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Dalal TC, Liang L, Silva AM, Mackinley M, Voppel A, Palaniyappan L. Speech based natural language profile before, during and after the onset of psychosis: A cluster analysis. Acta Psychiatr Scand 2024. [PMID: 38600593 DOI: 10.1111/acps.13685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 03/20/2024] [Accepted: 03/23/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND AND HYPOTHESIS Speech markers are digitally acquired, computationally derived, quantifiable set of measures that reflect the state of neurocognitive processes relevant for social functioning. "Oddities" in language and communication have historically been seen as a core feature of schizophrenia. The application of natural language processing (NLP) to speech samples can elucidate even the most subtle deviations in language. We aim to determine if NLP based profiles that are distinctive of schizophrenia can be observed across the various clinical phases of psychosis. DESIGN Our sample consisted of 147 participants and included 39 healthy controls (HC), 72 with first-episode psychosis (FEP), 18 in a clinical high-risk state (CHR), 18 with schizophrenia (SZ). A structured task elicited 3 minutes of speech, which was then transformed into quantitative measures on 12 linguistic variables (lexical, syntactic, and semantic). Cluster analysis that leveraged healthy variations was then applied to determine language-based subgroups. RESULTS We observed a three-cluster solution. The largest cluster included most HC and the majority of patients, indicating a 'typical linguistic profile (TLP)'. One of the atypical clusters had notably high semantic similarity in word choices with less perceptual words, lower cohesion and analytical structure; this cluster was almost entirely composed of patients in early stages of psychosis (EPP - early phase profile). The second atypical cluster had more patients with established schizophrenia (SPP - stable phase profile), with more perceptual but less cognitive/emotional word classes, simpler syntactic structure, and a lack of sufficient reference to prior information (reduced givenness). CONCLUSION The patterns of speech deviations in early and established stages of schizophrenia are distinguishable from each other and detectable when lexical, semantic and syntactic aspects are assessed in the pursuit of 'formal thought disorder'.
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Affiliation(s)
- Tyler C Dalal
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | | | | | | | - Alban Voppel
- Robarts Research Institute, London, Ontario, Canada
| | - Lena Palaniyappan
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Robarts Research Institute, London, Ontario, Canada
- Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Western University, London, Ontario, Canada
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Jimeno N. Language and communication rehabilitation in patients with schizophrenia: A narrative review. Heliyon 2024; 10:e24897. [PMID: 38312547 PMCID: PMC10835363 DOI: 10.1016/j.heliyon.2024.e24897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/06/2024] Open
Abstract
Language impairments often appear in patients with schizophrenia and are potential targets for rehabilitation. Clinical practice and research should be intimately connected. The aim was to perform a narrative review of the assessment and intervention tools that have been used for the rehabilitation of schizophrenia patients with language and communication impairments. Two types of tools, general and specific, were developed for both purposes. General tools include the Positive and Negative Syndrome Scale for assessment, and the Integrated Psychological Therapy for intervention. The specific tools used to evaluate language and communication impairments include the Scale for the Assessment of Thought, Language and Communication, the Formal Thought Disorder scales (for caregivers and patients), and the Thought and Language Disorder scale. The most recent language-specific intervention tools include the Cognitive Pragmatic Treatment, Conecta-2, Let's talk! Multimodal Speech-Gesture training, Speech Therapy Intervention Group, and PragmaCom. These tools primarily involve psychopathology/psychiatry, psychology, linguistics, speech and language therapy, and nursing. In conclusion, a wide range of assessment and intervention tools are available for the rehabilitation of language and communication impairments associated with schizophrenia. An integrative and interdisciplinary approach should always be considered for rehabilitation of language and communication in patients with schizophrenia throughout their lifetime.
