popular estimation procedure is IsingFit, which uses nodewise l1-regularized logistic regression along with the extended Bayesian information criterion to establish the edge weights for the network. In this paper, we report the results of a simulation study comparing IsingFit to two alternative approaches: (1) a nonregularized nodewise stepwise logistic regression method, and (2) a recently proposed global l1-regularized logistic regression method that estimates all edge weights in a single stage, thus circumventing the need for nodewise estimation. MATLAB scripts for the methods are provided as supplemental material. The global l1-regularized logistic regression method generally provided greater accuracy and sensitivity than IsingFit, at the expense of lower specificity and much greater computation time. The stepwise approach showed considerable promise. Relative to the l1-regularized approaches, the stepwise method provided better average specificity for all experimental conditions, as well as comparable accuracy and sensitivity at the largest sample size.
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Affiliation(s)
- Michael J Brusco
- Department of Business Analytics, Information Systems, and Supply Chain, Florida State University, Tallahassee, FL, USA.
| | - Douglas Steinley
- Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
| | - Ashley L Watts
- Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
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3
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Brusco MJ, Steinley D, Watts AL. On maximization of the modularity index in network psychometrics. Behav Res Methods 2023; 55:3549-3565. [PMID: 36258108 DOI: 10.3758/s13428-022-01975-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2022] [Indexed: 11/08/2022]
Abstract
The modularity index (Q) is an important criterion for many community detection heuristics used in network psychometrics and its subareas (e.g., exploratory graph analysis). Some heuristics seek to directly maximize Q, whereas others, such as the walktrap algorithm, only use the modularity index post hoc to determine the number of communities. Researchers in network psychometrics have typically not employed methods that are guaranteed to find a partition that maximizes Q, perhaps because of the complexity of the underlying mathematical programming problem. In this paper, for networks of the size commonly encountered in network psychometrics, we explore the utility of finding the partition that maximizes Q via formulation and solution of a clique partitioning problem (CPP). A key benefit of the CPP is that the number of communities is naturally determined by its solution and, therefore, need not be prespecified in advance. The results of two simulation studies comparing maximization of Q to two other methods that seek to maximize modularity (fast greedy and Louvain), as well as one popular method that does not (walktrap algorithm), provide interesting insights as to the relative performances of the methods with respect to identification of the correct number of communities and the recovery of underlying community structure.
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Affiliation(s)
- Michael J Brusco
- Department of Business Analytics, Information Systems, and Supply Chain, Florida State University, Tallahassee, FL, USA.
| | - Douglas Steinley
- Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
| | - Ashley L Watts
- Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
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4
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Pan W, Deng F, Wang X, Hang B, Zhou W, Zhu T. Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls. Front Psychiatry 2023; 14:1079448. [PMID: 37575564 PMCID: PMC10415910 DOI: 10.3389/fpsyt.2023.1079448] [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: 10/25/2022] [Accepted: 06/30/2023] [Indexed: 08/15/2023] Open
Abstract
Background Vocal features have been exploited to distinguish depression from healthy controls. While there have been some claims for success, the degree to which changes in vocal features are specific to depression has not been systematically studied. Hence, we examined the performances of vocal features in differentiating depression from bipolar disorder (BD), schizophrenia and healthy controls, as well as pairwise classifications for the three disorders. Methods We sampled 32 bipolar disorder patients, 106 depression patients, 114 healthy controls, and 20 schizophrenia patients. We extracted i-vectors from Mel-frequency cepstrum coefficients (MFCCs), and built logistic regression models with ridge regularization and 5-fold cross-validation on the training set, then applied models to the test set. There were seven classification tasks: any disorder versus healthy controls; depression versus healthy controls; BD versus healthy controls; schizophrenia versus healthy controls; depression versus BD; depression versus schizophrenia; BD versus schizophrenia. Results The area under curve (AUC) score for classifying depression and bipolar disorder was 0.5 (F-score = 0.44). For other comparisons, the AUC scores ranged from 0.75 to 0.92, and the F-scores ranged from 0.73 to 0.91. The model performance (AUC) of classifying depression and bipolar disorder was significantly worse than that of classifying bipolar disorder and schizophrenia (corrected p < 0.05). While there were no significant differences in the remaining pairwise comparisons of the 7 classification tasks. Conclusion Vocal features showed discriminatory potential in classifying depression and the healthy controls, as well as between depression and other mental disorders. Future research should systematically examine the mechanisms of voice features in distinguishing depression with other mental disorders and develop more sophisticated machine learning models so that voice can assist clinical diagnosis better.
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Affiliation(s)
- Wei Pan
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China
- School of Psychology, Central China Normal University, Wuhan, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan, China
| | - Fusong Deng
- Wuhan Wuchang Hospital, Wuchang Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Xianbin Wang
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China
- School of Psychology, Central China Normal University, Wuhan, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan, China
| | - Bowen Hang
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China
- School of Psychology, Central China Normal University, Wuhan, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan, China
| | - Wenwei Zhou
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China
- School of Psychology, Central China Normal University, Wuhan, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan, China
| | - Tingshao Zhu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
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5
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Zhao Y, Liang K, Qu D, He Y, Wei X, Chi X. The Longitudinal Features of Depressive Symptoms During the COVID-19 Pandemic Among Chinese College Students: A Network Perspective. J Youth Adolesc 2023:10.1007/s10964-023-01802-w. [PMID: 37306836 DOI: 10.1007/s10964-023-01802-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 05/29/2023] [Indexed: 06/13/2023]
Abstract
There is substantial evidence that the Corona Virus Disease 2019 (COVID-19) pandemic increased the risk of depressive symptoms among college students, but the long-term features of depressive symptoms on a symptom level have been poorly described. The current study investigated interaction patterns between depressive symptoms via network analysis. In this longitudinal study, participants included 860 Chinese college students (65.8% female; Mage = 20.6, SDage = 1.8, range: 17-27) who completed a questionnaire at three-time points three months apart. Results demonstrated that fatigue was the most influential symptom, and the occurrence of fatigue could give rise to other depressive symptoms. In addition to predicting other symptoms, fatigue could be predicted by other symptoms in the measurement. The network structures were similar across time, suggesting that the overall interaction pattern of depressive symptoms was stable over the longitudinal course. These findings suggest that depressive symptoms during the COVID-19 period are associated with the presence of fatigue.
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Affiliation(s)
- Yue Zhao
- School of Psychology, Shenzhen University, Shenzhen, Guangdong, China
- Center for Mental Health, Shenzhen University, Shenzhen, Guangdong, China
| | - Kaixin Liang
- School of Psychology, Shenzhen University, Shenzhen, Guangdong, China
- Center for Mental Health, Shenzhen University, Shenzhen, Guangdong, China
| | - Diyang Qu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Yunhan He
- School of Psychology, Shenzhen University, Shenzhen, Guangdong, China
- Center for Mental Health, Shenzhen University, Shenzhen, Guangdong, China
| | - Xiaoqi Wei
- School of Psychology, Shenzhen University, Shenzhen, Guangdong, China
- Center for Mental Health, Shenzhen University, Shenzhen, Guangdong, China
| | - Xinli Chi
- School of Psychology, Shenzhen University, Shenzhen, Guangdong, China.
- Center for Mental Health, Shenzhen University, Shenzhen, Guangdong, China.
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6
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Chan RYT, Hu HX, Wang LL, Chan MKM, Ho ZTY, Cheng KM, Lui SSY, Chan RCK. Emotional subtypes in patients with depression: A cluster analysis. Psych J 2023; 12:452-460. [PMID: 36859636 DOI: 10.1002/pchj.635] [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: 05/17/2022] [Accepted: 12/15/2022] [Indexed: 03/03/2023]
Abstract
Major depressive disorder (MDD) is associated with deficits in emotion experience, expression and regulation. Whilst emotion regulation deficits prolong MDD, emotion expression influences symptomatic presentations, and anticipatory pleasure deficits predict recurrence risk. Profiling MDD patients from an emotion componential perspective can characterize subtypes with different clinical and functional outcomes. This study aimed to investigate emotional subtypes of MDD. A two-stage cluster analysis applied to 150 MDD patients. Clustering variables included emotion experience measured by Temporal Experience of Pleasure Scale, emotion expression measured by Toronto Alexithymia Scale, and emotion regulation measured by Emotion Regulation Questionnaire. We validated the resultant clusters by comparing their symptoms and functioning with that of 50 controls. Cluster 1 (n = 50) exhibited intact emotion experience and expression yet adopted reappraisal rather than suppression strategy, whereas Cluster 2 (n = 66) exhibited generalized emotional deficits. Cluster 3 (n = 34) exhibited emotion expression deficits and adopted both reappraisal and suppression strategies. On validation, Cluster 2 exhibited the worst, but Cluster 1 exhibited the least symptoms and social functioning impairments. Cluster 3 was intermediate among the two other subtypes. Our findings support the existence of different emotional subtypes in MDD patients, and have clinical and theoretical implications for developing future specific treatments for MDD.
