Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Brain Networks Reveal the Effects of Antipsychotic Drugs on Schizophrenia Patients and Controls

Bibliographic Data

ID15516763
AuthorsEmma K Towlson (Massachusetts Institute of Technology, corresponding author), Petra E Vértes (0000-0002-0992-3210, University of Cambridge), Ulrich Müller (0000-0001-6070-7663, Barnet Hospital), Ulrich Müller-Sedgwick (0000-0002-5499-4077), Sebastian E Ahnert (0000-0003-2613-0041, Sainsbury Laboratory)
Year2019
Volume10
Pages611-611
Publication date2019-09-12
Open AccessYes
TypePREPRINT
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2019.00611
PMID31572229
OpenAlexW2807557438
LanguageEN
Citations received1
References cited53

The study of brain networks, including those derived from functional neuroimaging data, attracts a broad interest and represents a rapidly growing interdisciplinary field. Comparing networks of healthy volunteers with those of patients can potentially offer new, quantitative diagnostic methods and a framework for better understanding brain and mind disorders. We explore resting state functional Magnetic Resonance Imaging (fMRI) data through network measures. We construct networks representing 15 healthy individuals and 12 schizophrenia patients (males and females), all of whom are administered three drug treatments: i) a placebo; and two antipsychotic medications ii) aripiprazole and iii) sulpiride. We compare these resting state networks to a performance at an "N-back" working memory task. We demonstrate that not only is there a distinctive network architecture in the healthy brain that is disrupted in schizophrenia but also that both networks respond to antipsychotic medication. We first reproduce the established finding that brain networks of schizophrenia patients exhibit increased efficiency and reduced clustering compared with controls. Our data then reveal that the antipsychotic medications mitigate this effect, shifting the metrics toward those observed in healthy volunteers, with a marked difference in efficacy between the two drugs. Additionally, we find that aripiprazole considerably alters the network statistics of healthy controls. Examining the "N-back" working memory task, we establish that aripiprazole also adversely affects their performance. This suggests that changes to macroscopic brain network architecture result in measurable behavioral differences. This is one of the first studies to directly compare different medications using a whole-brain graph theoretical analysis with accompanying behavioral data. The small sample size is an inherent limitation and means a degree of caution is warranted in interpreting the findings. Our results lay the groundwork for an objective methodology with which to calculate and compare the efficacy of different treatments of mind and brain disorders

Amisulpride · Antipsychotic · Aripiprazole · Brain activity and meditation · Electroencephalography · Neuroimaging · Psychiatry · Schizophrenia (object-oriented programming · Functional Brain Connectivity Studies · Medicine · Mental Health Research Topics · Neural dynamics and brain function · Neuroscience · Psychology

  • Lurasidone induces developmental toxicity and behavioral impairments in zebrafish embryos

    Open Access•Wentian Li, Fang Wang et al.•Frontiers in Psychiatry•2025

  • Mixing patterns in networks

    Open Access•M E J Newman•Physical Review E•2003

  • Exploring Network Structure, Dynamics, and Function using NetworkX

    Open Access•Aric A Hagberg, Aric Hagberg et al.•Proceedings of the Python in…•2008

  • N‐back working memory paradigm

    Open Access•Adrian M Owen, Kathryn M McMillan et al.•Human Brain Mapping•2005

  • Working memory

    Open Access•Alan Baddeley•Nature Reviews Neuroscience•2003

  • Node centrality in weighted networks

    Open Access•Tore Opsahl, F Agneessens et al.•Social Networks•2010

Citations per year1
Citation span2025 - 2025 (1)
Highly citedNo
Open DOISci-HubOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae