Semantic speech networks linked to formal thought disorder in early psychosis
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Authors
Nettekoven, Caroline
Diederen, Kelly
Giles, Oscar
Duncan, Helen
Stenson, Iain
Olah, Julianna
Gibbs-Dean, Toni
Collier, Nigel
Vertes, Petra
Spencer, Tom
McGuire, Philip
Journal Title
Schizophrenia Bulletin
ISSN
0586-7614
Publisher
Oxford University Press (OUP)
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Nettekoven, C., Diederen, K., Giles, O., Duncan, H., Stenson, I., Olah, J., Gibbs-Dean, T., et al. Semantic speech networks linked to formal thought disorder in early psychosis. Schizophrenia Bulletin https://doi.org/10.17863/CAM.84735
Abstract
Background and Hypothesis. Mapping a patient’s speech as a
network has proved to be a useful way of understanding formal thought disorder in psychosis. However, to date, graph theory tools have not incorporated the semantic content of speech, which is altered in psychosis.
Study Design. We developed an algorithm, “netts”, to map the
semantic content of speech as a network, then applied netts to
construct semantic speech networks for a general population
sample, and a clinical sample comprising patients with first
episode psychosis (FEP), people at clinical high risk of psychosis
(CHR-P), and healthy controls.
Study Results. Semantic speech networks from the general population were more connected than size-matched randomised networks, with fewer and larger connected components, reflecting the non-random nature of speech. Networks from FEP patients were smaller than from healthy participants, for a picture description task but not a story recall task. For the former task, FEP networks were also more fragmented than those from controls; showing more connected components, which tended to include fewer nodes on average. CHR-P networks showed fragmentation values in-between FEP patients and controls. A clustering analysis suggested that semantic speech networks captured novel signal not already described by existing NLP measures. Network features were also related to negative symptom scores and scores on the Thought and Language Index, although these relationships did not survive correcting for multiple comparisons.
Conclusions. Overall, these data suggest that semantic networks
can enable deeper phenotyping of formal thought disorder in
psychosis. Whilst here we focus on network fragmentation, the
semantic speech networks created by Netts also contain other,
rich information which could be extracted to shed further light
on formal thought disorder. We are releasing Netts as an open
Python package alongside this manuscript.
Sponsorship
Alan Turing Institute (R-CAM-006)
Embargo Lift Date
2025-05-19
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.84735
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337321
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