Examining the associations between PTSD symptoms and aspects of emotion dysregulation through network analysis

Haws, J. Kyle; Brockdorf, Alexandra N.; Gratz, Kim L.; Messman, Terri L.; Tull, Matthew T.; DiLillo, David

doi:10.1016/j.janxdis.2022.102536

Cited by 18 publications

(20 citation statements)

References 109 publications

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“…In the network theory of psychopathology, the variables dynamically interact with each other and mutually reinforce to produce a complex network, thus causing psychiatric disorders (69,70). In the network, the various variables (i.e., symptoms, cognitive processes, and traits) are regarded as nodes; the relations (e.g., partial correlations) between observed variables are represented by node-node interactions (defined as edges) (69,71). This approach also makes it possible to identify bridge nodes, which are nodes that strongly connect two communities (71)(72)(73).…”

Section: Introductionmentioning

confidence: 99%

“…In the network, the various variables (i.e., symptoms, cognitive processes, and traits) are regarded as nodes; the relations (e.g., partial correlations) between observed variables are represented by node-node interactions (defined as edges) (69,71). This approach also makes it possible to identify bridge nodes, which are nodes that strongly connect two communities (71)(72)(73). The term "community" is used to represent variables grouped together on the basis of psychological theory (73).…”

Section: Introductionmentioning

confidence: 99%

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Applying network analysis to understand the relationships between impulsivity and social media addiction and between impulsivity and problematic smartphone use

et al. 2022

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BackgroundPrior studies have revealed the relationships between impulsivity and social media addiction (SMA) and between impulsivity and problematic smartphone use (PSU) based on total scores on standardized self-report scales. However, there has been a lack of studies exploring how the dimensions of impulsivity and components of SMA or PSU are interrelated. The present study aimed to investigate the structural relationships between the dimensions of impulsivity and components of SMA and PSU and determine the critical bridge node using network analysis.MethodsA total of 325 healthy adults aged 18–36 years participated in the study. SMA and PSU were assessed using the Bergen Social Media Addiction Scale (BSMAS) and Smartphone Application-Based Addiction Scale (SABAS), respectively. Impulsivity was measured by the Barratt Impulsiveness Scale Version 11 (BIS-11). Network analysis was used to construct an SMA-Impulsivity network and a PSU-Impulsivity network. Bridge centrality (bridge expected influence, BEI) was estimated to identify influential bridge nodes.ResultsIn addition to relationships within each community, network analysis revealed that the dimensions of impulsivity were closely associated with the components of SMA and PSU. Particularly, I2 “motor impulsivity” had a relatively strong connection with SMA3 “mood modification” and SMA4 “relapse” in the SMA-Impulsivity network, and with PSU2 “conflict” and PSU5 “withdrawal” in the PSU-Impulsivity network. Moreover, I2 “motor impulsivity” was identified as the most critical bridge node in both networks.ConclusionThese findings demonstrate potential pathways between different dimensions of impulsivity and the components of SMA and PSU, providing new evidence relevant to understanding the underlying mechanisms that account for how highly impulsive individuals develop SMA and PSU, and highlight the critical bridge node—motor impulsivity—that may be a promising and effective target for the prevention and treatment of SMA and PSU.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying network analysis to understand the relationships between impulsivity and social media addiction and between impulsivity and problematic smartphone use

et al. 2022

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“…From the perspective of the network theoretical model, psychological constructs are characterized as networks emerging from the interactions between different variables ( 60 , 62 ). Variables, whether symptoms or non-symptoms, are regarded as the nodes in the psychopathology network; the partial correlations between different variables are represented as node-to-node edges ( 62 , 63 ).…”

Section: Introductionmentioning

confidence: 99%

“…Network analysis provides further understanding of the mechanisms underlying the development and maintenance of disorders ( 62 , 64 ). This approach also makes it possible to identify central nodes that greatly affect other nodes across the whole network, as well as bridge nodes, which connect with nodes of other network communities ( 63 – 65 ). The term “community” is used to represent a group of nodes corresponding to a specific construct based on psychological theory or clinical criteria rather than based on any network analytical methods such as community detection ( 65 , 66 ).…”

