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Zaher F, Diallo M, Achim AM, Joober R, Roy MA, Demers MF, Subramanian P, Lavigne KM, Lepage M, Gonzalez D, Zeljkovic I, Davis K, Mackinley M, Sabesan P, Lal S, Voppel A, Palaniyappan L. Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities. Schizophr Res 2024; 266:205-215. [PMID: 38428118 DOI: 10.1016/j.schres.2024.02.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 02/18/2024] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.
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Affiliation(s)
- Farida Zaher
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Mariama Diallo
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Amélie M Achim
- Département de Psychiatrie et Neurosciences, Université Laval, Québec City, QC, Canada; Vitam - Centre de Recherche en Santé Durable, Québec City, QC, Canada; Centre de Recherche CERVO, Québec City, QC, Canada
| | - Ridha Joober
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Marc-André Roy
- Département de Psychiatrie et Neurosciences, Université Laval, Québec City, QC, Canada; Centre de Recherche CERVO, Québec City, QC, Canada
| | - Marie-France Demers
- Centre de Recherche CERVO, Québec City, QC, Canada; Faculté de Pharmacie, Université Laval, Québec City, QC, Canada
| | - Priya Subramanian
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada
| | - Katie M Lavigne
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Martin Lepage
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Daniela Gonzalez
- Prevention and Early Intervention Program for Psychosis, London Health Sciences Center, Lawson Health Research Institute, London, ON, Canada
| | - Irnes Zeljkovic
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada
| | - Kristin Davis
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Michael Mackinley
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada; Prevention and Early Intervention Program for Psychosis, London Health Sciences Center, Lawson Health Research Institute, London, ON, Canada
| | - Priyadharshini Sabesan
- Lakeshore General Hospital and Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Shalini Lal
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada; School of Rehabilitation, Faculty of Medicine, University of Montréal, Montréal, QC, Canada
| | - Alban Voppel
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada; Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada; Robarts Research Institute, Western University, London, ON, Canada.
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2
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Olah J, Cummins N, Arribas M, Gibbs-Dean T, Molina E, Sethi D, Kempton MJ, Morgan S, Spencer T, Diederen K. Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures. Transl Psychiatry 2024; 14:156. [PMID: 38509087 PMCID: PMC10954690 DOI: 10.1038/s41398-024-02851-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 02/15/2024] [Accepted: 02/22/2024] [Indexed: 03/22/2024] Open
Abstract
Automatically extracted measures of speech constitute a promising marker of psychosis as disorganized speech is associated with psychotic symptoms and predictive of psychosis-onset. The potential of speech markers is, however, hampered by (i) lengthy assessments in laboratory settings and (ii) manual transcriptions. We investigated whether a short, scalable data collection (online) and processing (automated transcription) procedure would provide data of sufficient quality to extract previously validated speech measures. To evaluate the fit of our approach for purpose, we assessed speech in relation to psychotic-like experiences in the general population. Participants completed an 8-minute-long speech task online. Sample 1 included measures of psychometric schizotypy and delusional ideation (N = 446). Sample 2 included a low and high psychometric schizotypy group (N = 144). Recordings were transcribed both automatically and manually, and connectivity, semantic, and syntactic speech measures were extracted for both types of transcripts. 73%/86% participants in sample 1/2 completed the experiment. Nineteen out of 25 speech measures were strongly (r > 0.7) and significantly correlated between automated and manual transcripts in both samples. Amongst the 14 connectivity measures, 11 showed a significant relationship with delusional ideation. For the semantic and syntactic measures, On Topic score and the Frequency of personal pronouns were negatively correlated with both schizotypy and delusional ideation. Combined with demographic information, the speech markers could explain 11-14% of the variation of delusional ideation and schizotypy in Sample 1 and could discriminate between high-low schizotypy with high accuracy (0.72-0.70, AUC = 0.78-0.79) in Sample 2. The moderate to high retention rate, strong correlation of speech measures across manual and automated transcripts and sensitivity to psychotic-like experiences provides initial evidence that online collected speech in combination with automatic transcription is a feasible approach to increase accessibility and scalability of speech-based assessment of psychosis.
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Affiliation(s)
- Julianna Olah
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Nicholas Cummins
- Institute of Psychiatry, Psychology and Neuroscience, Department of Biostatistics & Health Informatics, King's College London, London, UK
| | - Maite Arribas
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Toni Gibbs-Dean
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Elena Molina
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Divina Sethi
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sarah Morgan
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Tom Spencer
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kelly Diederen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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Olah J, Spencer T, Cummins N, Diederen K. Automated analysis of speech as a marker of sub-clinical psychotic experiences. Front Psychiatry 2024; 14:1265880. [PMID: 38361830 PMCID: PMC10867252 DOI: 10.3389/fpsyt.2023.1265880] [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: 07/24/2023] [Accepted: 12/22/2023] [Indexed: 02/17/2024] Open
Abstract
Automated speech analysis techniques, when combined with artificial intelligence and machine learning, show potential in capturing and predicting a wide range of psychosis symptoms, garnering attention from researchers. These techniques hold promise in predicting the transition to clinical psychosis from at-risk states, as well as relapse or treatment response in individuals with clinical-level psychosis. However, challenges in scientific validation hinder the translation of these techniques into practical applications. Although sub-clinical research could aid to tackle most of these challenges, there have been only few studies conducted in speech and psychosis research in non-clinical populations. This work aims to facilitate this work by summarizing automated speech analytical concepts and the intersection of this field with psychosis research. We review psychosis continuum and sub-clinical psychotic experiences, and the benefits of researching them. Then, we discuss the connection between speech and psychotic symptoms. Thirdly, we overview current and state-of-the art approaches to the automated analysis of speech both in terms of language use (text-based analysis) and vocal features (audio-based analysis). Then, we review techniques applied in subclinical population and findings in these samples. Finally, we discuss research challenges in the field, recommend future research endeavors and outline how research in subclinical populations can tackle the listed challenges.
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Affiliation(s)
- Julianna Olah
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Thomas Spencer
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Kelly Diederen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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4
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Malgaroli M, Hull TD, Zech JM, Althoff T. Natural language processing for mental health interventions: a systematic review and research framework. Transl Psychiatry 2023; 13:309. [PMID: 37798296 PMCID: PMC10556019 DOI: 10.1038/s41398-023-02592-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/31/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP's potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients' clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers' characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.
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Affiliation(s)
- Matteo Malgaroli
- Department of Psychiatry, New York University, Grossman School of Medicine, New York, NY, 10016, USA.
| | | | - James M Zech
- Talkspace, New York, NY, 10025, USA
- Department of Psychology, Florida State University, Tallahassee, FL, 32306, USA
| | - Tim Althoff
- Department of Computer Science, University of Washington, Seattle, WA, 98195, USA
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5
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Mota NB, Weissheimer J, Finger I, Ribeiro M, Malcorra B, Hübner L. Speech as a Graph: Developmental Perspectives on the Organization of Spoken Language. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:985-993. [PMID: 37085138 DOI: 10.1016/j.bpsc.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 04/02/2023] [Accepted: 04/10/2023] [Indexed: 04/23/2023]
Abstract
Language has been used as a privileged window to investigate mental processes. More recently, descriptions of psychopathological symptoms have been analyzed with the help of natural language processing tools. An example is the study of speech organization using graph theoretical approaches that began approximately 10 years ago. After its application in different areas, there is a need to better characterize what aspects can be associated with typical and atypical behavior throughout the lifespan, given the variables related to aging as well as biological and social contexts. The precise quantification of mental processes assessed through language may allow us to disentangle biological/social markers by looking at naturalistic protocols in different contexts. In this review, we discuss 10 years of studies in which word recurrence graphs were adopted to characterize the chain of thoughts expressed by individuals while producing discourse. Initially developed to understand formal thought disorder in the context of psychotic syndromes, this line of research has been expanded to understand the atypical development in different stages of psychosis and differential diagnosis (such as dementia) as well as the typical development of thought organization in school-age children/teenagers in naturalistic and school-based protocols. We comment on the effects of environmental factors, such as education and reading habits (in monolingual and bilingual contexts), in clinical and nonclinical populations at different developmental stages (from childhood to older adulthood, considering aging effects on cognition). Looking toward the future, there is an opportunity to use word recurrence graphs to address complex questions that consider biological/social factors within a developmental perspective in typical and atypical contexts.