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Affiliation(s)
- Rachel Y T Chan
- Castle Peak Hospital, Hong Kong Special Administration Region, China
| | - Hui-Xin Hu
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Ling-Ling Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Mandy K M Chan
- Castle Peak Hospital, Hong Kong Special Administration Region, China
| | - Zoe T Y Ho
- Castle Peak Hospital, Hong Kong Special Administration Region, China
| | - Koi-Man Cheng
- Castle Peak Hospital, Hong Kong Special Administration Region, China
| | - Simon S Y Lui
- Department of Psychiatry, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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7
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Flint J. The genetic basis of major depressive disorder. Mol Psychiatry 2023; 28:2254-2265. [PMID: 36702864 PMCID: PMC10611584 DOI: 10.1038/s41380-023-01957-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 12/30/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023]
Abstract
The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, Billy and Audrey Wilder Endowed Chair in Psychiatry and Neuroscience, Center for Neurobehavioral Genetics, 695 Charles E. Young Drive South, 3357B Gonda, Box 951761, Los Angeles, CA, 90095-1761, USA.
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8
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Barboza-Salerno GE, Kosloski A, Weir H, Thompson D, Bukreyev A. A Network Analysis of the Relationship Between Mental and Physical Health in Unsheltered Homeless Persons in Los Angeles County. JOURNAL OF INTERPERSONAL VIOLENCE 2023; 38:5902-5936. [PMID: 36300615 DOI: 10.1177/08862605221127222] [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: 06/16/2023]
Abstract
Homelessness is a public health crisis both nationally, in the United States, and internationally. Nevertheless, due to the hidden vulnerabilities of persons who are without shelter, little is known about their experiences during periods of homelessness. The present research adopts a network approach that conceptualizes how the major risk factors of homelessness interact, namely substance abuse problems, poor mental health, disability, and exposure to physical or sexual violence by an intimate partner. Our analysis draws on a large demographic survey of over 5,000 unsheltered homeless persons conducted in 2017 by the Los Angeles Homeless Services Authority. We estimated a network structure for 12 survey items tapping individual risk using the graphical least absolute shrinkage and selection operator algorithm. We then examined network centrality metrics and implemented a community detection algorithm to detect communities in the network. Our results indicated that mental illness and intimate partner violence (IPV) are central measures that connect all other mental and physical health variables together and that post-traumatic stress disorder and IPV are both highly affected by changes in any part of the network and, in turn, affect changes in other parts of the network. A community detection analysis derived four communities characterized by disability, sexual victimization and health, substance use, and mental health issues. Finally, a directed acyclic graph revealed that drug abuse and physical disability were key drivers of the overall system. We conclude with a discussion of the major implications of our findings and suggest how our results might inform programs aimed at homelessness prevention and intervention.
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Affiliation(s)
| | - Anna Kosloski
- School of Public Affairs, University of Colorado Colorado Springs, Colorado Springs, CO, USA
| | - Henriikka Weir
- School of Public Affairs, University of Colorado Colorado Springs, Colorado Springs, CO, USA
| | | | - Alexey Bukreyev
- College of Arts, Letters and Sciences, University of Colorado Colorado Springs, Colorado Springs, CO, USA
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Huang D, Susser E, Rudolph KE, Keyes KM. Depression networks: a systematic review of the network paradigm causal assumptions. Psychol Med 2023; 53:1665-1680. [PMID: 36927618 DOI: 10.1017/s0033291723000132] [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: 03/18/2023]
Abstract
The network paradigm for psychiatric disorder nosology was proposed based on the hypothesis that mental disorders are caused by networks of symptoms that are themselves causally related. Researchers have widely applied and integrated this paradigm to examine a variety of mental disorders, particularly depression. Existing studies generally focus on the correlation structure of symptoms, inferring causal relationships. Thus, presumption of causality may not be justified. The goal of this review was to examine the assumptions necessary for causal inference in network studies of depression. Specifically, we examined whether and how network studies address common violations of causal assumptions (i.e. no measurement error, exchangeability, and positivity). Of the 41 studies reviewed, five (12%) studies discussed sources of confounding unrelated to measurement error; none discussed positivity; and five conducted post-hoc analysis for measurement error. Depression network studies, in principle, are conducted under the assumption that symptom relationships are causal. Yet, in practice, studies seldomly discussed or adequately tested assumptions required to infer causality. Researchers continue to design studies that are unable to support the credibility of the network paradigm for the study of depression. There is a critical need to ensure scientific efforts cease to perpetuate problematic designs and findings to a potentially unsubstantiated paradigm.
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Affiliation(s)
- Debbie Huang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Ezra Susser
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, New York, United States of America
| | - Kara E Rudolph
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Katherine M Keyes
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
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10
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Saari TT. Empirical and Authoritative Classification of Neuropsychiatric Syndromes in Neurocognitive Disorders. J Neuropsychiatry Clin Neurosci 2023; 35:39-47. [PMID: 35872615 DOI: 10.1176/appi.neuropsych.21100249] [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] [Indexed: 01/17/2023]
Abstract
Neuropsychiatric symptoms of neurocognitive disorders have been classified into higher-order constructs, often called neuropsychiatric syndromes. As with the general psychopathology literature, these classifications have been achieved through two approaches: empirical and authoritative. The authoritative approach relies on expert panels that condense the available evidence into operational criteria, whereas the empirical approach uses statistical methods to discover symptom patterns and possible hierarchies formed by them. In this article, the author reviews the strengths and weaknesses of both approaches using general psychopathology literature as a reference point. The authoritative approach, influenced by the DSM, has led to several sets of criteria, which could aid clinical trials, diagnostics, and communication. However, unknown reliability and the complex relationships between empirical evidence and published criteria may limit the utility of current criteria. The empirical approach has been used to explore syndrome structures on the basis of rating scales for neuropsychiatric symptoms. The structures suggested in these studies have not been replicated easily and have been limited by either small sample sizes, restricted breadth of neuropsychiatric assessment, or both. Suggestions for further development of both approaches are offered. First, neuropsychiatric symptoms and syndromes need to be studied with measures of broad scope and in large samples. These requirements are prerequisites not only for eliciting highly informative empirical classifications but also for understanding these symptoms at a more nuanced level. Second, both approaches could benefit from more transparency. Finally, the reliability of the available authoritative criteria should be examined.
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Affiliation(s)
- Toni Tapani Saari
- Department of Neurology, University of Eastern Finland, Kuopio, and NeuroCenter, Neurology, Kuopio University Hospital, Kuopio, Finland
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11
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Psychometric Evaluation of The Irritability Scale-Initial Version in Chinese Cancer Patients. Nurs Res 2023; 72:49-57. [PMID: 35997690 DOI: 10.1097/nnr.0000000000000615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Irritability is a common experience of depressed Chinese patients but is understudied and poorly measured. OBJECTIVE We aimed to assess psychometric properties of a new measure of irritability in Chinese cancer patients across the social and political spectrum. METHODS The Irritability Scale-Initial Version (TISi) was translated into Chinese and tested in two samples of Chinese cancer patients undergoing treatments: 52 patients in Beijing, China, between 2018 and 2019 and 65 patients in Taipei, Taiwan, in 2020. RESULTS The Chinese version of TISi demonstrated high internal consistency, high reliability based on the split-half method in the two samples, and satisfactory discriminant validity using the Chinese version of the 17-item Hamilton Rating Scale for Depression and the seven-item depression subscale of the Hospital Anxiety and Depression Scale in the Beijing sample. A confirmatory factor analysis produced factor loadings in both samples, which resembled a sample of American cancer patients. Three TISi items were loaded more highly on the physical instead of the original behavioral subscale in the Beijing sample. A possible influence of cultures was explained. CONCLUSION The Chinese version of TISi has satisfactory psychometric properties for assessing the level of irritability in Chinese cancer patients. Future large-sample studies are needed to further determine TISi's factorial structure, test-retest reliability, sensitivity to change, and predictive validity for depression in Chinese cancer patients.
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12
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Huang J, Zheng J, Ling-Ling G. Individual and dyadic network analyses of depressive symptoms in Chinese postpartum couples: A cross-sectional study. Midwifery 2023; 116:103529. [PMID: 36323077 DOI: 10.1016/j.midw.2022.103529] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 09/10/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The network approach to psychopathology is symptom oriented and may open new possibilities for intervention development and health care practices in postpartum depression. OBJECTIVE To investigate the individual and dyadic symptom network of postpartum depression in Chinese mothers and fathers in the very early postpartum period. DESIGN A cross-sectional study was conducted. SETTINGS AND PARTICIPANTS A total of 457 couples in the 2∼3 days postpartum period was recruited consecutively from a hospital in Guangzhou, China from September 2020 to April 2021. METHODS The Edinburgh Postnatal Depression Scale and socio-demographic and obstetric data sheet were used to collect data. We estimated the individual symptom networks of postpartum mothers and fathers separately and a dyadic symptom network that consisted of symptoms of both spouses. Network characteristics including global strength and node centralities were analyzed and systematically compared. RESULTS Strength centralities in the individual networks showed acceptable stability [Correlation stability coefficient (CS) for mothers = 0.60; CS for fathers = 0.52]. The central depressive symptoms in mothers were Crying (Zstrength = 1.32), Overwhelmed (Zstrength = 1.01) and Sad mood (Zstrength = 0.93). The central depressive symptom in fathers was Sad Mood (Zstrength = 1.35). The symptom "Crying" had a distinctive link to thoughts of self-harm in fathers. The symptom network of mothers (global strength = 4.15) was more interconnected than that of fathers (global strength = 3.74). There was a statistically significant but unstable within-couple connection of thoughts of self-harm (CS = 0.21). CONCLUSIONS Postpartum mothers are more vulnerable to activation spreads of depressive symptoms than postpartum fathers. Symptoms including "Sad mood", "Overwhelmed" and "Crying" warrant the attention of health care providers. Investigations with larger sample sizes and gender-sensitive instruments are needed to further unfold the individual and dyadic symptom dynamics of postpartum depression.
