Evidence map›Paper›PMID 42178405›Full record

ArticlePsychopharmacology2026

Dissociation between the dynamics of symptom scores and network metrics in psychosis treatment: a proof-of-concept study using network intervention analysis.

Pierfrancesco Sarti, Giacomo Cecere, Wolfgang Omlor, Victoria Edkins, Johanna M C Blom, Philipp Homan

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Article in Psychopharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Pierfrancesco Sarti *Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Giacomo Cecere *Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland.
Wolfgang OmlorDepartment of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland.
Victoria EdkinsDepartment of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland.
Johanna M C BlomDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Philipp HomanDepartment of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland. philipp.homan@bli.uzh.ch.

Funding

Brain and Behavior Research Foundation 28445
6 · The paper itself

Abstract

rationaleAntipsychotic efficacy in schizophrenia spectrum disorders (SSD) is commonly evaluated using static measures that fail to capture the dynamic evolution of symptoms and the unfolding impact of treatment over time. Network intervention analysis (NIA) is a novel approach that models pharmacological treatments as active nodes within longitudinal symptom networks.

objectivesThis proof-of-concept study aimed to contrast the information about antipsychotic-symptom interactions provided using NIA with that using a simple time-based analysis of symptom progression. We hypothesised that while overall symptom improvement would be similar across pharmacological profiles, the symptom decoupling patterns (connection density) extracted from NIA would differ significantly. We also hypothesised that the rate of change for the decoupling would be significantly dissociated from the rate of change for the clinical symptoms.

methodsPatients with SSD underwent weekly symptom evaluation over six weeks. NIA was used to characterise the evolving impact of treatment with three distinct drug classes: muscarinic, serotonergic/dopaminergic, and adrenergic/low dopaminergic. Standardised slopes from NIA density regression models and symptom mixed-effect models were directly compared using a robust non-parametric bootstrap procedure.

resultsNIA density analysis showed a significant time*group interaction, (p = 0.016) which was not observed in symptom severity trajectories. The slope for density reduction was steeper than the slope for symptom severity reduction (Δβ̂=0.426, p = 0.002). For each receptor profile class, the treatment node within the NIA demonstrated distinct patterns of association with symptoms.

conclusionsThese findings highlight the potential of NIA to capture the evolving interaction between antipsychotic classes and dynamic symptom trajectories in SSD.

Indexed as

AntipsychoticsNetwork analysisPrecision medicineReceptor affinity profilesSchizophrenia spectrum disordersSymptom dynamics

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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.