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Affiliation(s)
- Natália Bezerra Mota
- Department of Psychiatry and Legal Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Research Department, Motrix Laboratory - Motrix, Rio de Janeiro, Brazil.
| | - Janaina Weissheimer
- Department of Modern Foreign Languages, Federal University of Rio Grande do Norte, Natal, Brazil; Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil; National Council for Scientific and Technological Development, Brasília, Brazil
| | - Ingrid Finger
- National Council for Scientific and Technological Development, Brasília, Brazil; Department of Modern Languages, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Marina Ribeiro
- Research Department, Motrix Laboratory - Motrix, Rio de Janeiro, Brazil; Bioinformatics Multidisciplinary Environment-Federal University of Rio Grande do Norte, Natal, Brazil
| | - Bárbara Malcorra
- Research Department, Motrix Laboratory - Motrix, Rio de Janeiro, Brazil
| | - Lilian Hübner
- National Council for Scientific and Technological Development, Brasília, Brazil; Department of Linguistics-Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
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6
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Fradkin I, Nour MM, Dolan RJ. Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1013-1023. [PMID: 37257754 DOI: 10.1016/j.bpsc.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/19/2023] [Accepted: 05/19/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD. METHODS We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and tangentiality (how quickly meaning drifts away from the topic) in detecting and dissociating the 2 underlying impairments. RESULTS Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were nonmonotonic in that forgetting the global context resulted in sentences that were semantically closer to their local, intermediate context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance. CONCLUSIONS This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial.
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Affiliation(s)
- Isaac Fradkin
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom.
| | - Matthew M Nour
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Raymond J Dolan
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Wellcome Trust Centre for Human Neuroimaging, University College London, London, United Kingdom; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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7
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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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8
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Mota NB, Ribeiro M, Malcorra BLC, Atídio JP, Haguiara B, Gadelha A. Happy thoughts: What computational assessment of connectedness and emotional words can inform about early stages of psychosis. Schizophr Res 2023; 259:38-47. [PMID: 35811267 DOI: 10.1016/j.schres.2022.06.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022]
Abstract
In recent years, different natural language processing tools measured aspects related to narratives' structural, semantic, and emotional content. However, there is a need to better understand the limitations and effectiveness of speech elicitation protocols. The graph-theoretical analysis applied to short narratives reveals lower connectedness associated with negative symptoms even in the early stages of psychosis, but emotional topics seem more informative than others. We investigate the interaction between connectedness and emotional words with negative symptoms and educational level in participants with and without psychosis. For that purpose, we used a speech elicitation protocol based on three positive affective pictures and calculated the proportion of emotional words and connectedness measures in the first-episode psychosis (FEP) group (N: 24) and a control group (N: 33). First, we replicated the association between connectedness and negative symptoms (R2: 0.53, p: 0.0049). Second, the more positive terms, the more connected the narrative was, exclusively under psychosis and in association with education, pointing to an interaction between symptoms and formal education. Negative symptoms were independently associated with connectedness, but not with emotional words, although the associations with education were mutually dependent. Together, education and symptoms explained almost 70 % of connectedness variance (R2: 0.67, p < 0.0001), but not emotional expression. At this initial stage of psychosis, education seems to play an important role, diminishing the impact of negative symptoms on the narrative connectedness. Negative symptoms in FEP impact narrative connectedness in association with emotional expression, revealing aspects of social cognition through a short and innocuous protocol.
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Affiliation(s)
- Natália Bezerra Mota
- Department of Psychiatry and Legal Medicine, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil; Research department at Motrix Lab, Motrix, Rio de Janeiro, Brazil.
| | - Marina Ribeiro
- Research department at Motrix Lab, Motrix, Rio de Janeiro, Brazil
| | | | - João Paulo Atídio
- Schizophrenia Program (PROESQ), Department of Psychiatry, Escola Paulista de Medicina, Universidade Federal de São Paulo (EPM/UNIFESP), Brazil
| | - Bernardo Haguiara
- Schizophrenia Program (PROESQ), Department of Psychiatry, Escola Paulista de Medicina, Universidade Federal de São Paulo (EPM/UNIFESP), Brazil
| | - Ary Gadelha
- Schizophrenia Program (PROESQ), Department of Psychiatry, Escola Paulista de Medicina, Universidade Federal de São Paulo (EPM/UNIFESP), Brazil
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9
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Tang SX, Cong Y, Nikzad AH, Mehta A, Cho S, Hänsel K, Berretta S, Dhar AA, Kane JM, Malhotra AK. Clinical and computational speech measures are associated with social cognition in schizophrenia spectrum disorders. Schizophr Res 2023; 259:28-37. [PMID: 35835710 DOI: 10.1016/j.schres.2022.06.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 12/15/2022]
Abstract
In this study, we compared three domains of social cognition (emotion processing, mentalizing, and attribution bias) to clinical and computational language measures in 63 participants with schizophrenia spectrum disorders. Based on the active inference model for discourse, we hypothesized that emotion processing and mentalizing, but not attribution bias, would be related to language disturbances. Clinical ratings for speech disturbance assessed disorganized and underproductive dimensions. Computational features included speech graph metrics, use of modal verbs, use of first-person pronouns, cosine similarity of adjacent utterances, and measures of sentiment; these were represented by four principal components. We found that higher clinical ratings for disorganized speech were predicted by greater impairments in both emotion processing and mentalizing, and that these relationships remained significant when accounting for demographic variables, overall psychosis symptoms, and verbal ability. Similarly, a computational speech component reflecting insular speech was consistently predicted by impairment in emotion processing. There were notable trends for computational speech components reflecting underproductive speech and decreased content-rich speech predicting mentalizing ability. Exploratory longitudinal analyses in a small subset of participants (n = 17) found that improvements in both emotion processing and mentalizing predicted improvements in disorganized speech. Attribution bias did not demonstrate strong relationships with language measures. Altogether, our findings are consistent with the active inference model of discourse and suggest greater emphasis on treatments that target social cognitive and language systems.
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Affiliation(s)
- Sunny X Tang
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Yan Cong
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Amir H Nikzad
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Aarush Mehta
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Sunghye Cho
- University of Pennsylvania, Linguistic Data Consortium, 3600 Market St., Suite 810, Philadelphia, PA 19104, United States of America.
| | - Katrin Hänsel
- Yale University, Department of Laboratory Medicine, 195 Church Street, New Haven, CT 06510, United States of America.
| | - Sarah Berretta
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Aamina A Dhar
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America
| | - John M Kane
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Anil K Malhotra
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
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10
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Gomes M, Picó Pérez M, Castro I, Moreira P, Ribeiro S, Mota NB, Morgado P. Speech graph analysis in obsessive-compulsive disorder: The relevance of dream reports. J Psychiatr Res 2023; 161:358-363. [PMID: 37004408 DOI: 10.1016/j.jpsychires.2023.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 03/12/2023] [Accepted: 03/27/2023] [Indexed: 04/04/2023]
Abstract
Obsessive-compulsive disorder (OCD) is a distressing disorder characterized by the presence of intrusive thoughts, images or urges (obsessions) and/or behavioral efforts to reduce the anxiety (compulsions). OCD lifetime prevalence varies between 1% and 3% in the general population and there are no reliable markers that support the diagnosis. In order to fill this gap, Computational Psychiatry employs multiple types of quantitative analyses to improve the understanding, diagnosis, prediction, and treatment of mental illnesses including OCD. One of these computational tools is speech graphs analysis. A graph represents a network of nodes connected by edges: in non-semantic speech graphs, nodes correspond to words and edges correspond to the directed link between consecutive words. Using non-semantic speech graphs, we compared free speech samples from OCD patients and healthy controls (HC), to test whether speech graphs analysis can grasp structural differences in speech between these groups. To this end, 39 OCD patients and 37 HC were interviewed and recorded during six types of speech reports: yesterday, dream, old memory, positive image, negative image and neutral image. Also, the Obsessive-Compulsive Inventory-Revised (OCI-R) and the Yale Brown Obsessive-Compulsive Scale (Y-BOCS) were used to assess symptom severity. The graph-theoretical structural analysis of dream reports showed that OCD patients have significantly smaller lexical diversity, lower speech connectedness and a higher recurrence of words in comparison with HC. The other five report types failed to show differences between the groups, adding to the notion that dream reports are especially informative of speech structure in different psychiatric states. Further investigation is necessary to completely assess the potential of this tool in OCD.
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Affiliation(s)
- Matilde Gomes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal; ICVS/3B's, PT Government Associate Laboratory, Braga, Guimarães, Portugal; Clinical Academic Center - Braga, Portugal
| | - Maria Picó Pérez
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal; ICVS/3B's, PT Government Associate Laboratory, Braga, Guimarães, Portugal; Clinical Academic Center - Braga, Portugal; Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Castelló de la Plana, Spain
| | - Inês Castro
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal; ICVS/3B's, PT Government Associate Laboratory, Braga, Guimarães, Portugal; Clinical Academic Center - Braga, Portugal
| | - Pedro Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal; ICVS/3B's, PT Government Associate Laboratory, Braga, Guimarães, Portugal; Clinical Academic Center - Braga, Portugal; School of Psychology, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
| | - Sidarta Ribeiro
- Brain Institute, Federal University of Rio Grande do Norte, 59078-900, Natal, Brazil
| | - Natália B Mota
- Institute of Psychiatry, Federal University of Rio de Janeiro, Brazil
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal; ICVS/3B's, PT Government Associate Laboratory, Braga, Guimarães, Portugal; Clinical Academic Center - Braga, Portugal.
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11
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Voleti R, Woolridge SM, Liss JM, Milanovic M, Stegmann G, Hahn S, Harvey PD, Patterson TL, Bowie CR, Berisha V. Language Analytics for Assessment of Mental Health Status and Functional Competency. Schizophr Bull 2023; 49:S183-S195. [PMID: 36946533 PMCID: PMC10031731 DOI: 10.1093/schbul/sbac176] [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 Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling. STUDY DESIGN Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer. STUDY RESULTS Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables. CONCLUSIONS Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency.