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Affiliation(s)
- Jiasheng Huang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Jie Zheng
- School of Nursing, Peking University, Beijing, China
| | - Gao Ling-Ling
- School of Nursing, Sun Yat-sen University, Guangzhou, China.
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13
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Ma S, Yang J, Cheng H, Wang W, Chen G, Bai H, Yao L, Liu Z. The central symptoms of depression, anxiety, and somatization: a network analysis. ALL LIFE 2022. [DOI: 10.1080/26895293.2022.2120091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
Affiliation(s)
- Simeng Ma
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Jun Yang
- School of Information Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China
| | - Haofan Cheng
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Wei Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Guopeng Chen
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Hanping Bai
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Lihua Yao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
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14
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Peng P, Chen Q, Liang M, Liu Y, Chen S, Wang Y, Yang Q, Wang X, Li M, Wang Y, Hao Y, He L, Wang Q, Zhang J, Ma Y, He H, Zhou Y, Li Z, Xu H, Long J, Qi C, Tang YY, Liao Y, Tang J, Wu Q, Liu T. A network analysis of anxiety and depression symptoms among Chinese nurses in the late stage of the COVID-19 pandemic. Front Public Health 2022; 10:996386. [PMID: 36408014 PMCID: PMC9667894 DOI: 10.3389/fpubh.2022.996386] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 10/10/2022] [Indexed: 01/26/2023] Open
Abstract
Background Nurses are at high risk for depression and anxiety symptoms after the outbreak of the COVID-19 pandemic. We aimed to assess the network structure of anxiety and depression symptoms among Chinese nurses in the late stage of this pandemic. Method A total of 6,183 nurses were recruited across China from Oct 2020 to Apr 2021 through snowball sampling. We used Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder scale-7 (GAD-7) to assess depression and anxiety, respectively. We used the Ising model to estimate the network. The index "expected influence" and "bridge expected influence" were applied to determine the central symptoms and bridge symptoms of the anxiety-depression network. We tested the stability and accuracy of the network via the case-dropping procedure and non-parametric bootstrapping procedure. Result The network had excellent stability and accuracy. Central symptoms included "restlessness", "trouble relaxing", "sad mood", and "uncontrollable worry". "Restlessness", "nervous", and "suicidal thoughts" served as bridge symptoms. Conclusion Restlessness emerged as the strongest central and bridge symptom in the anxiety-depression network of nurses. Intervention on depression and anxiety symptoms in nurses should prioritize this symptom.
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Affiliation(s)
- Pu Peng
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Qiongni Chen
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China,Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Mining Liang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China,Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yueheng Liu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shubao Chen
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yunfei Wang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Qian Yang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xin Wang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Manyun Li
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yingying Wang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuzhu Hao
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Li He
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Qianjin Wang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Junhong Zhang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuejiao Ma
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Haoyu He
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China,Department of Psychology, College of Education, Hunan First Normol University, Changsha, China
| | - Yanan Zhou
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China,Department of Psychiatry, Hunan Brain Hospital (Hunan Second People's Hospital, Changsha, China
| | - Zejun Li
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Huixue Xu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jiang Long
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chang Qi
- Department of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yi-Yuan Tang
- College of Health Solutions, Arizona State University, Phoenix, AZ, United States
| | - Yanhui Liao
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jinsong Tang
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qiuxia Wu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China,Qiuxia Wu
| | - Tieqiao Liu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China,*Correspondence: Tieqiao Liu
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15
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van Loo HM, Aggen SH, Kendler KS. The structure of the symptoms of major depression: Factor analysis of a lifetime worst episode of depressive symptoms in a large general population sample. J Affect Disord 2022; 307:115-124. [PMID: 35367501 PMCID: PMC10833125 DOI: 10.1016/j.jad.2022.03.064] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND A range of depressive symptoms may occur during an episode of major depression (MD). Do these symptoms describe a single disorder liability or different symptom dimensions? This study investigates the structure and clinical relevance of an expanded set of depressive symptoms in a large general population sample. METHODS We studied 43,431 subjects from the Dutch Lifelines Cohort Study who participated in an online survey assessing the 9 symptom criteria of MD (DSM-IV-TR) and additional depressive symptoms during their worst lifetime episode of depressive symptoms lasting two weeks or more. Exploratory factor analyses were performed on expanded sets of 9, 14, and 24 depressive symptoms. The clinical relevance of the identified symptom dimensions was analyzed in confirmatory factor analyses including ten external validators. RESULTS A single dimension adequately accounted for the covariation among the 9 DSM-criteria, but multiple dimensions were needed to describe the 14 and 24 depressive symptoms. Five dimensions described the structure underlying the 24 depressive symptoms. Three cognitive affective symptom dimensions were mainly associated with risk factors for MD. Two somatic dimensions -appetite/weight problems and sleep problems-were mainly associated with BMI and age, respectively. LIMITATIONS Respondents of our online survey tended to be more often female, older, and more highly educated than non-respondents. CONCLUSIONS Different symptom dimensions described the structure of depressive symptoms during a lifetime worst episode in a general population sample. These symptom dimensions resembled those reported in a large clinical sample of Han-Chinese women with recurrent MD, suggesting robustness of the syndrome of MD.
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Affiliation(s)
- Hanna M van Loo
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands.
| | - Steven H Aggen
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
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16
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Brusco MJ, Steinley D, Watts AL. Disentangling relationships in symptom networks using matrix permutation methods. PSYCHOMETRIKA 2022; 87:133-155. [PMID: 34282531 DOI: 10.1007/s11336-021-09760-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 02/12/2021] [Accepted: 03/18/2021] [Indexed: 06/13/2023]
Abstract
Common outputs of software programs for network estimation include association matrices containing the edge weights between pairs of symptoms and a plot of the symptom network. Although such outputs are useful, it is sometimes difficult to ascertain structural relationships among symptoms from these types of output alone. We propose that matrix permutation provides a simple, yet effective, approach for clarifying the order relationships among the symptoms based on the edge weights of the network. For directed symptom networks, we use a permutation criterion that has classic applications in electrical circuit theory and economics. This criterion can be used to place symptoms that strongly predict other symptoms at the beginning of the ordering, and symptoms that are strongly predicted by other symptoms at the end. For undirected symptom networks, we recommend a permutation criterion that is based on location theory in the field of operations research. When using this criterion, symptoms with many strong ties tend to be placed centrally in the ordering, whereas weakly-tied symptoms are placed at the ends. The permutation optimization problems are solved using dynamic programming. We also make use of branch-search algorithms for extracting maximum cardinality subsets of symptoms that have perfect structure with respect to a selected criterion. Software for implementing the dynamic programming algorithms is available in MATLAB and R. Two networks from the literature are used to demonstrate the matrix permutation algorithms.
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17
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Berta A, Miguel Ángel C, Clara GS, Rubén H. A bibliometric analysis of 10 years of research on symptom networks in psychopathology and mental health. Psychiatry Res 2022; 308:114380. [PMID: 34999293 DOI: 10.1016/j.psychres.2021.114380] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 11/18/2022]
Abstract
Psychopathology networks consist of aspects (e.g., symptoms) of mental disorders (nodes) and the connections between those aspects (edges). This article aims to analyze the research literature on network analysis in psychopathology and mental health for the last ten years. Statistical descriptive analysis was complemented with two bibliometric techniques: performance analysis and co-word analysis. There is an increase in publications that has passed from 1 article published in 2010 to 172 papers published in 2020. The 398 articles in the sample have 1,910 authors in total, being most of them occasional contributors. The Journal of Affective Disorders is the one with the highest number of publications on network analysis in psychopathology and mental health, followed by the Journal of Abnormal Psychology and Psychological Medicine stand out. The present study shows that this perspective in psychopathology and mental health is a recent field of study, but with solid advances in recent years from a wide variety of researchers, mainly from USA and Europe, who have extensively studied symptom networks in depression, anxiety, and post-traumatic stress disorders. However, gaps are identified in other psychological behaviors such as suicide, populations such as the elderly, and gender studies.
