Speech structure links the neural and socio-behavioural correlates of psychotic disorders

Background: A longstanding notion in the concept of psychosis is the prominence of loosened associative links in thought processes. Assessment of such subtle aspects of thought disorders has proved to be a challenging task in clinical practice and to date no surrogate markers exist that can reliably track the physiological effects of treatments that could reduce thought disorders. Recently, automated speech graph analysis has emerged as a promising means to reliably quantify structural speech disorganization. Methods: Using structural and functional imaging, we investigated the neural basis and the functional relevance of the structural connectedness of speech samples obtained from 56 patients with psychosis (22 with bipolar disorder, 34 with schizophrenia). Speech structure was assessed by non-semantic graph analysis. Results: We found a canonical correlation linking speech connectedness and i) functional as well as developmentally relevant structural brain markers (degree centrality from resting state functional imaging and cortical gyrification index) ii) psychometric evaluation of thought disorder iii) aspects of cognitive performance (processing speed deficits) and iv) functional outcome in patients. Of various clinical metrics, only speech connectedness was correlated with biological markers. Speech connectedness filled the dynamic range of responses better than psychometric measurements of thought disorder. Conclusions: The results provide novel evidence that speech dysconnectivity could emerge from neurodevelopmental deficits and associated dysconnectivity in psychosis

Prediction of psychosis across protocols and risk cohorts using automated language analysis

Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier – comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns – that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry.Fil: Corcoran, Cheryl M.. Icahn School of Medicine at Mount Sinai; Estados Unidos. New York State Psychiatric Institute; Estados UnidosFil: Carrillo, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Bedi, Gillinder. New York State Psychiatric Institute; Estados Unidos. Columbia University; Estados Unidos. University of Melbourne; AustraliaFil: Klim, Casimir. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados UnidosFil: Javitt, Daniel C.. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados UnidosFil: Bearden, Carrie E.. University of California at Los Angeles; Estados UnidosFil: Cecchi, Guillermo Alberto. IBM T.J. Watson Research Center; Estados Unido

A computational approach to the psychiatric diagnosis

One of the most important aims of computational psychiatry (CP) is to provide computational tools to help with earlier diagnoses in psychiatric disorders. CP implements different computer science tools to assist the medical professional in the path to the diagnoses. The paradigm where CP works is to understand the patient as a producer of data from differents cognitive levels. These data could come from physiological studies (as functional magnetic resonance imaging or electroencephalography) or from more abstract levels like patients' thoughts. In our research line, we use spontaneous speech as source as a window into the patient's minds.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Linguistic profile automated characterisation in pluripotential clinical high-risk mental state (CHARMS) conditions: methodology of a multicentre observational study

Introduction: Language is usually considered the social vehicle of thought in intersubjective communications. However, the relationship between language and high- order cognition seems to evade this canonical and unidirectional description (ie, the notion of language as a simple means of thought communication). In recent years, clinical high at-risk mental state (CHARMS) criteria (evolved from the Ultra-High-Risk paradigm) and the introduction of the Clinical Staging system have been proposed to address the dynamicity of early psychopathology. At the same time, natural language processing (NLP) techniques have greatly evolved and have been successfully applied to investigate different neuropsychiatric conditions. The combination of at-risk mental state paradigm, clinical staging system and automated NLP methods, the latter applied on spoken language transcripts, could represent a useful and convenient approach to the problem of early psychopathological distress within a transdiagnostic risk paradigm. Methods and analysis: Help-seeking young people presenting psychological distress (CHARMS+/− and Clinical Stage 1a or 1b; target sample size for both groups n=90) will be assessed through several psychometric tools and multiple speech analyses during an observational period of 1-year, in the context of an Italian multicentric study. Subjects will be enrolled in different contexts: Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Section of Psychiatry, University of Genoa—IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Mental Health Department—territorial mental services (ASL 3—Genoa), Genoa, Italy; and Mental Health Department—territorial mental services (AUSL—Piacenza), Piacenza, Italy. The conversion rate to full-blown psychopathology (CS 2) will be evaluated over 2 years of clinical observation, to further confirm the predictive and discriminative value of CHARMS criteria and to verify the possibility of enriching them with several linguistic features, derived from a fine-grained automated linguistic analysis of speech. Ethics and dissemination: The methodology described in this study adheres to ethical principles as formulated in the Declaration of Helsinki and is compatible with International Conference on Harmonization (ICH)-good clinical practice. The research protocol was reviewed and approved by two different ethics committees (CER Liguria approval code: 591/2020—id.10993; Comitato Etico dell’Area Vasta Emilia Nord approval code: 2022/0071963). Participants will provide their written informed consent prior to study enrolment and parental consent will be needed in the case of participants aged less than 18 years old. Experimental results will be carefully shared through publication in peer- reviewed journals, to ensure proper data reproducibility. Trial registration number DOI:10.17605/OSF.IO/BQZTN

Addressing Formal Thought Disorder in Psychosis through Novel Assessment and Targeted Intervention

Indiana University-Purdue University Indianapolis (IUPUI)Formal thought disorder (FTD) is a debilitating symptom of psychosis. It is linked to functional deficits and generally demonstrates poor response to interventions. Metacognition has emerged as a potential therapeutic target that may be effective in reducing FTD, as metacognitive deficits and FTD both arise from disruptions in associative thought processes. This study’s primary aim was to determine whether FTD could be reduced with metacognitive therapy. Pre-post changes in FTD severity were assessed using clinician-rated and automated measures in 20 individuals with psychotic disorders who received 12 sessions of evidence-based metacognitive therapy. We also examined whether reductions in FTD were larger when assessed with automated instruments versus clinician-rated measures. Aim two compared associations between FTD and three outcome variables (social functioning, role functioning, metacognition) across FTD-measurement approach. Results indicated that automated FTD, but not clinician-rated FTD, was significantly reduced post-intervention. This effect was more robust within a subsample exhibiting greater levels of FTD. Strength of associations between FTD and outcome variables did not differ across FTD measurement approach. These findings provide initial evidence that a targeted metacognitive intervention can reduce FTD. Effects were strongest for automated instruments, which may be more sensitive to detecting change; however, differences in measurement type did not extend to associations with selected outcome variables. This study provides preliminary support for future efforts to reduce FTD. Large-scale studies with longer intervention periods may further our understanding of the effectiveness of metacognitive intervention on FTD

Validation of natural language processing methods capturing semantic incoherence in the speech of patients with non-affective psychosis

Background: Impairments in speech production are a core symptom of non-affective psychosis (NAP). While traditional clinical ratings of patients’ speech involve a subjective human factor, modern methods of natural language processing (NLP) promise an automatic and objective way of analyzing patients’ speech. This study aimed to validate NLP methods for analyzing speech production in NAP patients. Methods: Speech samples from patients with a diagnosis of schizophrenia or schizoaffective disorder were obtained at two measurement points, 6 months apart. Out of N = 71 patients at T1, speech samples were also available for N = 54 patients at T2. Global and local models of semantic coherence as well as different word embeddings (word2vec vs. GloVe) were applied to the transcribed speech samples. They were tested and compared regarding their correlation with clinical ratings and external criteria from cross-sectional and longitudinal measurements. Results: Results did not show differences for global vs. local coherence models and found more significant correlations between word2vec models and clinically relevant outcome variables than for GloVe models. Exploratory analysis of longitudinal data did not yield significant correlation with coherence scores. Conclusion: These results indicate that natural language processing methods need to be critically validated in more studies and carefully selected before clinical application