The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research

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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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All for One and One for All: Mental Disorders in One Dimension

Terrie E Moffitt, Avshalom Caspi (2018)

In both child and adult psychiatry, empirical evidence has now accrued to suggest that a single dimension is able to measure a person's liability to mental disorder, comorbidity among disorders, persistence of disorders over time, and severity of symptoms. This single dimension of general psychopathology has been termed "p," because it conceptually parallels a dimension already familiar to behavioral scientists and clinicians: the "g" factor of general intelligence. As the g dimension reflects low to high mental ability, the p dimension represents low to high psychopathology severity, with thought disorder at the extreme. The dimension of p unites all disorders. It influences present/absent status on hundreds of psychiatric symptoms, which modern nosological systems typically aggregate into dozens of distinct diagnoses, which in turn aggregate into three overarching domains, namely, the externalizing, internalizing, and psychotic experience domains, which finally aggregate into one dimension of psychopathology from low to high: p. Studies show that the higher a person scores on p, the worse that person fares on measures of family history of psychiatric illness, brain function, childhood developmental history, and adult life impairment. A dimension of p may help account for ubiquitous nonspecificity in psychiatry: multiple disorders share the same risk factors and biomarkers and often respond to the same therapies. Here, the authors summarize the history of the unidimensional idea, review modern research into p, demystify statistical models, articulate some implications of p for prevention and clinical practice, and outline a transdiagnostic research agenda. [AJP AT 175: Remembering Our Past As We Envision Our Future October 1910: A Study of Association in Insanity Grace Helen Kent and A.J. Rosanoff: "No sharp distinction can be drawn between mental health and mental disease; a large collection of material shows a gradual and not an abrupt transition from the normal state to pathological states."(Am J Psychiatry 1910; 67(2):317-390 )].

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A new method for constructing networks from binary data

Claudia D. van Borkulo, Denny Borsboom, Sacha Epskamp … (2014)

Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

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The 52 symptoms of major depression: Lack of content overlap among seven common depression scales.

Eiko I Fried (2017)

Depression severity is assessed in numerous research disciplines, ranging from the social sciences to genetics, and used as a dependent variable, predictor, covariate, or to enroll participants. The routine practice is to assess depression severity with one particular depression scale, and draw conclusions about depression in general, relying on the assumption that scales are interchangeable measures of depression. The present paper investigates to which degree 7 common depression scales differ in their item content and generalizability.