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Cited by 367Mental Health Research TopicsAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesTreatment of Major Depression

The DOI matches its Crossref record; 125 of the 126 references it cites were found in a registry or an index.

Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward

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Fried, Eiko I.·2015·Frontiers in Psychology
DOI 10.3389/fpsyg.2015.00309PMC PMC4369644
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Abstract

Major depression (MD) is a highly heterogeneous diagnostic category. Diverse symptoms such as sad mood, anhedonia, and fatigue are routinely added to an unweighted sum-score, and cutoffs are used to distinguish between depressed participants and healthy controls. Researchers then investigate outcome variables like MD risk factors, biomarkers, and treatment response in such samples. These practices presuppose that (1) depression is a discrete condition, and that (2) symptoms are interchangeable indicators of this latent disorder. Here I review these two assumptions, elucidate their historical roots, show how deeply engrained they are in psychological and psychiatric research, and document that they contrast with evidence. Depression is not a consistent syndrome with clearly demarcated boundaries, and depression symptoms are not interchangeable indicators of an underlying disorder. Current research practices lump individuals with very different problems into one category, which has contributed to the remarkably slow progress in key research domains such as the development of efficacious antidepressants or the identification of biomarkers for depression. The recently proposed network framework offers an alternative to the problematic assumptions. MD is not understood as a distinct condition, but as heterogeneous symptom cluster that substantially overlaps with other syndromes such as anxiety disorders. MD is not framed as an underlying disease with a number of equivalent indicators, but as a network of symptoms that have direct causal influence on each other: insomnia can cause fatigue which then triggers concentration and psychomotor problems. This approach offers new opportunities for constructing an empirically based classification system and has broad implications for future research.

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FIGURE 1 Visualization of a reflective latent variable model. D indicates the latent disorder depression that is modeled as common cause of the observable symptoms s1–s14.
FIGURE 1 Visualization of a reflective latent variable model. D indicates the latent disorder depression that is modeled as common cause of the observable symptoms s1–s14.CC BY · PubMed Central
FIGURE 2 Visualization of a longitudinal psychopathological symptom network. Longitudinal network model of the directed associations between depression symptoms s1–s14 (not based on data). Self-loops represent autoregressive effects, edges represent associations among symptoms across time, and line-thickness indicates strength of the associations. Symptoms with a large number of connections are displayed in the center, symptoms with few or weak connections in the periphery.
FIGURE 2 Visualization of a longitudinal psychopathological symptom network. Longitudinal network model of the directed associations between depression symptoms s1–s14 (not based on data). Self-loops represent autoregressive effects, edges represent associations among symptoms across time, and line-thickness indicates strength of the associations. Symptoms with a large number of connections are displayed in the center, symptoms with few or weak connections in the periphery.CC BY · PubMed Central

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Cites

Research works printed but not linked: 6

  1. Brooks, 2013, Heroes of uncertainty., New York Times.
  2. Ereshefsky, 2007, Species, The Stanford Encyclopedia of Philosophy
  3. Grebb, 2009, Introduction and considerations for a brain-based diagnostic system in psychiatry, Comprehensive Textbook of Psychiatry, 1
  4. Hooper, 2008, Structural equation modeling: guidelines for determining model fit., Electron. J. Bus. Res. Methods, 6, 53
  5. Insel, 2013, Transforming Diagnosis.
  6. Kupfer, 2013, Field Trial Results Guide DSM Recommendations. Huffingt. Post.

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Metadata

Authors
Fried, E. I.
Affiliations
KU Leuven (BE)
Corresponding author
Fried, Eiko I.
ORCID
Fried, Eiko I.
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2015-03-23
Volume
6
Pages
309-309
ISSN
1664-1078 (ISSN-L)
DOI
10.3389/fpsyg.2015.00309
PMC
PMC4369644
OpenAlex
W2168432043
Cited by
367
References
126
Citations by year
2026: 25 · 2025: 32 · 2024: 42 · 2023: 57 · 2022: 39 · 2021: 37 · 2020: 35 · 2019: 24 · 2018: 21 · 2017: 22 · 2016: 25 · 2015: 8
FWCI
3.27
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Psychology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Lirias (KU Leuven) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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
  • Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
Keywords
major depressive disorder, diagnostic heterogeneity, symptom clusters, anhedonia, fatigue, insomnia, biomarkers, antidepressant drug development, anxiety disorders, psychiatric classification, research assumptions

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