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Affiliation(s)
- Natalia Jimeno
- School of Medicine, University of Valladolid, Av. Ramón y Cajal 7, E-47005 Valladolid, Spain
- Research Group on Clinical Neuroscience of Castile and Leon, Av. Ramón y Cajal 7, E-47005 Valladolid, Spain
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3
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Alonso-Sánchez MF, Limongi R, Gati J, Palaniyappan L. Language network self-inhibition and semantic similarity in first-episode schizophrenia: A computational-linguistic and effective connectivity approach. Schizophr Res 2023; 259:97-103. [PMID: 35568676 DOI: 10.1016/j.schres.2022.04.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 12/23/2022]
Abstract
INTRODUCTION A central feature of schizophrenia is the disorganization and impoverishment of language. Recently, we observed higher semantic similarity in first-episode-schizophrenia (FES) patients. In this study, we investigate if this aberrant similarity relates to the 'causal' connectivity between two key nodes of the word production system: inferior frontal gyrus (IFG) and the semantic-hub at the ventral anterior temporal lobe (vATL). METHODS Resting-state fMRI scans were collected from 60 participants (30 untreated FES and 30 healthy controls). The semantic distance was measured with the CoVec semantic tool based on GloVe. A spectral dynamic causal model with Parametrical Empirical Bayes was constructed modelling the intrinsic self-inhibitory and extrinsic-excitatory connections within the brain regions. We estimated the parameters of a fully connected model with the semantic distance as a covariate. RESULTS FES patients chose words with higher semantic similarity when describing the pictures compared to the HC group. Among patients, an increased semantic similarity was related with an increase in intrinsic connections within both the vATL and IFG, suggesting that reduced 'synaptic gain' in these regions likely contribute to aberrant sampling of the semantic space during discourse in schizophrenia. CONCLUSIONS Lexical impoverishment relates to increased self-inhibition in both the IFG and vATL. The associated reduction in synaptic gain may relate to reduced precision of locally generated neural activity, forcing the choice of words that are already 'activated' in a lexical network. One approach to improve word sampling may be via promoting synaptic gain via supra-physiological stimulation within the Broca's-vATL network; this proposal needs verification.
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Affiliation(s)
- María Francisca Alonso-Sánchez
- CIDCL, Fonoaudiología, Facultad de Medicina, Universidad de Valparaíso, Chile; Robarts Research Institute, Western University, London, Ontario, Canada.
| | - Roberto Limongi
- Robarts Research Institute, Western University, London, Ontario, Canada
| | - Joseph Gati
- Robarts Research Institute, Western University, London, Ontario, Canada; Centre for Youth Mental Health Service Innovation, Research and Training, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, Western University, London, Ontario, Canada; Centre for Youth Mental Health Service Innovation, Research and Training, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
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4
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Silva AM, Limongi R, MacKinley M, Ford SD, Alonso-Sánchez MF, Palaniyappan L. Syntactic complexity of spoken language in the diagnosis of schizophrenia: A probabilistic Bayes network model. Schizophr Res 2023; 259:88-96. [PMID: 35752547 DOI: 10.1016/j.schres.2022.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/09/2022] [Accepted: 06/12/2022] [Indexed: 01/25/2023]
Abstract
In the clinical linguistics of schizophrenia, syntactic complexity has received much attention. In this study, we address whether syntactic complexity deteriorates within the six months following the first episode of psychosis in those who develop a diagnosis of schizophrenia. We collected data from a cohort of twenty-six first-episode psychosis and 12 healthy control subjects using the Thought and Language Index interview in response to three pictures from the Thematic Apperception Test at first assessment and after six months (the time of consensus diagnosis). An automated labeling (part-of-speech tagging) for specific syntactic elements calculated large and granular syntactic complexity indices with a focus on clause complexity as a particular case from this spoken language data. Probabilistic reasoning leveraging the conditional independence properties of Bayes networks revealed that consensus diagnosis of schizophrenia predicted a decrease in nominal subjects per clause among individuals with first episode psychosis. From the entire sample, we estimate a 95.4 % probability that a 50 % decrease in mean nominal subjects per clause after six months is explained by the presence of first episode psychosis. Among those with psychosis, a 30 % decrease in this clause-complexity index after six months of experiencing the first episode predicted with 95 % probability a consensus diagnosis of schizophrenia, representing a conditional relationship between a longitudinal decrease in syntactic complexity and a diagnosis of schizophrenia. We conclude that an early drift towards linguistic disorganization/impoverishment of clause complexity-at the granular level of nominal subject per clause-is a distinctive feature of schizophrenia that decreases longitudinally, thus differentiating schizophrenia from other psychotic illnesses with shared phenomenology.