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Affiliation(s)
- Rohit Voleti
- School of Electrical Computer, and Energy Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Julie M Liss
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Aural Analytics Inc., Scottsdale, AZ, USA
| | - Melissa Milanovic
- CBT for Psychosis Service at the Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Gabriela Stegmann
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Aural Analytics Inc., Scottsdale, AZ, USA
| | - Shira Hahn
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Aural Analytics Inc., Scottsdale, AZ, USA
| | - Philip D Harvey
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Thomas L Patterson
- Department of Psychiatry, University of California, San Diego, La Jolla, CAUSA
| | | | - Visar Berisha
- School of Electrical Computer, and Energy Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Aural Analytics Inc., Scottsdale, AZ, USA
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12
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de Boer JN, Voppel AE, Brederoo SG, Schnack HG, Truong KP, Wijnen FNK, Sommer IEC. Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool. Psychol Med 2023; 53:1302-1312. [PMID: 34344490 PMCID: PMC10009369 DOI: 10.1017/s0033291721002804] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 06/10/2021] [Accepted: 06/21/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. METHODS Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. RESULTS The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. CONCLUSIONS Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.
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Affiliation(s)
- J. N. de Boer
- Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University & University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - A. E. Voppel
- Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - S. G. Brederoo
- Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - H. G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University & University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
- Utrecht Institute of Linguistics OTS, Utrecht University, Utrecht, the Netherlands
| | - K. P. Truong
- Department of Human Media Interaction, University of Twente, Enschede, the Netherlands
| | - F. N. K. Wijnen
- Utrecht Institute of Linguistics OTS, Utrecht University, Utrecht, the Netherlands
| | - I. E. C. Sommer
- Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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Wießner I, Falchi M, Daldegan-Bueno D, Palhano-Fontes F, Olivieri R, Feilding A, B Araujo D, Ribeiro S, Bezerra Mota N, Tófoli LF. LSD and language: Decreased structural connectivity, increased semantic similarity, changed vocabulary in healthy individuals. Eur Neuropsychopharmacol 2023; 68:89-104. [PMID: 36669231 DOI: 10.1016/j.euroneuro.2022.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 01/20/2023]
Abstract
Language has been explored as a window into the mind. Psychedelics, known to affect perception and cognition, seem to change language, but a systematic, time-dependent exploration is lacking. Therefore, we aimed at mapping the psychedelic effects on language over the time course of the acute and sub-acute effects in an explorative manner. For this, 24 healthy volunteers (age [mean±SD, range]: 35±11, 25-61 years; 33% women) received 50 μg lysergic acid diethylamide (LSD) or inactive placebo in a randomized, double-blind, placebo-controlled, crossover study. We assessed different language productions (experience reporting, storytelling), components (structure, semantics, vocabulary) and time points (+0 h to +24 h). Language productions included 5-min experience reporting (+1.5 h, +6.5 h) and 1-min storytelling (+0 h, +2 h, +4 h, +6 h, +24 h). Language structure was assessed by computing speech topology (SpeechGraphs), semantics by semantic distances (FastText), vocabulary by word categories (LIWC). LSD, compared to placebo, changed language structure, including decreased verbosity, lexicon, global and local connectivity (+1.5 h to +4 h); decreased semantic distances between neighbouring words and overall words (+2 h to +24 h); and changed vocabulary related to grammar, persons, time, space and biological processes (+1.5 h to +24 h). In conclusion, low to moderate LSD doses changed language over diverse production types, components and time points. While simpler and disconnected structure and semantic similarity might reflect cognitive impairments, changed vocabulary might reflect subjective perceptions. Therefore, language under LSD might provide a window into the psychedelic mind and automated language quantifications should be better explored as valuable tools to yield more unconstrained insights into psychedelic perception and cognition.
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Affiliation(s)
- Isabel Wießner
- Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas, Rua Tessália Vieira de Camargo 126, Cidade Universitária Zeferino Vaz, 13083-887, Campinas, São Paulo, Brazil; Brain Institute, Federal University of Rio Grande do Norte, Av. Senador Salgado Filho 3000, Lagoa Nova, 59078-900, Natal, Rio Grande do Norte, Brazil.
| | - Marcelo Falchi
- Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas, Rua Tessália Vieira de Camargo 126, Cidade Universitária Zeferino Vaz, 13083-887, Campinas, São Paulo, Brazil; Brain Institute, Federal University of Rio Grande do Norte, Av. Senador Salgado Filho 3000, Lagoa Nova, 59078-900, Natal, Rio Grande do Norte, Brazil
| | - Dimitri Daldegan-Bueno
- Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas, Rua Tessália Vieira de Camargo 126, Cidade Universitária Zeferino Vaz, 13083-887, Campinas, São Paulo, Brazil; Centre for Applied Research in Mental Health and Addiction, Faculty of Health Sciences, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Fernanda Palhano-Fontes
- Brain Institute, Federal University of Rio Grande do Norte, Av. Senador Salgado Filho 3000, Lagoa Nova, 59078-900, Natal, Rio Grande do Norte, Brazil
| | - Rodolfo Olivieri
- Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas, Rua Tessália Vieira de Camargo 126, Cidade Universitária Zeferino Vaz, 13083-887, Campinas, São Paulo, Brazil
| | - Amanda Feilding
- The Beckley Foundation, Beckley Park, Oxford, United Kingdom
| | - Draulio B Araujo
- Brain Institute, Federal University of Rio Grande do Norte, Av. Senador Salgado Filho 3000, Lagoa Nova, 59078-900, Natal, Rio Grande do Norte, Brazil
| | - Sidarta Ribeiro
- Brain Institute, Federal University of Rio Grande do Norte, Av. Senador Salgado Filho 3000, Lagoa Nova, 59078-900, Natal, Rio Grande do Norte, Brazil
| | - Natália Bezerra Mota
- Department of Psychiatry and Forensic Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Luís Fernando Tófoli
- Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas, Rua Tessália Vieira de Camargo 126, Cidade Universitária Zeferino Vaz, 13083-887, Campinas, São Paulo, Brazil.
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14
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Castro Martínez JC, Santamaría-García H. Understanding mental health through computers: An introduction to computational psychiatry. Front Psychiatry 2023; 14:1092471. [PMID: 36824671 PMCID: PMC9941647 DOI: 10.3389/fpsyt.2023.1092471] [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: 11/08/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.
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Affiliation(s)
- Juan Camilo Castro Martínez
- Departamento de Psiquiatría y Salud Mental, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Hernando Santamaría-García
- Ph.D. Programa de Neurociencias, Departamento de Psiquiatría y Salud Mental, Pontificia Universidad Javeriana, Bogotá, Colombia
- Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Global Brain Health Institute, University of California, San Francisco – Trinity College Dublin, San Francisco, CA, United States
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15
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Minor KS, Lundin NB, Myers EJ, Fernández-Villardón A, Lysaker PH. Automated measures of speech content and speech organization in schizophrenia: Test-retest reliability and generalizability across demographic variables. Psychiatry Res 2023; 320:115048. [PMID: 36645988 DOI: 10.1016/j.psychres.2023.115048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023]
Abstract
Technological advances in artificial intelligence and natural language processing have increased efficiency of assessing speech content and speech organization in schizophrenia. Despite these developments, there has been little focus on the psychometrics of these approaches. Using two common assessments, the current study addressed this gap by: 1) measuring test-retest reliability; and 2) assessing whether speech content and/or speech organization generalize across demographics. To test these aims, we examined psychometric properties of the Linguistic Inquiry Word Count (LIWC), a speech content measure, and the Coh-Metrix, a speech organization measure. Across baseline to six month (n = 101) and baseline to one year (n = 47) narrative speech samples, we generally observed fair reliability for speech content measures and fair to good reliability for speech organization measures. Regarding demographics, multiple speech indices varied by race, income, and education. The lack of excellent reliability scores for speech indices holds important implications for examining speech variables in clinical trials and highlights the dynamic nature of speech. This work illustrates the importance of designing speech content and speech organization measures with external validity across demographic factors. Future studies examining speech in schizophrenia should account for potential biases against demographic groups introduced by linguistic analysis tools.