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Affiliation(s)
- Ausín Berta
- School of Psychology, Personality, Evaluation and Clinical Psychology Department, Complutense University of Madrid, Spain.
| | - Castellanos Miguel Ángel
- School of Psychology, Psychobiology and Methodology in Behavioral Sciences Department, Complutense University of Madrid, Spain
| | - González-Sanguino Clara
- School of Psychology, Personality, Evaluation and Clinical Psychology Department, Complutense University of Madrid, Spain
| | - Heradio Rubén
- Department of Computer Systems and Software Engineering, National Distance Education University, Madrid, Spain
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18
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Manfro PH, Pereira RB, Rosa M, Cogo-Moreira H, Fisher HL, Kohrt BA, Mondelli V, Kieling C. Adolescent depression beyond DSM definition: a network analysis. Eur Child Adolesc Psychiatry 2021; 32:881-892. [PMID: 34854985 PMCID: PMC10147766 DOI: 10.1007/s00787-021-01908-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 09/23/2021] [Indexed: 01/22/2023]
Abstract
Calls for refining the understanding of depression beyond diagnostic criteria have been growing in recent years. We examined the prevalence and relevance of DSM and non-DSM depressive symptoms in two Brazilian school-based adolescent samples with two commonly used scales, the Patient Health Questionnaire (PHQ-A) and the Mood and Feelings Questionnaire (MFQ). We analyzed cross-sectional data from two similarly recruited samples of adolescents aged 14-16 years, as part of the Identifying Depression Early in Adolescence (IDEA) study in Brazil. We assessed dimensional depressive symptomatology using the PHQ-A in the first sample (n = 7720) and the MFQ in the second sample (n = 1070). We conducted network analyses to study symptom structure and centrality estimates of the two scales. Additionally, we compared centrality of items included (e.g., low mood, anhedonia) and not included in the DSM (e.g., low self-esteem, loneliness) in the MFQ. Sad mood and worthlessness items were the most central items in the network structure of the PHQ-A. In the MFQ sample, self-hatred and loneliness, two non-DSM features, were the most central items and DSM and non-DSM items in this scale formed a highly interconnected network of symptoms. Furthermore, analysis of the MFQ sample revealed DSM items not to be more frequent, severe or interconnected than non-DSM items, but rather part of a larger network of symptoms. A focus on symptoms might advance research on adolescent depression by enhancing our understanding of the disorder.
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Affiliation(s)
- Pedro H. Manfro
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2350, 400N, Porto Alegre, RS 90035-903 Brazil
| | - Rivka B. Pereira
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2350, 400N, Porto Alegre, RS 90035-903 Brazil
| | - Martha Rosa
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2350, 400N, Porto Alegre, RS 90035-903 Brazil
| | - Hugo Cogo-Moreira
- Faculty of Teacher Education and Languages, Department of Education, ICT and Learning, Østfold University College, Halden, Norway
| | - Helen L. Fisher
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- ESRC Centre for Society and Mental Health, King’s College London, London, UK
| | - Brandon A. Kohrt
- Division of Global Mental Health, Department of Psychiatry, School of Medicine and Health Sciences, The George Washington University, Washington, DC USA
| | - Valeria Mondelli
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- National Institute for Health Research Mental Health Biomedical Research Centre, South London and Maudsley NHS Foundation Trust and King’s College London, London, UK
| | - Christian Kieling
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2350, 400N, Porto Alegre, RS 90035-903 Brazil
- Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS Brazil
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19
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Peel AJ, Armour C, Buckman JE, Coleman JR, Curzons SC, Davies MR, Hübel C, Jones I, Kalsi G, McAtarsney-Kovacs M, McIntosh AM, Monssen D, Mundy J, Rayner C, Rogers HC, Skelton M, ter Kuile A, Thompson KN, Breen G, Danese A, Eley TC. Comparison of depression and anxiety symptom networks in reporters and non-reporters of lifetime trauma in two samples of differing severity. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2021; 6:100201. [PMID: 34988540 PMCID: PMC8689407 DOI: 10.1016/j.jadr.2021.100201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 06/24/2021] [Accepted: 07/18/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Reported trauma is associated with differences in the course and outcomes of depression and anxiety. However, no research has explored the association between reported trauma and patterns of clinically relevant symptoms of both depression and anxiety. METHODS We used network analysis to investigate associations between reported trauma and depression and anxiety symptom interactions in affected individuals from the Genetic Links to Anxiety and Depression (GLAD) Study (n = 17720), and population volunteers from the UK Biobank (n = 11120). Participants with current moderate symptoms of depression or anxiety were grouped into reporters and non-reporters of lifetime trauma. Networks of 16 depression and anxiety symptoms in the two groups were compared using the network comparison test. RESULTS In the GLAD Study, networks of reporters and non-reporters of lifetime trauma did not differ on any metric. In the UK Biobank, the symptom network of reporters had significantly greater density (7.80) than the network of non-reporters (7.05). LIMITATIONS The data collected in the GLAD Study and the UK Biobank are self-reported with validated or semi-validated questionnaires. CONCLUSIONS Reported lifetime trauma was associated with stronger interactions between symptoms of depression and anxiety in population volunteers. Differences between reporters and non-reporters may not be observed in individuals with severe depression and/or anxiety due to limited variance in the presentation of disorder.
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Affiliation(s)
- Alicia J. Peel
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
| | - Chérie Armour
- School of Psychology, Queens University Belfast, Belfast BT7 1NN, Northern Ireland
| | - Joshua E.J. Buckman
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, London WC1E 7HB, UK
- iCope – Camden and Islington Psychological Therapies Services, Camden & Islington NHS Foundation Trust, 4 St Pancras Way, London NW1 0PE, UK
| | - Jonathan R.I. Coleman
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Susannah C.B. Curzons
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Molly R. Davies
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Christopher Hübel
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ian Jones
- National Centre for Mental Health, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff CF24 4HQ, UK
| | - Gursharan Kalsi
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Monika McAtarsney-Kovacs
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | | | - Dina Monssen
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Jessica Mundy
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Christopher Rayner
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
| | - Henry C. Rogers
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Megan Skelton
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Abigail ter Kuile
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Katherine N. Thompson
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Andrea Danese
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
- National and Specialist CAMHS Trauma, Anxiety, and Depression Clinic, South London and Maudsley NHS Foundation Trust, London SE5 8AF, UK
| | - Thalia C. Eley
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology & Neuroscience; King's College London, London SE5 8AF, UK
- UK National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
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20
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Steen OD, van Borkulo CD, van Loo HM. Symptom networks in major depression do not diverge across sex, familial risk, and environmental risk. J Affect Disord 2021; 294:227-234. [PMID: 34303301 DOI: 10.1016/j.jad.2021.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/30/2021] [Accepted: 07/02/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Major depression (MD) is a heterogeneous disorder in terms of its symptoms. Symptoms vary by presence of risk factors such as female sex, familial risk, and environmental adversity. However, it is unclear if these factors also influence interactions between symptoms. This study investigates if symptom networks diverge across sex, familial risk, and adversity. METHODS We included 9713 subjects from the general population who reported a lifetime episode of MD based on DSM-IV criteria. The survey assessed a wide set of symptoms, both from within the DSM criteria as well as other symptoms commonly experienced in MD. We compared symptom endorsement rates across sex, age at onset, family history and environmental adversity. We used the Network Comparison Test to test for symptom network differences across risk factors. RESULTS We found differences in symptom endorsement between groups. For instance, participants with an early onset of MD reported suicidal ideation nearly twice as often compared to participants with a later onset. We did not find any robust differences in symptom networks, which suggests that symptom networks do not diverge across sex, familial risk, and adversity. LIMITATIONS We estimated symptom networks of individuals during their worst lifetime episode of MD. Network differences might exist in a prodromal stage, while disappearing in full-blown MD (equifinality). Furthermore, as we used retrospective reports, results could be prone to recall bias. CONCLUSIONS Despite MD's heterogeneous symptomatology, interactions between symptoms are stable across risk factors and sex.
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Affiliation(s)
- Olivier D Steen
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands.
| | - Claudia D van Borkulo
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands; Centre for Urban Mental Health, University of Amsterdam, Amsterdam, the Netherlands
| | - Hanna M van Loo
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
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21
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Berlim MT, Richard-Devantoy S, Dos Santos NR, Turecki G. The network structure of core depressive symptom-domains in major depressive disorder following antidepressant treatment: a randomized clinical trial. Psychol Med 2021; 51:2399-2413. [PMID: 32312344 DOI: 10.1017/s0033291720001002] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND Network analysis (NA) conceptualizes psychiatric disorders as complex dynamic systems of mutually interacting symptoms. Major depressive disorder (MDD) is a heterogeneous clinical condition, and very few studies to date have assessed putative changes in its psychopathological network structure in response to antidepressant (AD) treatment. METHODS In this randomized trial with adult depressed outpatients (n = 151), we estimated Gaussian graphical models among nine core MDD symptom-domains before and after 8 weeks of treatment with either escitalopram or desvenlafaxine. Networks were examined with the measures of cross-sectional and longitudinal structure and connectivity, centrality and predictability as well as stability and accuracy. RESULTS At baseline, the most connected MDD symptom-domains were fatigue-cognitive disturbance, whereas at week 8 they were depressed mood-suicidality. Overall, the most central MDD symptom-domains at baseline and week 8 were, respectively, fatigue and depressed mood; in contrast, the most peripheral symptom-domain across both timepoints was appetite/weight disturbance. Furthermore, the psychopathological network at week 8 was significantly more interconnected than at baseline, and they were also structurally dissimilar. CONCLUSION Our findings highlight the utility of focusing on the dynamic interaction between depressive symptoms to better understand how the treatment with ADs unfolds over time. In addition, depressed mood, fatigue, and cognitive/psychomotor disturbance seem to be central MDD symptoms that may be viable targets for novel, focused therapeutic interventions.
