Machine learning and graph-based connectivity metrics identify language disruption in psychosis: Novel insights from dream reports in an Italian cohort.
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| Titel: | Machine learning and graph-based connectivity metrics identify language disruption in psychosis: Novel insights from dream reports in an Italian cohort. |
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| Autoren: | Camon E; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. Electronic address: elena.camon01@universitadipavia.it., Masier S; Department of Health Sciences, University of Milan, Milan, Italy., Mota NB; Institute of Psychiatry at Federal University of Rio de Janeiro - IPUB/UFRJ, Rio de Janeiro, Brazil; Research department at Mobile Brain, Rio de Janeiro, Brazil., D'Agostino A; Department of Health Sciences, University of Milan, Milan, Italy. |
| Quelle: | Schizophrenia research [Schizophr Res] 2025 Dec; Vol. 286, pp. 55-62. Date of Electronic Publication: 2025 Oct 20. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Elsevier Science Publisher B. V Country of Publication: Netherlands NLM ID: 8804207 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2509 (Electronic) Linking ISSN: 09209964 NLM ISO Abbreviation: Schizophr Res Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam : Elsevier Science Publisher B. V., c1988- |
| MeSH-Schlagworte: | Psychotic Disorders*/complications , Psychotic Disorders*/physiopathology , Machine Learning* , Natural Language Processing* , Dreams* , Language Disorders*/etiology , Language Disorders*/diagnosis, Humans ; Male ; Female ; Italy ; Adult ; Young Adult ; Cohort Studies ; Middle Aged ; Bayes Theorem |
| Abstract: | Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Natalia Bezerra Mota reports a relationship with Mobile Brain (EduTech startup) that includes: employment. Natalia Bezerra Mota reports a relationship with Boehringer Ingelheim that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Language disorganization is a hallmark of psychosis which has traditionally been assessed through clinical interviews and standardized scales. Recent advances in Natural Language Processing (NLP) and graph theory provide innovative, objective methodologies to analyze language production in psychosis. In particular, dream reports, as a unique form of narrative, offer a valuable lens through which to examine the presence of disorganized linguistic features. This study analyzed structural speech graphs of written and oral dream reports from 193 Italian participants (115 individuals with acute psychosis and 78 healthy controls), focusing on key connectivity attributes, such as the Largest Connected Component (LCC), the Largest Strongly Connected Component (LSC) and their z-scores relative to random graph distributions. Patients with psychosis exhibited significantly lower connectivity values than controls (p < 0.0125), with their speech graphs resembling random word sequences more frequently. These results remained significant after controlling for education (p < 0.05), with LCC and LSCz surviving Bonferroni correction (p < 0.0125). A Naïve Bayes classifier using these features achieved 68 % accuracy (AUC = 0.75), demonstrating the potential for automated classification of psychosis. To our knowledge, this is the first study conducted with native Italian speakers, reinforcing the cross-linguistic validity of graph-based approaches. Also, our findings support the utility of graph analysis in detecting psychosis and reinforce the notion that speech abnormalities can be captured from a topological perspective through reductions in speech connectedness, thereby providing a novel framework for understanding thought and language impairments associated with the disorder. (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Connectedness; Graph analysis; Natural language processing tools; Psychosis; Schizophrenia; Thought disorder |
| Entry Date(s): | Date Created: 20251021 Date Completed: 20251122 Latest Revision: 20251122 |
| Update Code: | 20251123 |
| DOI: | 10.1016/j.schres.2025.10.017 |
| PMID: | 41118688 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Natalia Bezerra Mota reports a relationship with Mobile Brain (EduTech startup) that includes: employment. Natalia Bezerra Mota reports a relationship with Boehringer Ingelheim that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Language disorganization is a hallmark of psychosis which has traditionally been assessed through clinical interviews and standardized scales. Recent advances in Natural Language Processing (NLP) and graph theory provide innovative, objective methodologies to analyze language production in psychosis. In particular, dream reports, as a unique form of narrative, offer a valuable lens through which to examine the presence of disorganized linguistic features. This study analyzed structural speech graphs of written and oral dream reports from 193 Italian participants (115 individuals with acute psychosis and 78 healthy controls), focusing on key connectivity attributes, such as the Largest Connected Component (LCC), the Largest Strongly Connected Component (LSC) and their z-scores relative to random graph distributions. Patients with psychosis exhibited significantly lower connectivity values than controls (p < 0.0125), with their speech graphs resembling random word sequences more frequently. These results remained significant after controlling for education (p < 0.05), with LCC and LSCz surviving Bonferroni correction (p < 0.0125). A Naïve Bayes classifier using these features achieved 68 % accuracy (AUC = 0.75), demonstrating the potential for automated classification of psychosis. To our knowledge, this is the first study conducted with native Italian speakers, reinforcing the cross-linguistic validity of graph-based approaches. Also, our findings support the utility of graph analysis in detecting psychosis and reinforce the notion that speech abnormalities can be captured from a topological perspective through reductions in speech connectedness, thereby providing a novel framework for understanding thought and language impairments associated with the disorder.<br /> (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.) |
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| ISSN: | 1573-2509 |
| DOI: | 10.1016/j.schres.2025.10.017 |