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Affiliation(s)
- Kyle S Minor
- Department of Psychology, Indiana University- Purdue University Indianapolis, Indianapolis, IN, United States.
| | - Nancy B Lundin
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, United States
| | - Evan J Myers
- Department of Psychology, Indiana University- Purdue University Indianapolis, Indianapolis, IN, United States
| | | | - Paul H Lysaker
- Roudebush VA Medical Center, Indianapolis, IN, United States; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
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16
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Mota NB. How can computational tools help to understand language patterns in mental suffering considering social diversity. Psychiatry Res 2023; 319:114995. [PMID: 36495617 DOI: 10.1016/j.psychres.2022.114995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/20/2022] [Accepted: 11/27/2022] [Indexed: 11/30/2022]
Abstract
The complex interaction between biological and social factors challenges measuring human behavior. Language has been a crucial source of information that mirrors inner processes like thoughts. The development of a novel computational strategy that helps to understand language needs to consider social factors that could also impact human behavior. Ten years ago, I developed a computational approach based on graph theory to measure structural aspects of the narrative's mental organization expressed in spontaneous oral reports. It was possible to measure the decrease in narrative graph connectedness associated with the schizophrenia diagnosis and negative symptoms severity. However, I was worried that the psychiatric field neglected factors from diverse social realities (such as poor access to education). Formal education impacts language by mastering grammar and syntax. Changes in language structure could be related to symptoms and lack of exposure to formal education. Indeed, the same connectedness markers increase according to typical cognitive and academic development. In this paper, I describe the reasons and methods for investigating both factors (psychiatric symptoms and formal education) on language patterns. Further, I evaluate concerns and future challenges of using computational strategies that include social diversity in mental health conditions.
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Affiliation(s)
- Natália Bezerra Mota
- Institute of Psychiatry at Federal University of Rio de Janeiro - IPUB/UFRJ, Rio de Janeiro, Brazil; Research department at Motrix Lab - Motrix, Rio de Janeiro, Brazil.
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17
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Kapitány-Fövény M. A commentary on the interpretability of computational linguistic findings in schizophrenia research. Schizophr Res 2022; 250:60-61. [PMID: 36368278 DOI: 10.1016/j.schres.2022.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 08/24/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Máté Kapitány-Fövény
- Faculty of Health Sciences, Semmelweis University, Vas utca 17., H-1088 Budapest, Hungary; National Institute of Mental Health, Neurology and Neurosurgery - Nyírő Gyula Hospital, Lehel utca 59., H-1135 Budapest, Hungary.
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18
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Botezatu MR, Weissheimer J, Ribeiro M, Guo T, Finger I, Mota NB. Graph structure analysis of speech production among second language learners of Spanish and Chinese. Front Psychol 2022; 13:940269. [PMID: 36160589 PMCID: PMC9496641 DOI: 10.3389/fpsyg.2022.940269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
Language experience shapes the gradual maturation of speech production in both native (L1) and second (L2) languages. Structural aspects like the connectedness of spontaneous narratives reveal this maturation progress in L1 acquisition and, as it does not rely on semantics, it could also reveal structural pattern changes during L2 acquisition. The current study tested whether L2 lexical retrieval associated with vocabulary knowledge could impact the global connectedness of narratives during the initial stages of L2 acquisition. Specifically, the study evaluated the relationship between graph structure (long-range recurrence or connectedness) and L2 learners’ oral production in the L2 and L1. Seventy-nine college-aged students who were native speakers of English and had received classroom instruction in either L2-Spanish or L2-Chinese participated in this study. Three tasks were used: semantic fluency, phonemic fluency and picture description. Measures were operationalized as the number of words per minute in the case of the semantic and phonemic fluency tasks. Graph analysis was carried out for the picture description task using the computational tool SpeechGraphs to calculate connectedness. Results revealed significant positive correlations between connectedness in the picture description task and measures of speech production (number of correct responses per minute) in the phonemic and semantic fluency tasks. These correlations were only significant for the participants’ L2- Spanish and Chinese. Results indicate that producing low connectedness narratives in L2 may be a marker of the initial stages of L2 oral development. These findings are consistent with the pattern reported in the early stages of L1 literacy. Future studies should further explore the interactions between graph structure and second language production proficiency, including more advanced stages of L2 learning and considering the role of cognitive abilities in this process.
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Affiliation(s)
- Mona Roxana Botezatu
- Department of Speech, Language and Hearing Sciences, University of Missouri, Columbia, MO, United States
- *Correspondence: Mona Roxana Botezatu,
| | | | - Marina Ribeiro
- Research Department at Motrix Lab, Motrix, Rio de Janeiro, Brazil
| | - Taomei Guo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ingrid Finger
- Department of Modern Languages, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Natalia Bezerra Mota
- Research Department at Motrix Lab, Motrix, Rio de Janeiro, Brazil
- Department of Psychiatry and Legal Medicine, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
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Brain gyrification in bipolar disorder: a systematic review of neuroimaging studies. Brain Imaging Behav 2022; 16:2768-2784. [PMID: 36042153 DOI: 10.1007/s11682-022-00713-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/04/2022] [Indexed: 11/02/2022]
Abstract
Bipolar disorder (BD) is a severe mental illness with a strong genetic component. Genetic variations have been involved in the risk of this disorder, including those mediating brain function and neurodevelopment. Early neurodevelopment and neuroprogression processes could be reflected in brain gyrification patterns and help optimize the prediction and diagnosis of such disorders that is often delayed. Previous neuroimaging studies using this measure in patients with bipolar disorder revealed controversial results. This systematic review aimed to summarize available neuroimaging investigations on gyrification in BD compared to healthy controls (HC) and/or other psychiatric groups. Fourteen studies including 733 patients with BD, 585 patients with schizophrenia (SCZ), 90 with schizoaffective disorder (SZA), and 1380 healthy subjects were identified. Overall, a heterogeneous pattern of gyrification emerged between patients with BD and HC. Interestingly, increased gyrification or no differences were also observed in patients with BD compared to those with the schizophrenia-spectrum disorders. Furthermore, relatives of patients with BD showed lower or no differences in gyrification compared to healthy subjects without a family history of affective illness. Differences in the design and in methodological approaches could have contributed to the heterogeneity of the findings. The current review supports an altered brain gyrification pattern that underlies the pathophysiology of BD spanning large anatomical and functional neural networks, associated with altered cognitive functioning, difficulties in processing and affective regulation, and clinical symptoms. Longitudinal studies are needed to test different bipolar phenotypes and pharmacological effects on gyrification.
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Liang L, Silva AM, Jeon P, Ford SD, MacKinley M, Théberge J, Palaniyappan L. Widespread cortical thinning, excessive glutamate and impaired linguistic functioning in schizophrenia: A cluster analytic approach. Front Hum Neurosci 2022; 16:954898. [PMID: 35992940 PMCID: PMC9390601 DOI: 10.3389/fnhum.2022.954898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Symptoms of schizophrenia are closely related to aberrant language comprehension and production. Macroscopic brain changes seen in some patients with schizophrenia are suspected to relate to impaired language production, but this is yet to be reliably characterized. Since heterogeneity in language dysfunctions, as well as brain structure, is suspected in schizophrenia, we aimed to first seek patient subgroups with different neurobiological signatures and then quantify linguistic indices that capture the symptoms of "negative formal thought disorder" (i.e., fluency, cohesion, and complexity of language production). Methods Atlas-based cortical thickness values (obtained with a 7T MRI scanner) of 66 patients with first-episode psychosis and 36 healthy controls were analyzed with hierarchical clustering algorithms to produce neuroanatomical subtypes. We then examined the generated subtypes and investigated the quantitative differences in MRS-based glutamate levels [in the dorsal anterior cingulate cortex (dACC)] as well as in three aspects of language production features: fluency, syntactic complexity, and lexical cohesion. Results Two neuroanatomical subtypes among patients were observed, one with near-normal cortical thickness patterns while the other with widespread cortical thinning. Compared to the subgroup of patients with relatively normal cortical thickness patterns, the subgroup with widespread cortical thinning was older, with higher glutamate concentration in dACC and produced speech with reduced mean length of T-units (complexity) and lower repeats of content words (lexical cohesion), despite being equally fluent (number of words). Conclusion We characterized a patient subgroup with thinner cortex in first-episode psychosis. This subgroup, identifiable through macroscopic changes, is also distinguishable in terms of neurochemistry (frontal glutamate) and language behavior (complexity and cohesion of speech). This study supports the hypothesis that glutamate-mediated cortical thinning may contribute to a phenotype that is detectable using the tools of computational linguistics in schizophrenia.
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Affiliation(s)
- Liangbing Liang
- Graduate Program in Neuroscience, Western University, London, ON, Canada
- Robarts Research Institute, Western University, London, ON, Canada
| | | | - Peter Jeon
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Sabrina D. Ford
- Robarts Research Institute, Western University, London, ON, Canada
- London Health Sciences Centre, Victoria Hospital, London, ON, Canada
| | - Michael MacKinley
- Robarts Research Institute, Western University, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
| | - Jean Théberge
- Department of Medical Biophysics, Western University, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
- Department of Psychiatry, Western University, London, ON, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
- Department of Psychiatry, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
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21
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Who does what to whom? graph representations of action-predication in speech relate to psychopathological dimensions of psychosis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:58. [PMID: 35853912 PMCID: PMC9261087 DOI: 10.1038/s41537-022-00263-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/01/2022] [Indexed: 11/09/2022]
Abstract
Graphical representations of speech generate powerful computational measures related to psychosis. Previous studies have mostly relied on structural relations between words as the basis of graph formation, i.e., connecting each word to the next in a sequence of words. Here, we introduced a method of graph formation grounded in semantic relationships by identifying elements that act upon each other (action relation) and the contents of those actions (predication relation). Speech from picture descriptions and open-ended narrative tasks were collected from a cross-diagnostic group of healthy volunteers and people with psychotic or non-psychotic disorders. Recordings were transcribed and underwent automated language processing, including semantic role labeling to identify action and predication relations. Structural and semantic graph features were computed using static and dynamic (moving-window) techniques. Compared to structural graphs, semantic graphs were more strongly correlated with dimensional psychosis symptoms. Dynamic features also outperformed static features, and samples from picture descriptions yielded larger effect sizes than narrative responses for psychosis diagnoses and symptom dimensions. Overall, semantic graphs captured unique and clinically meaningful information about psychosis and related symptom dimensions. These features, particularly when derived from semi-structured tasks using dynamic measurement, are meaningful additions to the repertoire of computational linguistic methods in psychiatry.