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Affiliation(s)
- Marcelo T Berlim
- Depressive Disorders Program & McGill Group for Suicide Studies, McGill University & Douglas Mental Health University Institute, Montréal, Québec, Canada
| | - Stephane Richard-Devantoy
- Depressive Disorders Program & McGill Group for Suicide Studies, McGill University & Douglas Mental Health University Institute, Montréal, Québec, Canada
| | - Nicole Rodrigues Dos Santos
- Depressive Disorders Program & McGill Group for Suicide Studies, McGill University & Douglas Mental Health University Institute, Montréal, Québec, Canada
| | - Gustavo Turecki
- Depressive Disorders Program & McGill Group for Suicide Studies, McGill University & Douglas Mental Health University Institute, Montréal, Québec, Canada
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22
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Jones PJ, Robinaugh DR. An Answer to "So What?" Implications of Network Theory for Research and Practice. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2021; 19:204-210. [PMID: 34690584 PMCID: PMC8475911 DOI: 10.1176/appi.focus.20200050] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Research and practice in psychiatry and clinical psychology have been guided by differing schools of thought over the years. Recently, the network theory of psychopathology has arisen as a framework for thinking about mental health. Network theory challenges three common assumptions: psychological problems are caused by disease entities that exist independently of their signs and symptoms, classification and diagnosis of psychological problems should follow a medical model, and psychological problems are caused by diseases or aberrations in the brain. Conversely, network theory embraces other assumptions that are well accepted in clinical practice (e.g., the interaction of thoughts, behaviors, and emotions, as posited in cognitive-behavioral therapies) and integrates those assumptions into a coherent framework for research and practice. In this article, the authors review developments in network theory by focusing on anxiety-related conditions, discuss future areas for change, and outline implications of network theory for research and clinical practice.
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Affiliation(s)
- Payton J Jones
- Department of Psychology Harvard University, Cambridge, MA (Jones); Center for Anxiety and Traumatic Stress, Massachusetts General Hospital, Boston, MA (Jones, Robinaugh)
| | - Donald R Robinaugh
- Department of Psychology Harvard University, Cambridge, MA (Jones); Center for Anxiety and Traumatic Stress, Massachusetts General Hospital, Boston, MA (Jones, Robinaugh)
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23
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Malgaroli M, Calderon A, Bonanno GA. Networks of major depressive disorder: A systematic review. Clin Psychol Rev 2021; 85:102000. [DOI: 10.1016/j.cpr.2021.102000] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 01/06/2021] [Accepted: 02/23/2021] [Indexed: 12/14/2022]
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24
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An alternative approach to future diagnostic standards for major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 105:110133. [PMID: 33049324 DOI: 10.1016/j.pnpbp.2020.110133] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/06/2020] [Indexed: 12/17/2022]
Abstract
During the period extending from 1780 to 1880, the conceptualization of melancholia changed from an intellectual to a mood model. The modern view of depression, based on Kraepelinian dualism, has reflected changes in opinion on psychiatric taxonomy of individual melancholia. From the point of view of an "operational revolution," the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, 3rd edition (DSM-III) were based on a neoKraepelinian approach rooted in disease essentialism. In the revision process from the DSM-IV to the DSM-5, a combined dimensional and categorial approach was used. In the DSM-5, the diagnostic criteria for major depressive disorder are polythetic and operational in approach reflecting the heterogeneity of major depressive disorder. Although 227 different symptom combinations fulfilling the diagnostic criteria for major depressive disorder can be theoretically calculated, certain symptom combinations are more prevalent than others in real clinical situations. The heterogeneity of these operational criteria for major depressive disorder have been criticized in a manner informed by the Wittgensteinian analogy of the language game. Herein, our network analysis proposes a novel perspective on the psychopathology of major depressive disorder. The novel approach suggested here may lay the foundation for a redefinition of the traditional taxonomy of depression.
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25
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Abstract
Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research, this is often implausible. In this article, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct MNMs and propose an ℓ1-regularized nodewise regression approach to estimate them. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification.
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Hallquist MN, Wright AGC, Molenaar PCM. Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory. MULTIVARIATE BEHAVIORAL RESEARCH 2021; 56:199-223. [PMID: 31401872 PMCID: PMC7012663 DOI: 10.1080/00273171.2019.1640103] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.
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Affiliation(s)
| | | | - Peter C M Molenaar
- Department of Human Development and Family Studies, Penn State University
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Park SC, Kim YK. Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:103-116. [PMID: 33834397 DOI: 10.1007/978-981-33-6044-0_7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
The Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition (DSM-IV) was revised based on a combination of a categorical and a dimensional approach such that in the DSM, Fifth Edition (DSM-5), depressive disorders have been separated as a distinctive disease entity from bipolar disorders, consistent with the deconstruction of Kraepelinian dualism. Additionally, the diagnostic thresholds of depressive disorders may be reduced due to the addition of "hopelessness" to the subjective descriptors of depressed mood and the removal of the "bereavement exclusion." Manic/hypomanic, psychotic, and anxious symptoms in major depressive disorder (MDD) and other depressive disorders are described using the transdiagnostic specifiers of "with mixed features," "with psychotic features," and "with anxious distress," respectively. Additionally, due to the polythetic and operational characteristics of the DSM-5 diagnostic criteria, the heterogeneity of MDD is inevitable. Thus, 227 different symptom combinations fulfill the DSM-5 diagnostic criteria for MDD. This heterogeneity of MDD is criticized in view of the Wittgensteinian analogy of language game. Depression subtypes determined by disturbances in monoamine levels and the severity of the disease have been identified in the literature. According to a review of the Gottesman and Gould criteria, neuroticism, morning cortisol, cortisol awakening response, asymmetry in frontal cortical activity on electroencephalography (EEG), and probabilistic reward learning, among other variables, are evidenced as endophenotypes for depressive disorders. Network analysis has been proposed as a potential method to compliment the limitations of current diagnostic criteria and to explore the pathways between depressive symptoms, as well as to identify novel and interesting relationships between depressive symptoms. Based on the literature on network analysis in this field, no differences in the centrality index of the DSM and non-DSM symptoms were repeatedly present among patients with MDD. Furthermore, MDD and other depressive syndromes include two of the Research Domain Criteria (RDoC), including the Loss construct within the Negative Valence Systems domains and various Reward constructs within the Positive Valence Systems domain.
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Affiliation(s)
- Seon-Cheol Park
- Department of Psychiatry, Hanyang University Guri Hospital, Guri, Republic of Korea
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University Ansan Hospital, College of Medicine, Ansan, Republic of Korea.
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Park SC, Kim Y, Kim K, Woo YS, Kim JB, Jang EY, Lee HY, Yim HW, Ham BJ, Kim JM, Park YC. Network Analysis of the Symptoms of Depressive Disorders Over the Course of Therapy: Changes in Centrality Measures. Psychiatry Investig 2021; 18:48-58. [PMID: 33460534 PMCID: PMC7897865 DOI: 10.30773/pi.2020.0367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/07/2020] [Indexed: 11/27/2022] Open
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.
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Affiliation(s)
- Seon-Cheol Park
- Department of Psychiatry, Inje University Haeundae Paik Hospital, Busan, Republic of Korea
| | - Yaeseul Kim
- Department of Psychiatry, Hanyang University School of Medicine, Seoul, Republic of Korea
| | - Kiwon Kim
- Department of Psychiatry, Veteran Health Service Medical Center, Seoul, Republic of Korea
| | - Young Sup Woo
- Department of Psychiatry, The Catholic University of Korea, Yeouido St. Mary's Hospital, Seoul, Republic of Korea
| | - Jung-Bum Kim
- Department of Psychiatry, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Eun Young Jang
- Department of Counselling Psychology, Honam Unviersity College of Humanities and Social Sciences, Gwangju, Republic of Korea
| | - Hwa-Young Lee
- Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Hyeon-Woo Yim
- Department of Preventive Medicine, The Catholic University of Korea College of Medicine, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Jae-Min Kim
- Department of Psychiatry, Chonnam National University School of Medicine, Gwangju, Republic of Korea
| | - Yong Chon Park
- Department of Neuropsychiatry, Hanyang University Guri Hospital, Guri, Republic of Korea
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Yun JY, Kim YK. Phenotype Network and Brain Structural Covariance Network of Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:3-18. [PMID: 33834391 DOI: 10.1007/978-981-33-6044-0_1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea. .,Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University Ansan Hospital, College of Medicine, Ansan, Republic of Korea
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Wichers M, Riese H, Hodges TM, Snippe E, Bos FM. A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression. Front Psychiatry 2021; 12:719490. [PMID: 34777038 PMCID: PMC8581034 DOI: 10.3389/fpsyt.2021.719490] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/30/2021] [Indexed: 11/13/2022] Open
Abstract
The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.
