Can network analysis transform psychopathology?
Abstract
Experimental psychopathology has been the primary path to gaining causal knowledge about variables maintaining mental disorders. Yet a radically different approach to conceptualizing psychopathology promises to advance our understanding, thereby complementing traditional laboratory experiments. In contrast to viewing symptoms as reflective of underlying, latent categories or dimensions, network analysis conceptualizes symptoms as constitutive of mental disorders, not reflective of them. Disorders emerge from the causal interactions among symptoms themselves, and intervening on central symptoms in disorder networks promises to foster rapid recovery. One purpose of this article is to contrast network analysis with traditional approaches, and consider its strengths and limitations. A second purpose is to review novel computational methods that may enable researchers to discern the causal structure of disorders (e.g., Bayesian networks). I close by sketching exciting new developments in methods that have direct implications for treatment.
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Network analysis allows examination of complex relations among multiple variables simultaneously.
“Thus, it allows to examine complex relations among multiple variables, simultaneously.”
From Psychometric properties of the engaged living scale (ELS) Spanish version in a large sample of Spanish pilgrims (2023) · cites this paper as McNally, 2016
Network analysis is a set of integrated statistical techniques that can simultaneously analyze many variables and investigate their role as outcome and mediator at the same time.
“Network analysis is a set of integrated statistical techniques that can simultaneously analyze many variables and investigate their role as outcome and mediator at the same time (McNally, 2016).”
From Psychometric properties of the engaged living scale (ELS) Spanish version in a large sample of Spanish pilgrims (2023) · cites this paper as McNally, 2016
It is possible to study a network structure at various time points and determine changes following an intervention when using longitudinal data.
“Moreover, it is possible to study a network structure at various time points and determine the changes following an intervention when using longitudinal data (Epskamp et al., 2018; McNally, 2016).”
From Psychometric properties of the engaged living scale (ELS) Spanish version in a large sample of Spanish pilgrims (2023) · cites this paper as McNally, 2016
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- Authors
- McNally, R. J.
- Affiliations
- Harvard University (US)
- Corresponding author
- McNally, Richard J.
- ORCID
- McNally, Richard J.
- Venue
- Behaviour Research and Therapy
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 86
- Pages
- 95-104
- ISSN
- 0005-7967 (ISSN-L)
- OpenAlex
- W2469324137
- Cited by
- 1,072
- References
- 95
- Citations by year
- 2026: 140 · 2025: 181 · 2024: 147 · 2023: 139 · 2022: 129 · 2021: 95 · 2020: 87 · 2019: 73 · 2018: 41 · 2017: 34 · 2016: 5
- FWCI
- 6.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Behaviour Research and Therapy (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Clinical Psychology › Personality Disorders and Psychopathology
- Keywords
- network analysis, psychopathology, symptom networks, Bayesian networks, computational psychiatry, symptomatic treatment, experimental psychopathology
- MeSH terms
- Bayes Theorem, Humans, Psychopathology, Models, Statistical, Behavioral Research
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