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22
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Malcorra BLC, Mota NB, Weissheimer J, Schilling LP, Wilson MA, Hübner LC. Reading and writing habits compensate for aging effects in speech connectedness. NPJ SCIENCE OF LEARNING 2022; 7:13. [PMID: 35676305 PMCID: PMC9178018 DOI: 10.1038/s41539-022-00129-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 05/18/2022] [Indexed: 05/06/2023]
Abstract
We investigate the association of short- and long-range recurrences (speech connectedness) with age, education, and reading and writing habits (RWH) in typical aging using an oral narrative production task. Oral narrative transcriptions were represented as word-graphs to measure short- and long-range recurrences. Speech connectedness was explained by the combination of age, education, and RWH, and the strength of RWH's coefficient reflects the aging effect.
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Affiliation(s)
- Bárbara L C Malcorra
- School of Humanities, Graduate Course in Linguistics, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil.
| | - Natália B Mota
- Institute of Psychiatry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil
- Department of Physics, Federal University of Pernambuco (UFPE), Recife, PE, Brazil
| | - Janaina Weissheimer
- Brain Institute, Department of Foreign Languages and Literatures, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil
- National Council for Scientific and Technological Development (CNPq), Brasília, DF, Brazil
| | - Lucas P Schilling
- School of Medicine, Graduate Course in Medicine and Healthy Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- School of Medicine, Graduate Course in Biomedical Gerontology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- Brain Institute of Rio Grande do Sul (InsCer), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Maximiliano A Wilson
- Centre interdisciplinaire de recherche en réadaptation et intégration sociale (CIRRIS), Département de réadaptation, Université Laval, Québec City, QC, Canada
| | - Lilian C Hübner
- School of Humanities, Graduate Course in Linguistics, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- National Council for Scientific and Technological Development (CNPq), Brasília, DF, Brazil
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23
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Mota NB, Pimenta J, Tavares M, Palmeira L, Loch AA, Hedin-Pereira C, Dias EC. A Brazilian bottom-up strategy to address mental health in a diverse population over a large territorial area - an inspiration for the use of digital mental health. Psychiatry Res 2022; 311:114477. [PMID: 35245744 DOI: 10.1016/j.psychres.2022.114477] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 12/12/2021] [Accepted: 02/21/2022] [Indexed: 01/01/2023]
Abstract
Brazil is a continental country with a history of massive immigration waves from around the world. Consequently, the Brazilian population is rich in ethnic, cultural, and religious diversity, but suffers from tremendous socioeconomic inequality. Brazil has a documented history of categorizing individuals with culturally specific behaviors as mentally ill, which has led to psychiatric institutionalization for reasons that were more social than clinical. To address this, a "network for psychosocial care" was created in Brazil, that included mental health clinics and community services distributed throughout the country. This generates local support for mental health rehabilitation, integrating psychiatric care, family support and education/work opportunities. These clinics and community services are tailored to provide care for each specific area, and are more attuned to regional culture, values and neighborhood infrastructure. Here we review existing reports about the Brazilian experience, including advances in public policy on mental health, and challenges posed by the large diversity to the psychosocial rehabilitation. In addition, we show how new digital technologies in general, and computational speech analysis in particular, can contribute to unbiased assessments, resulting in decreased stigma and more effective diagnosis of the mental diseases, with methods that are free of gender, ethnic, or socioeconomic biases.
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Affiliation(s)
- Natália Bezerra Mota
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil; Departamento de Física, Universidade Federal de Pernambuco, Recife, Brazil.
| | - Juliana Pimenta
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Maria Tavares
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Leonardo Palmeira
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Alexandre Andrade Loch
- Laboratorio de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Cientifico e Tecnológico, Brazil
| | - Cecília Hedin-Pereira
- Vice-Presidência de Pesquisa e Coleções Biológicas (VPPCB), Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Elisa C Dias
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
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24
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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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25
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Kircher T, Stein F, Nagels A. Differences in single positive formal thought disorder symptoms between closely matched acute patients with schizophrenia and mania. Eur Arch Psychiatry Clin Neurosci 2022; 272:395-401. [PMID: 33961098 PMCID: PMC8938354 DOI: 10.1007/s00406-021-01263-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/20/2021] [Indexed: 12/01/2022]
Abstract
Formal thought disorders (FTD) are a hallmark diagnostic feature of schizophrenia (SZ) and (bipolar) mania (MA). FTD can be separated into positive (pFTD) and negative dimensions. It is unclear whether there are differences in pFTD on a single symptom level between acutely ill patients with SZ and MA, which cannot be attributed to cognitive impairment. We compared single pFTD symptoms in two groups of acutely ill patients with ICD-10 bipolar mania and schizophrenia, closely matched for age, sex, pFTD TALD score, verbal IQ and neuropsychological test performance (executive function, verbal fluency, attention, and working memory). SZ patients had higher severity of the TALD symptoms "perseverations" and "poverty of content of speech" than those with MA (Mann-Whitney U, significant, Bonferroni corrected). Speech in acute SZ patients differs from MA in that it conveys little information and adheres to previously mentioned ideas and topics. Matching for confounding variables, such as IQ and cognition, is important when comparing patients with different diagnoses.
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Affiliation(s)
- Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany. .,Center for Mind, Brain and Behaviour, University of Marburg, Marburg, Germany.
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany ,Center for Mind, Brain and Behaviour, University of Marburg, Marburg, Germany
| | - Arne Nagels
- Department of English and Linguistic, Johannes Gutenberg-University Mainz, Mainz, Germany
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26
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Mota NB. Commentary on "Investigating the diagnostic utility of speech patterns in schizophrenia and their symptom associations": The current need for the harmonization of speech elicitation protocols in basic and applied science. Schizophr Res 2021; 238:199-200. [PMID: 34798501 DOI: 10.1016/j.schres.2021.11.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 11/11/2021] [Indexed: 10/19/2022]
Affiliation(s)
- Natália Bezerra Mota
- Institute of Psychiatry at Federal University of Rio de Janeiro-IPUB/UFRJ, Rio de Janeiro, Brazil; Department of Physics at Federal University of Pernambuco-UFPE, Recife, Brazil.
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27
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Ziv I, Baram H, Bar K, Zilberstein V, Itzikowitz S, Harel EV, Dershowitz N. Morphological characteristics of spoken language in schizophrenia patients - an exploratory study. Scand J Psychol 2021; 63:91-99. [PMID: 34813111 DOI: 10.1111/sjop.12790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/24/2021] [Accepted: 10/26/2021] [Indexed: 11/28/2022]
Abstract
Psychosis is diagnosed based on disruptions in the structure and use of language, including reduced syntactic complexity, derailment, and tangentiality. With the development of computational analysis, natural language processing (NLP) techniques are used in many areas of life to make evaluations and inferences regarding people's thoughts, feelings and behavior. The present study explores morphological characteristic of schizophrenia inpatients using NLP. Transcripts of recorded stories by 49 male subjects (24 inpatients diagnosed with schizophrenia and 25 controls) about 14 Thematic Apperception Test (TAT) pictures were morphologically analyzed. Relative to the control group, the schizophrenic inpatients employed: (1) a similar ratio of nouns, but fewer verbs, adjectives and adverbs; (2) a higher ratio of lemmas to token (LTR) and type to token (TTR); (3) a smaller gap between LTR and TTR; and (4) greater use of the first person. The results were cross-verified using three well-known fitting classifier algorithms (Random Forest, XGBoost and a support vector machine). Tests of prediction accuracy, precision and recall found correct attribution of patients to the schizophrenia group at a rate of between 80 and 90%. Overall, the results suggest that the language of schizophrenic inpatients is significantly different from that of healthy controls, being morphologically less complex, more associative and more focused on the self. The findings support NLP analysis as a complementary addition to the traditional clinical psychosis evaluation for schizophrenia.