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Affiliation(s)
- Marieke Wichers
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands
| | - Harriëtte Riese
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands
| | - Taylor M Hodges
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands
| | - Evelien Snippe
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands
| | - Fionneke M Bos
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands.,University of Groningen, University Medical Center Groningen, Department of Psychiatry, Rob Giel Research Center, Groningen, Netherlands
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Buelens T, Costantini G, Luyckx K, Claes L. Comorbidity Between Non-suicidal Self-Injury Disorder and Borderline Personality Disorder in Adolescents: A Graphical Network Approach. Front Psychiatry 2020; 11:580922. [PMID: 33329123 PMCID: PMC7728714 DOI: 10.3389/fpsyt.2020.580922] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 11/02/2020] [Indexed: 12/26/2022] Open
Abstract
In 2013, DSM-5 urged for further research on non-suicidal self-injury (NSSI) and defined NSSI disorder (NSSI-D) for the first time separate from borderline personality disorder (BPD). However, research on the comorbidity between NSSI-D and BPD symptoms is still scarce, especially in adolescent populations. The current study selected 347 adolescents who engaged at least once in NSSI (78.4% girls, M age = 15.05) and investigated prevalence, comorbidity, gender differences, and bridge symptoms of NSSI-D and BPD. Network analysis allowed us to visualize the comorbidity structure of NSSI-D and BPD on a symptom-level and revealed which bridge symptoms connected both disorders. Our results supported NSSI-D as significantly distinct from, yet closely related to, BPD in adolescents. Even though girls were more likely to meet the NSSI-D criteria, our findings suggested that the manner in which NSSI-D and BPD symptoms were interconnected, did not differ between girls and boys. Furthermore, loneliness, impulsivity, separation anxiety, frequent thinking about NSSI, and negative affect prior to NSSI were detected as prominent bridge symptoms between NSSI-D and BPD. These bridge symptoms could provide useful targets for early intervention in and prevention of the development of comorbidity between NSSI-D and BPD. Although the current study was limited by a small male sample, these findings do provide novel insights in the complex comorbidity between NSSI-D and BPD symptoms in adolescence.
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Affiliation(s)
- Tinne Buelens
- Research Unit Clinical Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Giulio Costantini
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Koen Luyckx
- School Psychology and Development in Context, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
- Unit for Professional Training and Service in the Behavioural Sciences, University of the Free State, Bloemfontein, South Africa
| | - Laurence Claes
- Research Unit Clinical Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
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Kaiser AJE, Funkhouser CJ, Mittal VA, Walther S, Shankman SA. Test-retest & familial concordance of MDD symptoms. Psychiatry Res 2020; 292:113313. [PMID: 32738552 PMCID: PMC7529979 DOI: 10.1016/j.psychres.2020.113313] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 11/23/2022]
Abstract
Psychopathology research has increasingly sought to study the etiology and treatment of individual symptoms, rather than categorical diagnoses. However, it is unclear whether commonly used measures have adequate psychometric properties for assessing individual symptoms. This study examined the test-retest reliability and familial concordance (an indicator of validity) of the symptoms of Major Depressive Disorder (MDD), a disorder consisting of nine core symptoms, most of which are aggregated (e.g., symptom 7 of the DSM criteria for MDD is worthlessness or guilt). Lifetime MDD symptoms were measured in 504 young adults (237 sibling pairs) using the Structured Clinical Interview for DSM-5 (SCID). Fifty-one people completed a second SCID within three weeks of their first SCID. Results indicated that aggregated and unaggregated symptoms demonstrated moderate to substantial test-retest reliability and generally significant, but slight to fair familial concordance (with the highest familial concordance being for markedly diminished interest or pleasure and its unaggregated components - decreased interest and decreased pleasure). Given the increasing focus on the differential validity of individual MDD symptoms, the present study suggests that interview-based assessments of depression can assess most individual symptoms with adequate levels of reliability and validity.
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Affiliation(s)
- Ariela J E Kaiser
- University of Illinois at Chicago, Department of Psychology, United States; Northwestern University, Department of Psychiatry and Behavioral Sciences, United States
| | - Carter J Funkhouser
- University of Illinois at Chicago, Department of Psychology, United States; Northwestern University, Department of Psychiatry and Behavioral Sciences, United States
| | - Vijay A Mittal
- Northwestern University, Department of Psychiatry and Behavioral Sciences, United States; Northwestern University, Departments of Psychology, Medical Social Sciences.. Institutes for Policy Research, Innovations in Developmental Sciences (DevSci), United States
| | - Sebastian Walther
- University of Bern, University Hospital of Psychiatry, Translational Research Center, Bern, Switzerland
| | - Stewart A Shankman
- University of Illinois at Chicago, Department of Psychology, United States; Northwestern University, Department of Psychiatry and Behavioral Sciences, United States.
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The Centrality of Depression and Anxiety Symptoms in Major Depressive Disorder Determined Using a Network Analysis. J Affect Disord 2020; 271:19-26. [PMID: 32312693 DOI: 10.1016/j.jad.2020.03.078] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 01/21/2020] [Accepted: 03/24/2020] [Indexed: 01/27/2023]
Abstract
BACKGROUND Comorbid anxiety symptoms are highly prevalent and closely linked with poorer treatment outcomes, chronicity, and hospitalization in major depressive disorder (MDD). Our study aimed to estimate the network of depression and anxiety symptoms that was developed based on a sample of MDD patients. METHODS We constructed a network of the 21 Beck Depression Inventory (BDI) symptoms and 21 Beck Anxiety Inventory (BAI) symptoms in 223 patients with MDD who were beginning psychiatric treatment. In addition, each of the depression and anxiety symptoms was considered to be an ordered categorical variable ranging in value from 0 to 3. RESULTS The three depression symptoms including loss of energy, loss of interest, and worthlessness and the seven anxiety symptoms including faintness or lightheadedness, feeling of choking, feeling scared, fear of the worst happening, nervousness, inability to relax, and feeling shaky were identified as the ten most central nodes within a network of depression and anxiety symptoms. The inter-connection between irritability and nervousness was a strong trans-diagnostic edge within the network of depression and anxiety symptoms. LIMITATIONS Because our study was designed in a cross-sectional manner, the networks were estimated undirectionally. CONCLUSIONS Our findings show that depression symptoms are not more central than anxiety symptoms within an estimated network structure of symptoms in patients with MDD. Moreover, the inter-connection between irritability and nervousness may suggests a probable trans-diagnostic association in MDD symptomatology.
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Park SC, Jang EY, Xiang YT, Kanba S, Kato TA, Chong MY, Lin SK, Yang SY, Avasthi A, Grover S, Kallivayalil RA, Udomratn P, Chee KY, Tanra AJ, Tan CH, Sim K, Sartorius N, Park YC, Shinfuku N. 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 2020; 74:344-353. [PMID: 32048773 PMCID: PMC7318233 DOI: 10.1111/pcn.12989] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 01/21/2020] [Accepted: 02/04/2020] [Indexed: 12/13/2022]
Abstract
AIM We aimed to estimate the network structures of depressive symptoms using network analysis and evaluated the geographic regional differences in theses network structures among Asian patients with depressive disorders. METHODS Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD), the network of the ICD-10 diagnostic criteria for depressive episode was estimated from 1174 Asian patients with depressive disorders. The node strength centrality of all ICD-10 diagnostic criteria for a depressive episode was estimated using a community-detection algorithm. In addition, networks of depressive symptoms were estimated separately among East Asian patients and South or Southeast Asian patients. Moreover, networks were estimated separately among Asian patients from high-income countries and those from middle-income countries. RESULTS Persistent sadness, fatigue, and loss of interest were the most centrally situated within the network of depressive symptoms in Asian patients with depressive disorders overall. A community-detection algorithm estimated that when excluding psychomotor disturbance as an outlier, the other nine symptoms formed the largest clinically meaningful cluster. Geographic and economic variations in networks of depressive symptoms were evaluated. CONCLUSION Our findings demonstrated that the typical symptoms of the ICD-10 diagnostic criteria for depressive episode are the most centrally situated within the network of depressive symptoms. Furthermore, our findings suggested that cultural influences related to geographic and economic distributions of participants could influence the estimated depressive symptom network in Asian patients with depressive disorders.