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Affiliation(s)
- Angelica M Silva
- Robarts Research Institute, Western University, London, Ontario, Canada.
| | - Roberto Limongi
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Psychology, Western University, London, Canada; Faculty of Human and Social Sciences, Wilfred Laurier University, Brantford, Ontario, Canada
| | - Michael MacKinley
- Robarts Research Institute, Western University, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada
| | - Sabrina D Ford
- Lawson Health Research Institute, London, Ontario, Canada
| | | | - Lena Palaniyappan
- Robarts Research Institute, Western University, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada
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Limongi R, Silva AM, Mackinley M, Ford SD, Palaniyappan L. Active Inference, Epistemic Value, and Uncertainty in Conceptual Disorganization in First-Episode Schizophrenia. Schizophr Bull 2023; 49:S115-S124. [PMID: 36946528 PMCID: PMC10031740 DOI: 10.1093/schbul/sbac125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
BACKGROUND AND HYPOTHESIS Active inference has become an influential concept in psychopathology. We apply active inference to investigate conceptual disorganization in first-episode schizophrenia. We conceptualize speech production as a decision-making process affected by the latent "conceptual organization"-as a special case of uncertainty about the causes of sensory information. Uncertainty is both minimized via speech production-in which function words index conceptual organization in terms of analytic thinking-and tracked by a domain-general salience network. We hypothesize that analytic thinking depends on conceptual organization. Therefore, conceptual disorganization in schizophrenia would be both indexed by low conceptual organization and reflected in the effective connectivity within the salience network. STUDY DESIGN With 1-minute speech samples from a picture description task and resting state fMRI from 30 patients and 30 healthy subjects, we employed dynamic causal and probabilistic graphical models to investigate if the effective connectivity of the salience network underwrites conceptual organization. STUDY RESULTS Low analytic thinking scores index low conceptual organization which affects diagnostic status. The influence of the anterior insula on the anterior cingulate cortex and the self-inhibition within the anterior cingulate cortex are elevated given low conceptual organization (ie, conceptual disorganization). CONCLUSIONS Conceptual organization, a construct that explains formal thought disorder, can be modeled in an active inference framework and studied in relation to putative neural substrates of disrupted language in schizophrenia. This provides a critical advance to move away from rating-scale scores to deeper constructs in the pursuit of the pathophysiology of formal thought disorder.
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Affiliation(s)
- Roberto Limongi
- Department of Psychology, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, London, ON, Canada
| | | | - Michael Mackinley
- Robarts Research Institute, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
| | | | - Lena Palaniyappan
- Robarts Research Institute, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
- The Brain and Mind Institute, University of Western Ontario, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
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Mackinley M, Limongi R, Silva AM, Richard J, Subramanian P, Ganjavi H, Palaniyappan L. More than words: Speech production in first-episode psychosis predicts later social and vocational functioning. Front Psychiatry 2023; 14:1144281. [PMID: 37124249 PMCID: PMC10140590 DOI: 10.3389/fpsyt.2023.1144281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/20/2023] [Indexed: 05/02/2023] Open
Abstract
Background Several disturbances in speech are present in psychosis; however, the relationship between these disturbances during the first-episode of psychosis (FEP) and later vocational functioning is unclear. Demonstrating this relationship is critical if we expect speech and communication deficits to emerge as targets for early intervention. Method We analyzed three 1-min speech samples using automated speech analysis and Bayes networks in an antipsychotic-naive sample of 39 FEP patients and followed them longitudinally to determine their vocational status (engaged or not engaged in employment education or training-EET vs. NEET) after 6-12 months of treatment. Five baseline linguistic variables with prior evidence of clinical relevance (total and acausal connectives use, pronoun use, analytic thinking, and total words uttered in a limited period) were included in a Bayes network along with follow-up NEET status and Social and Occupational Functioning Assessment Scale (SOFAS) scores to determine dependencies among these variables. We also included clinical (Positive and Negative Syndrome Scale 8-item version (PANSS-8)), social (parental socioeconomic status), and cognitive features (processing speed) at the time of presentation as covariates. Results The Bayes network revealed that only total words spoken at the baseline assessment were directly associated with later NEET status and had an indirect association with SOFAS, with a second set of dependencies emerging among the remaining linguistic variables. The primary (speech-only) model outperformed models including parental socioeconomic status, processing speed or both as latent variables. Conclusion Impoverished speech, even at subclinical levels, may hold prognostic value for functional outcomes and warrant consideration when providing measurement based care for first-episode psychosis.