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Affiliation(s)
- Ido Ziv
- Psychology Department, The College of Management - Academic Studies, Rishon LeZion, Israel
| | - Heli Baram
- Psychology Department, Ruppin Academic Center, Ruppin, Israel
| | - Kfir Bar
- School of Computer Science, The College of Management - Academic Studies, Rishon LeZion, Israel
| | | | - Samuel Itzikowitz
- School of Computer Science, The College of Management - Academic Studies, Rishon LeZion, Israel
| | - Eran V Harel
- Be'er Ya'akov Medical Center for Mental Health, Be'er Ya'akov, Israel
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28
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Voppel AE, de Boer JN, Brederoo SG, Schnack HG, Sommer I. Quantified language connectedness in schizophrenia-spectrum disorders. Psychiatry Res 2021; 304:114130. [PMID: 34332431 DOI: 10.1016/j.psychres.2021.114130] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/13/2021] [Accepted: 07/16/2021] [Indexed: 01/02/2023]
Abstract
Language abnormalities are a core symptom of schizophrenia-spectrum disorders and could serve as a potential diagnostic marker. Natural language processing enables quantification of language connectedness, which may be lower in schizophrenia-spectrum disorders. Here, we investigated connectedness of spontaneous speech in schizophrenia-spectrum patients and controls and determine its accuracy in classification. Using a semi-structured interview, speech of 50 patients with a schizophrenia-spectrum disorder and 50 controls was recorded. Language connectedness in a semantic word2vec model was calculated using consecutive word similarity in moving windows of increasing sizes (2-20 words). Mean, minimal and variance of similarity were calculated per window size and used in a random forest classifier to distinguish patients and healthy controls. Classification based on connectedness reached 85% cross-validated accuracy, with 84% specificity and 86% sensitivity. Features that best discriminated patients from controls were variance of similarity at window sizes between 5 and 10. We show impaired connectedness in spontaneous speech of patients with schizophrenia-spectrum disorders even in patients with low ratings of positive symptoms. Effects were most prominent at the level of sentence connectedness. The high sensitivity, specificity and tolerability of this method show that language analysis is an accurate and feasible digital assistant in diagnosing schizophrenia-spectrum disorders.
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Affiliation(s)
- A E Voppel
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
| | - J N de Boer
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - S G Brederoo
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - H G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Utrecht University, Utrecht Institute of Linguistics OTS, Utrecht, the Netherlands
| | - Iec Sommer
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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29
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Palaniyappan L. Dissecting the neurobiology of linguistic disorganisation and impoverishment in schizophrenia. Semin Cell Dev Biol 2021; 129:47-60. [PMID: 34507903 DOI: 10.1016/j.semcdb.2021.08.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 08/13/2021] [Accepted: 05/06/2021] [Indexed: 12/16/2022]
Abstract
Schizophrenia provides a quintessential disease model of how disturbances in the molecular mechanisms of neurodevelopment lead to disruptions in the emergence of cognition. The central and often persistent feature of this illness is the disorganisation and impoverishment of language and related expressive behaviours. Though clinically more prominent, the periodic perceptual distortions characterised as psychosis are non-specific and often episodic. While several insights into psychosis have been gained based on study of the dopaminergic system, the mechanistic basis of linguistic disorganisation and impoverishment is still elusive. Key findings from cellular to systems-level studies highlight the role of ubiquitous, inhibitory processes in language production. Dysregulation of these processes at critical time periods, in key brain areas, provides a surprisingly parsimonious account of linguistic disorganisation and impoverishment in schizophrenia. This review links the notion of excitatory/inhibitory (E/I) imbalance at cortical microcircuits to the expression of language behaviour characteristic of schizophrenia, through the building blocks of neurochemistry, neurophysiology, and neurocognition.
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Affiliation(s)
- Lena Palaniyappan
- Department of Psychiatry,University of Western Ontario, London, Ontario, Canada; Robarts Research Institute,University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada.
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30
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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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31
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Malcorra BLC, Mota NB, Weissheimer J, Schilling LP, Wilson MA, Hübner LC. Low Speech Connectedness in Alzheimer's Disease is Associated with Poorer Semantic Memory Performance. J Alzheimers Dis 2021; 82:905-912. [PMID: 34120904 DOI: 10.3233/jad-210134] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Connected speech is an everyday activity. We aimed to investigate whether connected speech can differentiate oral narrative production between adults with Alzheimer's disease (AD; n = 24) and cognitively healthy older adults (n = 48). We used graph attributes analysis to represent connected speech. Participants produced oral narratives and performed semantic, episodic, and working memory tasks. AD patients produced less connected narratives than cognitively healthy older adults. Connectedness was associated with semantic memory in AD and with episodic memory in controls. Word-graphs connectedness represents a practical tool to assess cognitive impairment in AD patients.
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Affiliation(s)
- Bárbara Luzia Covatti Malcorra
- School of Humanities, Graduate Course in Linguistics, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Natália Bezerra Mota
- Department of Physics, Federal University of Pernambuco (UFPE), Recife, PE, Brazil
| | - Janaina Weissheimer
- Brain Institute, Department of Foreign Languages and Literatures, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil; CNPq - National Council for Scientific and Technological Development - Brasília, DF, Brazil
| | - Lucas Porcello Schilling
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS); Brain Institute of Rio Grande do Sul, Pontifical Catholic University of Rio Grande do Sul (InsCer - PUCRS) - Porto Alegre, RS, Brazil; Neurology Service, São Lucas Hospital, Pontifical Catholic University of Rio Grande do Sul (PUCRS) - Porto Alegre, RS, Brazil
| | | | - Lilian Cristine Hübner
- School of Humanities, Graduate Course in Linguistics, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil; CNPq - National Council for Scientific and Technological Development - Brasília, DF, Brazil
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32
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Coelho RM, Drummond C, Mota NB, Erthal P, Bernardes G, Lima G, Molina R, Sudo FK, Tannock R, Mattos P. Network analysis of narrative discourse and attention-deficit hyperactivity symptoms in adults. PLoS One 2021; 16:e0245113. [PMID: 33826632 PMCID: PMC8026017 DOI: 10.1371/journal.pone.0245113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 12/23/2020] [Indexed: 11/18/2022] Open
Abstract
Previous research investigating language in attention-deficit hyperactivity disorder (ADHD) has demonstrated several deficits in many aspects. However, no previous study employed quantitative methodology providing objective measures that could be compared among different studies with diverse samples. To fill this gap, we used network analysis to investigate how ADHD symptomatology impacts narrative discourse, a complex linguistic task considered to be an ecological measure of language. Fifty-eight adults (34 females and 24 males) with a mean age of 26 years old and a mean of 17 years of educational level were administered the Adult Self-Rating Scale for ADHD symptomatology. They also completed the State-Trait Anxiety Inventory, the Beck Depression Inventory and the Urgency, Premeditation, Perseverance, Sensation Seeking Behavior Scale. Intelligence quotient was calculated. Individuals were asked to tell a story based on a wordless picture book. Speech was recorded and transcribed as an input to SpeechGraphs software. Parameters were total number of words (TNW), number of loops of one node (L1), repeated edges (RE), largest strongly connected component (LSC) and average shortest path (ASP). Verbosity was controlled. Statistical analysis was corrected for multiples comparisons and partial correlations were performed for confounding variables. After controlling for anxiety, depression, IQ, and impulsiveness ADHD symptomatology was positively correlated with L1 and negatively correlated with LSC. TNW was positively correlated with ADHD symptoms. In a subdomain analysis, both inattention and hyperactivity-impulsivity were negatively correlated with LSC. Only hyperactivity-impulsivity positively correlated with TNW and L1. Results indicated a correlation between ADHD symptoms and lower connectedness in narrative discourse (as indicated by higher L1 and lower LSC), as well as higher total number of words (TNW). Our results suggest that the higher the number of ADHD symptoms, the less connectivity among words, and a higher number of words in narrative discourse.
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Affiliation(s)
| | - Cláudia Drummond
- Institute D'Or for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil.,Department of speech and hearing pathology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil
| | | | - Pilar Erthal
- Institute D'Or for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Gabriel Bernardes
- Institute D'Or for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Gabriel Lima
- Institute D'Or for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Raquel Molina
- Institute D'Or for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Felipe Kenji Sudo
- Institute D'Or for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Rosemary Tannock
- Research Institute, The Hospital for Sick Children, Toronto, Canada
| | - Paulo Mattos
- Institute D'Or for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil.,Institute of Psychiatry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil
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33
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Sasabayashi D, Takahashi T, Takayanagi Y, Suzuki M. Anomalous brain gyrification patterns in major psychiatric disorders: a systematic review and transdiagnostic integration. Transl Psychiatry 2021; 11:176. [PMID: 33731700 PMCID: PMC7969935 DOI: 10.1038/s41398-021-01297-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 02/14/2021] [Accepted: 02/24/2021] [Indexed: 01/31/2023] Open
Abstract
Anomalous patterns of brain gyrification have been reported in major psychiatric disorders, presumably reflecting their neurodevelopmental pathology. However, previous reports presented conflicting results of patients having hyper-, hypo-, or normal gyrification patterns and lacking in transdiagnostic consideration. In this article, we systematically review previous magnetic resonance imaging studies of brain gyrification in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder at varying illness stages, highlighting the gyral pattern trajectory for each disorder. Patients with each psychiatric disorder may exhibit deviated primary gyri formation under neurodevelopmental genetic control in their fetal life and infancy, and then exhibit higher-order gyral changes due to mechanical stress from active brain changes (e.g., progressive reduction of gray matter volume and white matter integrity) thereafter, representing diversely altered pattern trajectories from those of healthy controls. Based on the patterns of local connectivity and changes in neurodevelopmental gene expression in major psychiatric disorders, we propose an overarching model that spans the diagnoses to explain how deviated gyral pattern trajectories map onto clinical manifestations (e.g., psychosis, mood dysregulation, and cognitive impairments), focusing on the common and distinct gyral pattern changes across the disorders in addition to their correlations with specific clinical features. This comprehensive understanding of the role of brain gyrification pattern on the pathophysiology may help to optimize the prediction and diagnosis of psychiatric disorders using objective biomarkers, as well as provide a novel nosology informed by neural circuits beyond the current descriptive diagnostics.