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Affiliation(s)
- Seon-Cheol Park
- Department of Psychiatry, Inje University Haeundae Paik Hospital, Busan, Republic of Korea
| | - Eun Young Jang
- Department of Counseling Psychology, Honam University College of Humanities and Social Sciences, Gwangju, Republic of Korea
| | - Yu-Tao Xiang
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Shigenobu Kanba
- Department of Neuropsychiatry, Graduate School of Medicine, Kyushu University, Fukuoka, Japan
| | - Takahiro A Kato
- Department of Neuropsychiatry, Graduate School of Medicine, Kyushu University, Fukuoka, Japan
| | - Mian-Yoon Chong
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung & Chang Gung University School of Medicine, Linkou, Taiwan
| | - Shih-Ku Lin
- Psychiatry Center, Tapei City Hospital, Taipei, Taiwan
| | - Shu-Yu Yang
- Department of Pharmacy, Taipei City Hospital and Fu Jen University, Taipei, Taiwan
| | - Ajit Avasthi
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Sandeep Grover
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | | | - Pichet Udomratn
- Department of Psychiatry, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
| | - Kok Yoon Chee
- Tunku Abdul Rahman Institute of Neurosciences, Kuala Lumpur, Malaysia
| | - Andi J Tanra
- Faculty of Medicine, Department of Psychiatry, Hasanuddin University, Makassar, Indonesia
| | - Chay-Hoon Tan
- Department of Pharmacology, National University Hospital, Singapore
| | - Kang Sim
- Institute of Mental Health, Buangkok Green Medical Park, Singapore
| | - Norman Sartorius
- Association for the Improvement of Mental Health Programmes, Geneva, Switzerland
| | - Yong Chon Park
- Department of Neuropsychiatry, Hanyang University Guri Hospital, Guri, Republic of Korea
| | - Naotaka Shinfuku
- Department of Social Welfare, School of Human Sciences, Seinan Gakuin University, Fukuoka, Japan
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Abstract
Most evidence-based pharmacological guidelines recommend selective serotonin reuptake inhibitors, serotoninnorepinephrine reuptake inhibitors, norepinephrine-dopamine reuptake inhibitors or norepinephrine and specific serotonin antidepressants as the first-line treatment for major depression. Since the clinical factors associated with treating patients with depression are relatively complex, it can be challenging to apply the recommendations of evidence-based medicine verbatim. Furthermore, the diagnostic criteria of major depressive disorders, which are defined in a polythetic and operational manner, inevitably result in their heterogeneity. Studies have inferred that depressive syndrome may be connected with “family resemblance” rather than being shared with a neurobiological essence. Therefore, the symptom-based selection of antidepressants can be supported by a network analysis that provides a novel perspective on the symptom structure of major depression. The symptom-based treatment algorithm suggests treatment options that can be applied to the symptoms that are included in and excluded from the diagnosis criteria of major depressive disorder. The symptom-based selection of antidepressants and other psychotropic agents involves matching the deconstructed symptoms of depression to the specific neuroanatomical regions and neurotransmitters. This ensures timely and optimized treatment options for patients with depression.
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Robinaugh DJ, Hoekstra RHA, Toner ER, Borsboom D. The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research. Psychol Med 2020; 50:353-366. [PMID: 31875792 PMCID: PMC7334828 DOI: 10.1017/s0033291719003404] [Citation(s) in RCA: 279] [Impact Index Per Article: 69.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.
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Affiliation(s)
- Donald J. Robinaugh
- Massachusetts General Hospital, Department of Psychiatry
- Harvard Medical School
| | | | - Emma R. Toner
- Massachusetts General Hospital, Department of Psychiatry
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Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000-2011 study. Soc Psychiatry Psychiatr Epidemiol 2020; 55:1273-1282. [PMID: 32047972 PMCID: PMC7544719 DOI: 10.1007/s00127-020-01843-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 02/03/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE Putative causal relations among depressive symptoms in forms of network structures have been of recent interest, with prior studies suggesting that high connectivity of the symptom network may drive the disease process. We examined in detail the network structure of depressive symptoms among participants with and without depressive disorders (DD; consisting of major depressive disorder (MDD) and dysthymia) at two time points. METHODS Participants were from the nationally representative Health 2000 and Health 2011 surveys. In 2000 and 2011, there were 5998 healthy participants (DD-) and 595 participants with DD diagnosis (DD+). Depressive symptoms were measured using the 13-item version of the Beck Depression Inventory (BDI). Fused Graphical Lasso was used to estimate network structures, and mixed graphical models were used to assess network connectivity and symptom centrality. Network community structure was examined using the walktrap-algorithm and minimum spanning trees (MST). Symptom centrality was evaluated with expected influence and participation coefficients. RESULTS Overall connectivity did not differ between networks from participants with and without DD, but more simple community structure was observed among those with DD compared to those without DD. Exploratory analyses revealed small differences between the samples in the order of one centrality estimate participation coefficient. CONCLUSIONS Community structure, but not overall connectivity of the symptom network, may be different for people with DD compared to people without DD. This difference may be of importance when estimating the overall connectivity differences between groups with and without mental disorders.
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Castro D, Ferreira F, de Castro I, Rodrigues AR, Correia M, Ribeiro J, Ferreira TB. The Differential Role of Central and Bridge Symptoms in Deactivating Psychopathological Networks. Front Psychol 2019; 10:2448. [PMID: 31827450 PMCID: PMC6849493 DOI: 10.3389/fpsyg.2019.02448] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 10/15/2019] [Indexed: 12/19/2022] Open
Abstract
The network model of psychopathology suggests that central and bridge symptoms represent promising treatment targets because they may accelerate the deactivation of the network of interactions between the symptoms of mental disorders. However, the evidence confirming this hypothesis is scarce. This study re-analyzed a convenience sample of 51 cross-sectional psychopathological networks published in previous studies addressing diverse mental disorders or clinically relevant problems. In order to address the hypothesis that central and bridge symptoms are valuable treatment targets, this study simulated five distinct attack conditions on the psychopathological networks by deactivating symptoms based on two characteristics of central symptoms (degree and strength), two characteristics of bridge symptoms (overlap and bridgeness), and at random. The differential impact of the characteristics of these symptoms was assessed in terms of the magnitude and the extent of the attack required to achieve a maximum impact on the number of components, average path length, and connectivity. Only moderate evidence was obtained to sustain the hypothesis that central and bridge symptoms constitute preferential treatment targets. The results suggest that the degree, strength, and bridgeness attack conditions are more effective than the random attack condition only in increasing the number of components of the psychopathological networks. The degree attack condition seemed to perform better than the strength, bridgeness, and overlap attack conditions. Overlapping symptoms evidenced limited impact on the psychopathological networks. The need to address the basic mechanisms underlying the structure and dynamics of psychopathological networks through the expansion of the current methodological framework and its consolidation in more robust theories is stressed.
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Affiliation(s)
- Daniel Castro
- Department of Social and Behavioural Sciences, University Institute of Maia, Maia, Portugal
- Center for Psychology at University of Porto, Porto, Portugal
| | - Filipa Ferreira
- Department of Social and Behavioural Sciences, University Institute of Maia, Maia, Portugal
- Center for Psychology at University of Porto, Porto, Portugal
| | - Inês de Castro
- Department of Social and Behavioural Sciences, University Institute of Maia, Maia, Portugal
| | - Ana Rita Rodrigues
- Department of Social and Behavioural Sciences, University Institute of Maia, Maia, Portugal
- Center for Psychology at University of Porto, Porto, Portugal
| | - Marta Correia
- Department of Social and Behavioural Sciences, University Institute of Maia, Maia, Portugal
| | - Josefina Ribeiro
- Department of Social and Behavioural Sciences, University Institute of Maia, Maia, Portugal
| | - Tiago Bento Ferreira
- Department of Social and Behavioural Sciences, University Institute of Maia, Maia, Portugal
- Center for Psychology at University of Porto, Porto, Portugal
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Hoffman M, Steinley D, Trull TJ, Lane SP, Wood PK, Sher KJ. The influence of sample selection on the structure of psychopathology symptom networks: An example with alcohol use disorder. JOURNAL OF ABNORMAL PSYCHOLOGY 2019; 128:473-486. [PMID: 31192638 PMCID: PMC6614010 DOI: 10.1037/abn0000438] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Increasingly, the structure of mental disorders has been studied in the form of a network, characterizing how symptoms or criteria interact with and influence each other. Many studies of psychiatric symptoms and diagnostic criteria employ community or population-based surveys using co-occurrence of the symptoms/criteria to form the networks. However, given the overall low prevalence rates of mental disorders and their symptoms in the general population, most of those surveyed may not exhibit or endorse any symptoms and yet are often included in network analyses. Consequently, because network models are built on associations between symptoms/criteria, much of the observed variability is driven by individuals who are asymptomatic. Using data from the National Epidemiological Survey of Alcohol and Related Conditions (NESARC) Wave 2 and NESARC-III, we explore the effect of these "asymptomatic" observations on the estimated relations among diagnostic criteria of alcohol use disorder to determine the effects of such observations on estimated networks. We do so using the eLasso tool, as well as with traditional measures of correlation between binary variables (the Φ coefficient and odds ratio). We find that when the proportion of asymptomatic individuals are systematically culled from the sample, the estimated pairwise relations are often significantly affected, even changing signs in some cases. Our findings indicate that researchers should carefully consider the population(s) included in their sample and the implications it has on their interpretations of pairwise similarity estimates and resulting generalizability and reproducibility of estimates of network structures. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Boschloo L, Bekhuis E, Weitz ES, Reijnders M, DeRubeis RJ, Dimidjian S, Dunner DL, Dunlop BW, Hegerl U, Hollon SD, Jarrett RB, Kennedy SH, Miranda J, Mohr D, Simons AD, Parker G, Petrak F, Herpertz S, Quilty LC, John Rush A, Segal ZV, Vittengl JR, Schoevers RA, Cuijpers P. The symptom-specific efficacy of antidepressant medication vs. cognitive behavioral therapy in the treatment of depression: results from an individual patient data meta-analysis. World Psychiatry 2019; 18:183-191. [PMID: 31059603 PMCID: PMC6502416 DOI: 10.1002/wps.20630] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
A recent individual patient data meta-analysis showed that antidepressant medication is slightly more efficacious than cognitive behavioral therapy (CBT) in reducing overall depression severity in patients with a DSM-defined depressive disorder. We used an update of that dataset, based on seventeen randomized clinical trials, to examine the comparative efficacy of antidepressant medication vs. CBT in more detail by focusing on individual depressive symptoms as assessed with the 17-item Hamilton Rating Scale for Depression. Five symptoms (i.e., "depressed mood" , "feelings of guilt" , "suicidal thoughts" , "psychic anxiety" and "general somatic symptoms") showed larger improvements in the medication compared to the CBT condition (effect sizes ranging from .13 to .16), whereas no differences were found for the twelve other symptoms. In addition, network estimation techniques revealed that all effects, except that on "depressed mood" , were direct and could not be explained by any of the other direct or indirect treatment effects. Exploratory analyses showed that information about the symptom-specific efficacy could help in identifying those patients who, based on their pre-treatment symptomatology, are likely to benefit more from antidepressant medication than from CBT (effect size of .30) versus those for whom both treatments are likely to be equally efficacious. Overall, our symptom-oriented approach results in a more thorough evaluation of the efficacy of antidepressant medication over CBT and shows potential in "precision psychiatry" .
