The Small World of Psychopathology

Background: Mental disorders are highly comorbid: people having one disorder are likely to have another as well. We explain empirical comorbidity patterns based on a network model of psychiatric symptoms, derived from an analysis of symptom overlap in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV). Principal Findings: We show that a) half of the symptoms in the DSM-IV network are connected, b) the architecture of these connections conforms to a small world structure, featuring a high degree of clustering but a short average path length, and c) distances between disorders in this structure predict empirical comorbidity rates. Network simulations of Major Depressive Episode and Generalized Anxiety Disorder show that the model faithfully reproduces empirical population statistics for these disorders. Conclusions: In the network model, mental disorders are inherently complex. This explains the limited successes of genetic, neuroscientific, and etiological approaches to unravel their causes. We outline a psychosystems approach to investigate the structure and dynamics of mental disorders

Major depression as a complex dynamic system

In this paper, we characterize major depression (MD) as a complex dynamical system in which symptoms (e.g., insomnia and fatigue) are directly connected to one another in a network structure. We hypothesize that individuals can be characterized by their own network with unique architecture and resulting dynamics. With respect to architecture, we show that individuals vulnerable to developing MD are those with strong connections between symptoms: e.g., only one night of poor sleep suffices to make a particular person feel tired. Such vulnerable networks, when pushed by forces external to the system such as stress, are more likely to end up in a depressed state; whereas networks with weaker connections tend to remain in or return to a healthy state. We show this with a simulation in which we model the probability of a symptom becoming active as a logistic function of the activity of its neighboring symptoms. Additionally, we show that this model potentially explains some well-known empirical phenomena such as spontaneous recovery as well as accommodates existing theories about the various subtypes of MD. To our knowledge, we offer the first intra-individual, symptom-based, process model with the potential to explain the pathogenesis and maintenance of major depression.Comment: 8 figure

Количественная оценка рисков безопасности информации на основе пробит-анализа

Приведено обоснование постановки задачи развития методологии количественной оценки рисков безопасности информации конкретных объектов информационной деятельности на основе пробит-анализа.Наведено обґрунтування постановки задачі розвитку методології оцінки ризиків безпеки інформації конкретних об’єктів інформаційної діяльності на основі пробіт-аналізу.The substantiation of problem statement of information security risks assessment methodology development for specific information activity objects based on probit-analysis is given

Deconstructing the construct: a network perspective on psychological phenomena. New Ideas Psychol. 31, 43–53. doi: 10.1016/j.newideapsych.2011.02.007

a b s t r a c t In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, in which the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measured attribute is seen as the common effect of the observables. We advocate a third interpretation, in which attributes are conceptualized as systems of causally coupled (observable) variables. In such a view, a construct like 'depression' is not seen as a latent variable that underlies symptoms like 'lack of sleep' or 'fatigue', and neither as a composite constructed out of these symptoms, but as a system of causal relations between the symptoms themselves (e.g., lack of sleep / fatigue, etc.). We discuss methodological strategies to investigate such systems as well as theoretical consequences that bear on the question in which sense such a construct could be interpreted as real. Ó 2011 Elsevier Ltd. All rights reserved. Current theorizing and research in psychology is dominated by two conceptualizations of the relationship between psychological attributes (e.g., 'neuroticism') and observable variables (e.g., 'worries about things going wrong '; In the present paper, we argue that the dichotomy of reflective/formative models does not exhaust the possibilities that can be used to connect psychological attributes and observable variables. We advocate an alternative conceptualization, in which psychological attributes are conceptualized as networks of directly related observables. We discuss the possibilities that this addition to the psychometric arsenal offers, the inferential techniques that it allows for, and the consequences it has for the ontology of psychopathological constructs and the epistemic status of validation strategies. The structure of this paper is as follows. First, we discuss the ideas that underlie reflective and formative models. Second, we highlight important problems that the models face. Third, we discuss the network approach. Fourth, we touch on the ramifications that this approach has in the context of validity theory. 1. Reflective and formative models Reflective models In reflective models, observed indicators (e.g., item or subtest scores) are modeled as a function of a common latent variable (i.e., unobserved) and item-specific erro

Deconstructing the construct: a network perspective on psychological phenomena. New Ideas Psychol. 31, 43–53. doi: 10.1016/j.newideapsych.2011.02.007

a b s t r a c t In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, in which the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measured attribute is seen as the common effect of the observables. We advocate a third interpretation, in which attributes are conceptualized as systems of causally coupled (observable) variables. In such a view, a construct like 'depression' is not seen as a latent variable that underlies symptoms like 'lack of sleep' or 'fatigue', and neither as a composite constructed out of these symptoms, but as a system of causal relations between the symptoms themselves (e.g., lack of sleep / fatigue, etc.). We discuss methodological strategies to investigate such systems as well as theoretical consequences that bear on the question in which sense such a construct could be interpreted as real. Ó 2011 Elsevier Ltd. All rights reserved. Current theorizing and research in psychology is dominated by two conceptualizations of the relationship between psychological attributes (e.g., 'neuroticism') and observable variables (e.g., 'worries about things going wrong '; In the present paper, we argue that the dichotomy of reflective/formative models does not exhaust the possibilities that can be used to connect psychological attributes and observable variables. We advocate an alternative conceptualization, in which psychological attributes are conceptualized as networks of directly related observables. We discuss the possibilities that this addition to the psychometric arsenal offers, the inferential techniques that it allows for, and the consequences it has for the ontology of psychopathological constructs and the epistemic status of validation strategies. The structure of this paper is as follows. First, we discuss the ideas that underlie reflective and formative models. Second, we highlight important problems that the models face. Third, we discuss the network approach. Fourth, we touch on the ramifications that this approach has in the context of validity theory. 1. Reflective and formative models Reflective models In reflective models, observed indicators (e.g., item or subtest scores) are modeled as a function of a common latent variable (i.e., unobserved) and item-specific erro

Densities of simulation results of original vs random parameter values.

<p>Top to bottom: Prevalence of MDE, Prevalence of GAD, Odds ratio, Cronbach' alpha. Densities of networks resulting from original (random) parameter values are shown in blue (red).</p

The simulation of MDE and GAD and its results.

<p>The left part of the figure shows core MDE (<i>blue nodes</i>), core GAD (<i>red nodes</i>) and bridge symptoms (<i>purple nodes</i>). The middle part of the figure represents the implied structure of the simulated network: comorbidity arises through connections via bridge symptoms. There are no direct connections between core MDE and core GAD symptoms. The right part of the figure displays the results of the simulations. The x-axis represents the number of replications of the simulation. The y-axis represents 1) <i>odds</i>: odds ratio of diagnoses as measure of comorbidity, 2) <i>alpha</i>: Cronbach's α, 3) <i>MDE</i>: prevalence of MDE and 4) <i>GAD</i>: prevalence of GAD.</p