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Affiliation(s)
- Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan. .,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan.
| | - Tsutomu Takahashi
- grid.267346.20000 0001 2171 836XDepartment of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan ,grid.267346.20000 0001 2171 836XResearch Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yoichiro Takayanagi
- grid.267346.20000 0001 2171 836XDepartment of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan ,Arisawabashi Hospital, Toyama, Japan
| | - Michio Suzuki
- grid.267346.20000 0001 2171 836XDepartment of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan ,grid.267346.20000 0001 2171 836XResearch Center for Idling Brain Science, University of Toyama, Toyama, Japan
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Del Re EC, Stone WS, Bouix S, Seitz J, Zeng V, Guliano A, Somes N, Zhang T, Reid B, Lyall A, Lyons M, Li H, Whitfield-Gabrieli S, Keshavan M, Seidman LJ, McCarley RW, Wang J, Tang Y, Shenton ME, Niznikiewicz MA. Baseline Cortical Thickness Reductions in Clinical High Risk for Psychosis: Brain Regions Associated with Conversion to Psychosis Versus Non-Conversion as Assessed at One-Year Follow-Up in the Shanghai-At-Risk-for-Psychosis (SHARP) Study. Schizophr Bull 2021; 47:562-574. [PMID: 32926141 PMCID: PMC8480195 DOI: 10.1093/schbul/sbaa127] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To assess cortical thickness (CT) and surface area (SA) of frontal, temporal, and parietal brain regions in a large clinical high risk for psychosis (CHR) sample, and to identify cortical brain abnormalities in CHR who convert to psychosis and in the whole CHR sample, compared with the healthy controls (HC). METHODS Magnetic resonance imaging, clinical, and cognitive data were acquired at baseline in 92 HC, 130 non-converters, and 22 converters (conversion assessed at 1-year follow-up). CT and SA at baseline were calculated for frontal, temporal, and parietal subregions. Correlations between regions showing group differences and clinical scores and age were also obtained. RESULTS CT but not SA was significantly reduced in CHR compared with HC. Two patterns of findings emerged: (1) In converters, CT was significantly reduced relative to non-converters and controls in the banks of superior temporal sulcus, Heschl's gyrus, and pars triangularis and (2) CT in the inferior parietal and supramarginal gyrus, and at trend level in the pars opercularis, fusiform, and middle temporal gyri was significantly reduced in all high-risk individuals compared with HC. Additionally, reduced CT correlated significantly with older age in HC and in non-converters but not in converters. CONCLUSIONS These results show for the first time that fronto-temporo-parietal abnormalities characterized all CHR, that is, both converters and non-converters, relative to HC, while CT abnormalities in converters relative to CHR-NC and HC were found in core auditory and language processing regions.
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Affiliation(s)
- Elisabetta C Del Re
- Laboratory of Neuroscience, Department of Psychiatry, VA Boston
Healthcare System, Brockton Division, and Harvard Medical School,
Boston, MA
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham
and Women’s Hospital, and Harvard Medical School, Boston,
MA
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, MA
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham
and Women’s Hospital, and Harvard Medical School, Boston,
MA
| | - Johanna Seitz
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham
and Women’s Hospital, and Harvard Medical School, Boston,
MA
| | - Victor Zeng
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, MA
| | - Anthony Guliano
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, MA
| | - Nathaniel Somes
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham
and Women’s Hospital, and Harvard Medical School, Boston,
MA
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of
Medicine, Shanghai Key Laboratory of Psychotic Disorders, SHARP
Program, Shanghai China
| | - Benjamin Reid
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham
and Women’s Hospital, and Harvard Medical School, Boston,
MA
| | - Amanda Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham
and Women’s Hospital, and Harvard Medical School, Boston,
MA
- Department of Psychiatry, Massachusetts General Hospital and Harvard
Medical School, Boston, MA
| | - Monica Lyons
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham
and Women’s Hospital, and Harvard Medical School, Boston,
MA
- Department of Psychiatry, Massachusetts General Hospital and Harvard
Medical School, Boston, MA
| | - Huijun Li
- Florida A&M University, Department of Psychology,
Tallahassee, FL
| | | | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, MA
| | - Larry J Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital and Harvard
Medical School, Boston, MA
| | - Robert W McCarley
- Laboratory of Neuroscience, Department of Psychiatry, VA Boston
Healthcare System, Brockton Division, and Harvard Medical School,
Boston, MA
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of
Medicine, Shanghai Key Laboratory of Psychotic Disorders, SHARP
Program, Shanghai China
| | - Yingying Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of
Medicine, Shanghai Key Laboratory of Psychotic Disorders, SHARP
Program, Shanghai China
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham
and Women’s Hospital, and Harvard Medical School, Boston,
MA
- Department of Psychiatry, Massachusetts General Hospital and Harvard
Medical School, Boston, MA
- Department of Radiology, Brigham and Women’s Hospital, and
Harvard Medical School, Boston, MA
- Research and Development, VA Boston Healthcare System,
Boston, MA
| | - Margaret A Niznikiewicz
- Laboratory of Neuroscience, Department of Psychiatry, VA Boston
Healthcare System, Brockton Division, and Harvard Medical School,
Boston, MA
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, MA
- To whom correspondence should be addressed; e-mail:
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Corcoran CM. Commentary on "Lower speech connectedness linked to incidence of psychosis in people at clinical high risk": The promise of graph theory and network neuroscience. Schizophr Res 2021; 228:481-482. [PMID: 33046332 PMCID: PMC7987843 DOI: 10.1016/j.schres.2020.09.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/26/2020] [Accepted: 09/26/2020] [Indexed: 10/23/2022]
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Busatto G, Rosa PG, Serpa MH, Squarzoni P, Duran FL. Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities. REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2021; 43:83-101. [PMID: 32520165 PMCID: PMC7861184 DOI: 10.1590/1516-4446-2019-0757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/03/2020] [Indexed: 11/23/2022]
Abstract
The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.
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Affiliation(s)
- Geraldo Busatto
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Pedro G. Rosa
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Mauricio H. Serpa
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Paula Squarzoni
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Fabio L. Duran
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
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Lower speech connectedness linked to incidence of psychosis in people at clinical high risk. Schizophr Res 2021; 228:493-501. [PMID: 32951966 DOI: 10.1016/j.schres.2020.09.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 05/29/2020] [Accepted: 09/07/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND Formal thought disorder is a cardinal feature of psychotic disorders, and is also evident in subtle forms before psychosis onset in individuals at clinical high-risk for psychosis (CHR-P). Assessing speech output or assessing expressive language with speech as the medium at this stage may be particularly useful in predicting later transition to psychosis. METHOD Speech samples were acquired through administration of the Thought and Language Index (TLI) in 24 CHR-P participants, 16 people with first-episode psychosis (FEP) and 13 healthy controls. The CHR-P individuals were then followed clinically for a mean of 7 years (s.d. = 1.5) to determine if they transitioned to psychosis. Non-semantic speech graph analysis was used to assess the connectedness of transcribed speech in all groups. RESULTS Speech was significantly more disconnected in the FEP group than in both healthy controls (p < .01) and the CHR-P group (p < .05). Results remained significant when IQ was included as a covariate. Significant correlations were found between speech connectedness measures and scores on the TLI, a manual assessment of formal thought disorder. In the CHR-P group, lower scores on two measures of speech connectedness were associated with subsequent transition to psychosis (8 transitions, 16 non-transitions; p < .05). CONCLUSION These findings support the utility and validity of speech graph analysis methods in characterizing speech connectedness in the early phases of psychosis. This approach has the potential to be developed into an automated, objective and time-efficient way of stratifying individuals at CHR-P according to level of psychosis risk.
