ArticleSchizophrenia bulletin2023
Semantic Speech Networks Linked to Formal Thought Disorder in Early Psychosis.
Article in Schizophrenia bulletin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- An integrative NLP framework identifies multilevel linguistic phenotypes of schizophrenia across tasks.Psychological medicine · 2026Article
- Linguistic Markers in At-Risk Mental States Using Natural Language Processing: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Linking spontaneous speech, cognition, and psychopathology across affective and psychotic disorders: A network approach.European psychiatry : the journal of the Association of European Psychiatrists · 2026Article
- Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.Translational psychiatry · 2025Article
- Speech analytics across the schizophrenia spectrum disorders: multimodal natural language processing and machine learning modelling in a Chinese-speaking population.Frontiers in psychiatry · 2025Article
- Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Language and Psychosis: Tightening the Association.Schizophrenia bulletin · 2023Article
- Automated analysis of speech as a marker of sub-clinical psychotic experiences.Frontiers in psychiatry · 2023Review
- Cognitive Networks for Knowledge Modeling: A Gentle Introduction for Data- and Cognitive Scientists.Wiley interdisciplinary reviews. Cognitive scienceReview
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
background and hypothesisMapping 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 explicitly modelled the semantic content of speech, which is altered in psychosis. STUDY
designWe 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 (N = 436), and a clinical sample comprising patients with first episode psychosis (FEP), people at clinical high risk of psychosis (CHR-P), and healthy controls (total N = 53). STUDY
resultsSemantic speech networks from the general population were more connected than size-matched randomized networks, with fewer and larger connected components, reflecting the nonrandom 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 signals 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.
conclusionsOverall, 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.
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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.