Evidence map›Paper›PMID 33040734›Full record

ArticleBMC medicine2020

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology.

Tobias R Spiller, Ofir Levi, Yuval Neria, Benjamin Suarez-Jimenez, Yair Bar-Haim, Amit Lazarov

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 1 pooled it
9.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 1 synthesis or guideline pooled it, 105 citations in OpenAlex.

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  15. From childhood trauma to complex PTSD: a network analysis.European journal of psychotraumatology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 5 institutions in 3 countries.

Tobias R SpillerDepartment of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich, Zurich, Switzerland. tobiasraphael.spiller@uzh.ch.
Ofir LeviDivision of Mental Health, Medical Corps, Israel Defense Forces, Tel Aviv, Israel.
Yuval NeriaDepartments of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
Benjamin Suarez-JimenezDepartments of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
Yair Bar-HaimSchool of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.
Amit LazarovDepartments of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
Columbia University Irving Medical Center · USIsrael Defense Forces Medical Corps · ILNew York Psychoanalytic Society and Institute · USTel Aviv University · ILUniversity Hospital of Zurich · CH

Funding

Forschungskredit FK-19-048
6 · The paper itself

Abstract

backgroundIn the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

methodsHere, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

resultsOf the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

conclusionsThe centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

Indexed as

Social Network AnalysisCausalityCross-Sectional StudiesFemaleHumansMalePsychopathologyReproducibility of ResultsCentrality measuresNetwork analysisNetwork approachPosttraumatic stress disorderTreatment

Identifiers

PMID33040734
PMCPMC7549218
OpenAlexW3092266760

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.