Section: Introductionmentioning

confidence: 99%

“…The term “community” is used to represent a group of nodes corresponding to a specific construct based on psychological theory or clinical criteria rather than based on any network analytical methods such as community detection ( 65 , 66 ). Because central nodes have the greatest influence on the overall network and bridge nodes are critical to maintaining the co-occurrence of mental disorders and facilitating the contagion of one disorder to another, these nodes have been identified as more effective targets for prevention and treatment than other nodes ( 53 , 63 – 65 ). Network analysis has been used to investigate three or more co-occurring constructs in a joint framework, including problematic Internet and smartphone use, primary emotional systems, and need satisfaction ( 67 ), as well as depressive symptoms, parental stress, and mechanistic variables ( 68 ).…”

Section: Introductionmentioning

confidence: 99%

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The roles of behavioral inhibition/activation systems and impulsivity in problematic smartphone use: A network analysis

Guo

Yang

et al. 2022

Front. Public Health

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BackgroundBehavioral inhibition/activation systems (BIS/BAS) and impulsivity are associated with problematic smartphone use (PSU). However, no studies to date have explored how the subdomains of BIS/BAS and the dimensions of impulsivity relate to the components of PSU in a joint framework. This study aimed to examine the relationships between the three constructs at a fine-grained level and identify the central nodes and bridge nodes of their relationships using network analysis.MethodsA regularized partial correlation network of PSU, BIS/BAS, and impulsivity communities was estimated to investigate the connections between variables and determine the expected influence and bridge expected influence for each variable based on data from 325 Chinese adults. PSU, BIS/BAS, and impulsivity were assessed using the Smartphone Application-Based Addiction Scale (SABAS), BIS/BAS scales, and Barratt Impulsiveness Scale-Version 11 (BIS-11), respectively.ResultsIn addition to connections within each community, network analysis revealed that there were connections between different communities, especially connections to PSU. I2 “motor impulsivity” was strongly associated with PSU2 “conflict” and PSU6 “relapse”; BASR “BAS-reward responsiveness” was strongly associated with PSU5 “withdrawal.” Nodes BASR “BAS-reward responsiveness” and PSU6 “relapse” were the most central variables, while nodes BASR “BAS-reward responsiveness” and I2 “motor impulsivity” were the strongest bridge variables.ConclusionThe connections between the subdomains of BIS/BAS and the components of PSU and between the dimensions of impulsivity and the components of PSU may be particularly important in the development and maintenance of PSU. The central variables identified here, along with the bridge variables, could be promising and effective targets for the prevention and intervention of PSU.

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Symptom structure of posttraumatic stress disorder in workplace trauma: A “distraction‐avoidance” pattern

Zhu,

Eli,

Chen

et al. 2023

J Clin Psychol

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BackgroundIndividuals who experience a workplace trauma are at a high risk of developing posttraumatic stress disorder (PTSD). Most of these people return to work soon after the trauma (i.e., experience early‐stage natural exposure). This study aims to explore the response pattern of workers who have experienced a workplace trauma and early‐stage natural exposure through network analysis and provide insights into the potential impact of early‐stage natural exposure.MethodsOur study included 278 workers directly experiencing the workplace explosion in the Tianjin Economic and Technological Development Zone. A partial correlation network analysis was used to explore the PTSD symptoms relationship and identify central symptoms.ResultsThe results suggest that emotional numbness, difficulty in concentration, re‐experiencing symptoms, and avoidance of thoughts are the most central symptoms, reflecting a “distraction‐avoidance” pattern.ConclusionsThe current study found that workers who experienced workplace trauma exhibited a “distraction‐avoidance” pattern, which helps deepen our understanding of the PTSD network and leads to some suggestions on intervention measures.

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Examining the associations between PTSD symptoms and aspects of emotion dysregulation through network analysis

Cited by 18 publications

References 109 publications

Applying network analysis to understand the relationships between impulsivity and social media addiction and between impulsivity and problematic smartphone use

Applying network analysis to understand the relationships between impulsivity and social media addiction and between impulsivity and problematic smartphone use

The roles of behavioral inhibition/activation systems and impulsivity in problematic smartphone use: A network analysis

Symptom structure of posttraumatic stress disorder in workplace trauma: A “distraction‐avoidance” pattern

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