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Affiliation(s)
- Michael Mackinley
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
| | - Roberto Limongi
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | | | - Julie Richard
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
| | - Priya Subramanian
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
| | - Hooman Ganjavi
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- *Correspondence: Lena Palaniyappan,
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Thought disorder is correlated with atypical spoken binomial orderings. SCHIZOPHRENIA 2022; 8:25. [PMID: 35304875 PMCID: PMC8933394 DOI: 10.1038/s41537-022-00238-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 02/23/2022] [Indexed: 11/30/2022]
Abstract
Thought disorder may be associated with subtle language abnormalities. Binomials are pairs of words of the same grammatical type that are joined by a conjunction that often have a preferred order (for example, “up and down” is more common than “down and up”). We analyzed speech transcripts from patients with first-episode psychosis and found that atypical ordering of binomial pairs was associated with thought disorder but not with other psychosis symptoms. These results illustrate the potential to generate objective, quantifiable measures of disorganized speech.
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Bambini V, Frau F, Bischetti L, Cuoco F, Bechi M, Buonocore M, Agostoni G, Ferri I, Sapienza J, Martini F, Spangaro M, Bigai G, Cocchi F, Cavallaro R, Bosia M. Deconstructing heterogeneity in schizophrenia through language: a semi-automated linguistic analysis and data-driven clustering approach. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:102. [PMID: 36446789 PMCID: PMC9708845 DOI: 10.1038/s41537-022-00306-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Previous works highlighted the relevance of automated language analysis for predicting diagnosis in schizophrenia, but a deeper language-based data-driven investigation of the clinical heterogeneity through the illness course has been generally neglected. Here we used a semiautomated multidimensional linguistic analysis innovatively combined with a machine-driven clustering technique to characterize the speech of 67 individuals with schizophrenia. Clusters were then compared for psychopathological, cognitive, and functional characteristics. We identified two subgroups with distinctive linguistic profiles: one with higher fluency, lower lexical variety but greater use of psychological lexicon; the other with reduced fluency, greater lexical variety but reduced psychological lexicon. The former cluster was associated with lower symptoms and better quality of life, pointing to the existence of specific language profiles, which also show clinically meaningful differences. These findings highlight the importance of considering language disturbances in schizophrenia as multifaceted and approaching them in automated and data-driven ways.
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Affiliation(s)
- Valentina Bambini
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy.
| | - Federico Frau
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy
| | - Luca Bischetti
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy
| | - Federica Cuoco
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Margherita Bechi
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Mariachiara Buonocore
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giulia Agostoni
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Ilaria Ferri
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jacopo Sapienza
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesca Martini
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Spangaro
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giorgia Bigai
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Federica Cocchi
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberto Cavallaro
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Marta Bosia
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
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Gargano G, Caletti E, Perlini C, Turtulici N, Bellani M, Bonivento C, Garzitto M, Siri FM, Longo C, Bonetto C, Cristofalo D, Scocco P, Semrov E, Preti A, Lazzarotto L, Gardellin F, Lasalvia A, Ruggeri M, Marini A, Brambilla P. Language production impairments in patients with a first episode of psychosis. PLoS One 2022; 17:e0272873. [PMID: 35951619 PMCID: PMC9371299 DOI: 10.1371/journal.pone.0272873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 07/27/2022] [Indexed: 11/18/2022] Open
Abstract
Language production has often been described as impaired in psychiatric diseases such as in psychosis. Nevertheless, little is known about the characteristics of linguistic difficulties and their relation with other cognitive domains in patients with a first episode of psychosis (FEP), either affective or non-affective. To deepen our comprehension of linguistic profile in FEP, 133 patients with FEP (95 non-affective, FEP-NA; 38 affective, FEP-A) and 133 healthy controls (HC) were assessed with a narrative discourse task. Speech samples were systematically analyzed with a well-established multilevel procedure investigating both micro- (lexicon, morphology, syntax) and macro-linguistic (discourse coherence, pragmatics) levels of linguistic processing. Executive functioning and IQ were also evaluated. Both linguistic and neuropsychological measures were secondarily implemented with a machine learning approach in order to explore their predictive accuracy in classifying participants as FEP or HC. Compared to HC, FEP patients showed language production difficulty at both micro- and macro-linguistic levels. As for the former, FEP produced shorter and simpler sentences and fewer words per minute, along with a reduced number of lexical fillers, compared to HC. At the macro-linguistic level, FEP performance was impaired in local coherence, which was paired with a higher percentage of utterances with semantic errors. Linguistic measures were not correlated with any neuropsychological variables. No significant differences emerged between FEP-NA and FEP-A (p≥0.02, after Bonferroni correction). Machine learning analysis showed an accuracy of group prediction of 76.36% using language features only, with semantic variables being the most impactful. Such a percentage was enhanced when paired with clinical and neuropsychological variables. Results confirm the presence of language production deficits already at the first episode of the illness, being such impairment not related to other cognitive domains. The high accuracy obtained by the linguistic set of features in classifying groups support the use of machine learning methods in neuroscience investigations.
