The network-based approach to mood and anxiety disorder phenotypes assumes dynamic interactions between symptoms, neurocognitive factors such as attributional styles, and psychosocial and biological factors. For different types of datasets from which the phenotype network is to be estimated, a Gaussian graphical model, an Ising model, a directed acyclic graph, or an intra-individual covariance network could be used. Accordingly, these network-based approaches for understanding mood and anxiety-related psychological phenomena have been helpful in a quantitative and pictorial understanding of qualitative dynamics among diverse psychological phenomena as well as mind-environment interactions. For patients diagnosed with mood or anxiety disorders, as well as those who suffer from depressive or anxiety symptoms in the community, a phenotype network framework might help uncover precipitating factors of their depressive and anxiety symptoms. Furthermore, this approach may better identify the factors mediating the appearance of depressive and anxiety symptoms that can propagate in the aftermath of stressors and/or from other underlying psychopathology. Additionally, this phenotype network paradigm can also be applied to identify possible novel targets for more effective therapeutic interventions for these clinical and community populations.
The goal of this Research Topic is to better understand the interacting patterns among the psychological symptoms including (but not limited to) mood state and anxiety level, cognitive style, and environmental factors using the framework of a phenotype network. We aim to decipher the interacting patterns among mood and/or anxiety symptoms and stressors, associated factors of suicidality and subjective well-being, and any stress resilience-related factors, using the phenotype network approach. Additionally, longitudinal comparisons of phenotype network characteristics before/after treatment are highly relevant.
We encourage Original Research (both cross-sectional and longitudinal designs), Systematic Review, Methods, and Perspective articles on (but not limited to) the following topics:
• Interacting patterns among psychological symptoms including (but not limited to) mood states and anxiety levels in community or clinical populations
• Longitudinal comparison of phenotype network characteristics before/after treatments (ex. pharmacotherapy, cognitive behavioral therapy, brain stimulation, among others) for patients diagnosed with mood and/or anxiety disorder(s)
• Associated factors of suicidality in community or clinical populations
• Associated factors of subjective well-being or life satisfaction in community or clinical populations
• Interacting patterns between stressors (ex. childhood adversity, verbal abuse, workplace violence, intimate partner violence, natural disaster, among others) and psychological symptoms including (but surely not limited to) mood state and anxiety level
• Deciphering stress resilience factors after exposure to stressors or distressing environmental factors (ex. neurocognitive test results, cognitive attributional style, behavioral traits such as exercise, among others) by comparing subgroups with mood or anxiety disorder(s) versus those who do not satisfy the diagnostic criteria of mood or anxiety disorder(s).
The network-based approach to mood and anxiety disorder phenotypes assumes dynamic interactions between symptoms, neurocognitive factors such as attributional styles, and psychosocial and biological factors. For different types of datasets from which the phenotype network is to be estimated, a Gaussian graphical model, an Ising model, a directed acyclic graph, or an intra-individual covariance network could be used. Accordingly, these network-based approaches for understanding mood and anxiety-related psychological phenomena have been helpful in a quantitative and pictorial understanding of qualitative dynamics among diverse psychological phenomena as well as mind-environment interactions. For patients diagnosed with mood or anxiety disorders, as well as those who suffer from depressive or anxiety symptoms in the community, a phenotype network framework might help uncover precipitating factors of their depressive and anxiety symptoms. Furthermore, this approach may better identify the factors mediating the appearance of depressive and anxiety symptoms that can propagate in the aftermath of stressors and/or from other underlying psychopathology. Additionally, this phenotype network paradigm can also be applied to identify possible novel targets for more effective therapeutic interventions for these clinical and community populations.
The goal of this Research Topic is to better understand the interacting patterns among the psychological symptoms including (but not limited to) mood state and anxiety level, cognitive style, and environmental factors using the framework of a phenotype network. We aim to decipher the interacting patterns among mood and/or anxiety symptoms and stressors, associated factors of suicidality and subjective well-being, and any stress resilience-related factors, using the phenotype network approach. Additionally, longitudinal comparisons of phenotype network characteristics before/after treatment are highly relevant.
We encourage Original Research (both cross-sectional and longitudinal designs), Systematic Review, Methods, and Perspective articles on (but not limited to) the following topics:
• Interacting patterns among psychological symptoms including (but not limited to) mood states and anxiety levels in community or clinical populations
• Longitudinal comparison of phenotype network characteristics before/after treatments (ex. pharmacotherapy, cognitive behavioral therapy, brain stimulation, among others) for patients diagnosed with mood and/or anxiety disorder(s)
• Associated factors of suicidality in community or clinical populations
• Associated factors of subjective well-being or life satisfaction in community or clinical populations
• Interacting patterns between stressors (ex. childhood adversity, verbal abuse, workplace violence, intimate partner violence, natural disaster, among others) and psychological symptoms including (but surely not limited to) mood state and anxiety level
• Deciphering stress resilience factors after exposure to stressors or distressing environmental factors (ex. neurocognitive test results, cognitive attributional style, behavioral traits such as exercise, among others) by comparing subgroups with mood or anxiety disorder(s) versus those who do not satisfy the diagnostic criteria of mood or anxiety disorder(s).