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Affiliation(s)
- Lynn Boschloo
- Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research InstituteVrije Universiteit AmsterdamAmsterdamThe Netherlands,Department of Psychiatry and Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE)University of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - Ella Bekhuis
- Department of Psychiatry and Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE)University of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - Erica S. Weitz
- Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research InstituteVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Mirjam Reijnders
- Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research InstituteVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | | | - Sona Dimidjian
- Department of Psychology and NeuroscienceUniversity of ColoradoBoulderCOUSA
| | - David L. Dunner
- Center for Anxiety and Depression, Mercer IslandWashingtonWAUSA
| | - Boadie W. Dunlop
- Department of Psychiatry and Behavioral SciencesEmory University School of MedicineAtlantaGAUSA
| | - Ulrich Hegerl
- Department of Psychiatry and PsychotherapyUniversity of LeipzigLeipzigGermany
| | | | - Robin B. Jarrett
- Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | | | - Jeanne Miranda
- Health Services Research Center, Neuropsychiatric InstituteUniversity of CaliforniaLos AngelesCAUSA
| | - David C. Mohr
- Center for Behavioral Intervention Technologies, Feinberg School of MedicineNorthwestern UniversityChicagoILUSA
| | - Anne D. Simons
- Department of PsychologyUniversity of Notre DameNotre DameINUSA
| | - Gordon Parker
- School of PsychiatryUniversity of New South WalesSydneyNSWAustralia
| | - Frank Petrak
- Department of Psychosomatic Medicine and PsychotherapyLWL‐University Clinic Bochum, Ruhr University BochumBochumGermany
| | - Stephan Herpertz
- Department of Psychosomatic Medicine and PsychotherapyLWL‐University Clinic Bochum, Ruhr University BochumBochumGermany
| | - Lena C. Quilty
- Department of PsychiatryUniversity of TorontoTorontoONCanada,Campbell Family Mental Health Research InstituteCentre for Addiction and Mental HealthTorontoONCanada
| | - A. John Rush
- Duke‐National University of Singapore Graduate Medical SchoolSingapore,Department of PsychiatryDuke Medical SchoolDurham, NCUSA,Texas Tech University Health Sciences CenterPermian BasinTXUSA
| | - Zindel V. Segal
- Department of PsychologyUniversity of Toronto ScarboroughTorontoONCanada
| | | | - Robert A. Schoevers
- Department of Psychiatry and Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE)University of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research InstituteVrije Universiteit AmsterdamAmsterdamThe Netherlands
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Fang Y, Wu Z. Advance in Diagnosis of Depressive Disorder. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1180:179-191. [DOI: 10.1007/978-981-32-9271-0_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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Abstract
The General Health Questionnaire-28 is a well-known symptom-based rating scale of mental health. Several studies have investigated its latent structure using confirmatory factor analysis. This study questions this approach on several substantive points, most notably the inability for symptoms to interact using confirmatory factor analysis, and argues for the use of network analysis instead. Network results demonstrate the method's utility to improve our understanding of the rating scales' symptom structure. Insights include a much richer understanding of comorbidity on the General Health Questionnaire-28 and the identification of particularly salient symptoms affecting the network. It yields substantive information of interest to researchers and practitioners alike.
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Network Analysis as an Alternative Approach to Conceptualizing Eating Disorders: Implications for Research and Treatment. Curr Psychiatry Rep 2018; 20:67. [PMID: 30079431 DOI: 10.1007/s11920-018-0930-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PURPOSE OF REVIEW Network analysis (NA) is an emerging methodology that allows for the characterization of maintaining symptoms and pathways among symptoms of mental disorders. The current paper provides background on NA and discusses the relevance of the network approach for the conceptualization of eating disorders (ED). RECENT FINDINGS We review the burgeoning literature conceptualizing ED from a network approach. Overall, these papers find that fear of weight gain and overvaluation of weight and shape are core symptoms in networks of ED pathology. We integrate literature on new advances in network methodology (e.g., within-person NA) and the clinical relevance of these approaches for the ED field (e.g., personalized ED treatment). We also provide several considerations (e.g., replicability, sample size, and node (item) selection) for researchers who are interested in using network science and recommend several emerging "best practices" for NA. Finally, we highlight novel applications of NA, specifically the ability to identify within-person maintaining symptoms, and the potential treatment implications for ED that network methods may hold. Overall, NA is a new methodology that holds significant promise for new treatment development in the ED field.
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Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 4:16-26. [PMID: 29980494 DOI: 10.1016/j.bpsc.2018.05.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 05/10/2018] [Accepted: 05/21/2018] [Indexed: 01/01/2023]
Abstract
BACKGROUND Depression is a complex disorder with large interindividual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains, such as anxiety. A dimensional and symptom-based approach may help refine the characterization of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We use resting-state functional magnetic resonance imaging to assess the brain functional connectivity correlates of a symptom-based clustering of individuals. METHODS We assessed symptoms using the Beck Depression and Beck Anxiety Inventories in individuals with or without a history of depression (N = 1084) and high-dimensional data clustering to form subgroups based on symptom profiles. We compared dynamic and static functional connectivity between subgroups in a subset of the total sample (n = 252). RESULTS We identified five subgroups with distinct symptom profiles, which cut across diagnostic boundaries with different total severity, symptom patterns, and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroups 1, 2, and 3, respectively. The distribution of individuals was 32%, 25%, 22%, 10%, and 11% in subgroups 1 to 5, respectively. These subgroups showed evidence of differential static brain-connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity, or global connectivity. CONCLUSIONS Adding to the pursuit of individual-based treatment, subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct patterns of static functional connectivity in the brain.
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van Loo HM, Van Borkulo CD, Peterson RE, Fried EI, Aggen SH, Borsboom D, Kendler KS. Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk. J Affect Disord 2018; 227:313-322. [PMID: 29132074 PMCID: PMC5815316 DOI: 10.1016/j.jad.2017.10.038] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 10/18/2017] [Accepted: 10/21/2017] [Indexed: 12/13/2022]
Abstract
BACKGROUND Genetic risk and environmental adversity-both important risk factors for major depression (MD)-are thought to differentially impact on depressive symptom types and associations. Does heterogeneity in these risk factors result in different depressive symptom networks in patients with MD? METHODS A clinical sample of 5784 Han Chinese women with recurrent MD were interviewed about their depressive symptoms during their lifetime worst episode of MD. The cases were classified into subgroups based on their genetic risk for MD (family history, polygenic risk score, early age at onset) and severe adversity (childhood sexual abuse, stressful life events). Differences in MD symptom network structure were statistically examined for these subgroups using permutation-based network comparison tests. RESULTS Although significant differences in symptom endorsement rates were seen in 18.8% of group comparisons, associations between depressive symptoms were similar across the different subgroups of genetic and environmental risk. Network comparison tests showed no significant differences in network strength, structure, or specific edges (P-value > 0.05) and correlations between edges were strong (0.60-0.71). LIMITATIONS This study analyzed depressive symptoms retrospectively reported by severely depressed women using novel statistical methods. Future studies are warranted to investigate whether similar findings hold in prospective longitudinal data, less severely depressed patients, and men. CONCLUSIONS Similar depressive symptom networks for MD patients with a higher or lower genetic or environmental risk suggest that differences in these etiological influences may produce similar symptom networks downstream for severely depressed women.
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Affiliation(s)
- H M van Loo
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen The Netherlands.
| | - C D Van Borkulo
- Department of Psychology, University of Amsterdam, The Netherlands
| | - R E Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - E I Fried
- Department of Psychology, University of Amsterdam, The Netherlands
| | - S H Aggen
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - D Borsboom
- Department of Psychology, University of Amsterdam, The Netherlands
| | - K S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
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