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Affiliation(s)
- Giulia Gargano
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Elisabetta Caletti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Cinzia Perlini
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Verona Hospital Trust–Azienda Ospedaliera Universitaria Integrata Verona–AOUI, Verona, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Marcella Bellani
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Verona Hospital Trust–Azienda Ospedaliera Universitaria Integrata Verona–AOUI, Verona, Italy
| | - Carolina Bonivento
- IRCCS “E.Medea” Polo Friuli Venezia Giulia, San Vito al Tagliamento, PN, Italy
| | - Marco Garzitto
- Department of Languages and Literatures, Communication, Education and Society, University of Udine, Udine, Italy
| | - Francesca Marzia Siri
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Chiara Longo
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Chiara Bonetto
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Doriana Cristofalo
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Paolo Scocco
- Department of Mental Health, Azienda ULSS 16, Padua, Italy
| | | | - Antonio Preti
- Department of Mental Health, Niguarda Ca’ Granda Hospital, Milan, Italy
| | - Lorenza Lazzarotto
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Antonio Lasalvia
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Verona Hospital Trust–Azienda Ospedaliera Universitaria Integrata Verona–AOUI, Verona, Italy
| | - Mirella Ruggeri
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Verona Hospital Trust–Azienda Ospedaliera Universitaria Integrata Verona–AOUI, Verona, Italy
| | - Andrea Marini
- Department of Languages and Literatures, Communication, Education and Society, University of Udine, Udine, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy
- * E-mail:
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Progressive changes in descriptive discourse in First Episode Schizophrenia: a longitudinal computational semantics study. NPJ SCHIZOPHRENIA 2022; 8:36. [PMID: 35853894 PMCID: PMC9261094 DOI: 10.1038/s41537-022-00246-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/14/2022] [Indexed: 12/14/2022]
Abstract
AbstractComputational semantics, a branch of computational linguistics, involves automated meaning analysis that relies on how words occur together in natural language. This offers a promising tool to study schizophrenia. At present, we do not know if these word-level choices in speech are sensitive to the illness stage (i.e., acute untreated vs. stable established state), track cognitive deficits in major domains (e.g., cognitive control, processing speed) or relate to established dimensions of formal thought disorder. In this study, we collected samples of descriptive discourse in patients experiencing an untreated first episode of schizophrenia and healthy control subjects (246 samples of 1-minute speech; n = 82, FES = 46, HC = 36) and used a co-occurrence based vector embedding of words to quantify semantic similarity in speech. We obtained six-month follow-up data in a subsample (99 speech samples, n = 33, FES = 20, HC = 13). At baseline, semantic similarity was evidently higher in patients compared to healthy individuals, especially when social functioning was impaired; but this was not related to the severity of clinically ascertained thought disorder in patients. Across the study sample, higher semantic similarity at baseline was related to poorer Stroop performance and processing speed. Over time, while semantic similarity was stable in healthy subjects, it increased in patients, especially when they had an increasing burden of negative symptoms. Disruptions in word-level choices made by patients with schizophrenia during short 1-min descriptions are sensitive to interindividual differences in cognitive and social functioning at first presentation and persist over the early course of the illness.
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11
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More than a biomarker: could language be a biosocial marker of psychosis? NPJ SCHIZOPHRENIA 2021; 7:42. [PMID: 34465778 PMCID: PMC8408150 DOI: 10.1038/s41537-021-00172-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/06/2021] [Indexed: 02/07/2023]
Abstract
Automated extraction of quantitative linguistic features has the potential to predict objectively the onset and progression of psychosis. These linguistic variables are often considered to be biomarkers, with a large emphasis placed on the pathological aberrations in the biological processes that underwrite the faculty of language in psychosis. This perspective offers a reminder that human language is primarily a social device that is biologically implemented. As such, linguistic aberrations in patients with psychosis reflect both social and biological processes affecting an individual. Failure to consider the sociolinguistic aspects of NLP measures will limit their usefulness as digital tools in clinical settings. In the context of psychosis, considering language as a biosocial marker could lead to less biased and more accessible tools for patient-specific predictions in the clinic.
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