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Silva A, Limongi R, MacKinley M, Palaniyappan L. Small Words That Matter: Linguistic Style and Conceptual Disorganization in Untreated First-Episode Schizophrenia. SCHIZOPHRENIA BULLETIN OPEN 2021; 2:sgab010. [PMID: 33937775 PMCID: PMC8072135 DOI: 10.1093/schizbullopen/sgab010] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This study aimed to shed light on the linguistic style affecting the communication discourse in first-episode schizophrenia (FES) by investigating the analytic thinking index in relation to clinical scores of conceptual and thought disorganization (Positive and Negative Syndrome Scale, PANSS-P2 and Thought and Language Index, TLI). Using robust Bayesian modeling, we report three major findings: (1) FES subjects showed reduced analytic thinking, exhibiting a less categorical linguistic style than healthy control (HC) subjects (Bayes factor, BF10 > 1000), despite using the same proportion of function and content words as HCs; (2) the lower the analytic thinking score, the higher the symptoms scores of conceptual disorganization (PANSS-P2, BF = 22.66) and global disorganization of thinking (TLI, BF10 = 112.73); (3) the linguistic style is a better predictor of conceptual disorganization than the cognitive measure of processing speed in schizophrenia (SZ). These findings provide an objectively detectable linguistic style with a focus on Natural Language Processing Analytics of transcribed speech samples of patients with SZ that require no clinical judgment. These findings also offer a crucial insight into the primacy of linguistic structural disruption in clinically ascertained disorganized thinking in SZ. Our work contributes to an emerging body of literature on the psychopathology of SZ using a first-order lexeme-level analysis and a hypothesis-driven approach. At a utilitarian level, this has implications for improving educational and social outcomes in patients with SZ.
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Affiliation(s)
| | - Roberto Limongi
- Robarts Research Institute, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, 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
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Corcoran CM, Mittal VA, Bearden CE, E Gur R, Hitczenko K, Bilgrami Z, Savic A, Cecchi GA, Wolff P. Language as a biomarker for psychosis: A natural language processing approach. Schizophr Res 2020; 226:158-166. [PMID: 32499162 PMCID: PMC7704556 DOI: 10.1016/j.schres.2020.04.032] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/22/2020] [Accepted: 04/24/2020] [Indexed: 12/21/2022]
Abstract
Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.
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Affiliation(s)
- Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, CA, USA; Department of Psychology, Semel Institute for Neuroscience and Human Behavior, Brain Research Institute, University of California Los Angeles, CA, USA; Department of Psychology, University of California Los Angeles, CA USA
| | - Raquel E Gur
- Brain Behavior Laboratory, Neuropsychiatry Division, Department of Psychiatry, Philadelphia, PA 19104, USA
| | - Kasia Hitczenko
- Department of Linguistics, Northwestern University, Evanston, IL, USA
| | - Zarina Bilgrami
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aleksandar Savic
- Department of Diagnostics and Intensive Care, University Psychiatric Hospital Vrapce, Zagreb, Croatia
| | - Guillermo A Cecchi
- Computational Biology Center-Neuroscience, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Phillip Wolff
- Department of Psychology, Emory University, Atlanta, GA, USA.
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Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion. PLoS One 2020; 15:e0242903. [PMID: 33253274 PMCID: PMC7703999 DOI: 10.1371/journal.pone.0242903] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 11/12/2020] [Indexed: 01/25/2023] Open
Abstract
The current global threat brought on by the Covid-19 pandemic has led to widespread social isolation, posing new challenges in dealing with metal suffering related to social distancing, and in quickly learning new social habits intended to prevent contagion. Neuroscience and psychology agree that dreaming helps people to cope with negative emotions and to learn from experience, but can dreaming effectively reveal mental suffering and changes in social behavior? To address this question, we applied natural language processing tools to study 239 dream reports by 67 individuals, made either before the Covid-19 outbreak or during the months of March and April, 2020, when lockdown was imposed in Brazil following the WHO’s declaration of the pandemic. Pandemic dreams showed a higher proportion of anger and sadness words, and higher average semantic similarities to the terms “contamination” and “cleanness”. These features seem to be associated with mental suffering linked to social isolation, as they explained 40% of the variance in the PANSS negative subscale related to socialization (p = 0.0088). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits that directly impact socialization.
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41
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Hitczenko K, Mittal VA, Goldrick M. Understanding Language Abnormalities and Associated Clinical Markers in Psychosis: The Promise of Computational Methods. Schizophr Bull 2020; 47:344-362. [PMID: 33205155 PMCID: PMC8480175 DOI: 10.1093/schbul/sbaa141] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment-manual annotations and/or clinical rating scales-are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis.
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Affiliation(s)
- Kasia Hitczenko
- Department of Linguistics, Northwestern University, Evanston,
IL,To whom correspondence should be addressed; Northwestern University, 2016
Sheridan Road, Evanston, IL 60208; tel: 847-491-5831, fax: 847-491-3770, e-mail:
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL,Department of Psychiatry, Northwestern University, Chicago, IL,Institute for Policy Research, Northwestern University, Evanston,
IL,Medical Social Sciences, Northwestern University, Chicago, IL,Institute for Innovations in Developmental Sciences, Northwestern
University, Evanston and Chicago, IL
| | - Matthew Goldrick
- Department of Linguistics, Northwestern University, Evanston,
IL,Institute for Innovations in Developmental Sciences, Northwestern
University, Evanston and Chicago, IL
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42
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Argolo F, Magnavita G, Mota NB, Ziebold C, Mabunda D, Pan PM, Zugman A, Gadelha A, Corcoran C, Bressan RA. Lowering costs for large-scale screening in psychosis: a systematic review and meta-analysis of performance and value of information for speech-based psychiatric evaluation. REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2020; 42:673-686. [PMID: 32321060 PMCID: PMC7678898 DOI: 10.1590/1516-4446-2019-0722] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 01/23/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Obstacles for computational tools in psychiatry include gathering robust evidence and keeping implementation costs reasonable. We report a systematic review of automated speech evaluation for the psychosis spectrum and analyze the value of information for a screening program in a healthcare system with a limited number of psychiatrists (Maputo, Mozambique). METHODS Original studies on speech analysis for forecasting of conversion in individuals at clinical high risk (CHR) for psychosis, diagnosis of manifested psychotic disorder, and first-episode psychosis (FEP) were included in this review. Studies addressing non-verbal components of speech (e.g., pitch, tone) were excluded. RESULTS Of 168 works identified, 28 original studies were included. Valuable speech features included direct measures (e.g., relative word counting) and mathematical embeddings (e.g.: word-to-vector, graphs). Accuracy estimates reported for schizophrenia diagnosis and CHR conversion ranged from 71 to 100% across studies. Studies used structured interviews, directed tasks, or prompted free speech. Directed-task protocols were faster while seemingly maintaining performance. The expected value of perfect information is USD 9.34 million. Imperfect tests would nevertheless yield high value. CONCLUSION Accuracy for screening and diagnosis was high. Larger studies are needed to enhance precision of classificatory estimates. Automated analysis presents itself as a feasible, low-cost method which should be especially useful for regions in which the physician pool is insufficient to meet demand.
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Affiliation(s)
- Felipe Argolo
- Universidade Federal de São Paulo, São Paulo, SP, Brazil
- King’s College London, London, UK
| | | | - Natalia Bezerra Mota
- Brain Institute, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
- Departamento de Física, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil
| | | | - Dirceu Mabunda
- Faculdade de Medicina, Universidade Eduardo Mondlane, Maputo, Mozambique
| | - Pedro M. Pan
- Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - André Zugman
- National Institute of Mental Health (NIMH), Bethesda, MD, USA
| | - Ary Gadelha
- Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Cheryl Corcoran
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education and Clinical Center (MIRECC VISN2), New York, NY, USA
| | - Rodrigo A. Bressan
- Universidade Federal de São Paulo, São Paulo, SP, Brazil
- King’s College London, London, UK
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Zhuang X, Yang Z, Cordes D. A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp 2020; 41:3807-3833. [PMID: 32592530 PMCID: PMC7416047 DOI: 10.1002/hbm.25090] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 05/23/2020] [Indexed: 12/11/2022] Open
Abstract
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.
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Affiliation(s)
- Xiaowei Zhuang
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
| | - Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
- University of ColoradoBoulderColoradoUSA
- Department of Brain HealthUniversity of NevadaLas VegasNevadaUSA
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44
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Haas SS, Doucet GE, Garg S, Herrera SN, Sarac C, Bilgrami ZR, Shaik RB, Corcoran CM. Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis. Eur Psychiatry 2020; 63:e72. [PMID: 32778184 PMCID: PMC7443790 DOI: 10.1192/j.eurpsy.2020.73] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Abnormalities in the semantic and syntactic organization of speech have been reported in individuals at clinical high-risk (CHR) for psychosis. The current study seeks to examine whether such abnormalities are associated with changes in brain structure and functional connectivity in CHR individuals. METHODS Automated natural language processing analysis was applied to speech samples obtained from 46 CHR and 22 healthy individuals. Brain structural and resting-state functional imaging data were also acquired from all participants. Sparse canonical correlation analysis (sCCA) was used to ascertain patterns of covariation between linguistic features, clinical symptoms, and measures of brain morphometry and functional connectivity related to the language network. RESULTS In CHR individuals, we found a significant mode of covariation between linguistic and clinical features (r = 0.73; p = 0.003), with negative symptoms and bizarre thinking covarying mostly with measures of syntactic complexity. In the entire sample, separate sCCAs identified a single mode of covariation linking linguistic features with brain morphometry (r = 0.65; p = 0.05) and resting-state network connectivity (r = 0.63; p = 0.01). In both models, semantic and syntactic features covaried with brain structural and functional connectivity measures of the language network. However, the contribution of diagnosis to both models was negligible. CONCLUSIONS Syntactic complexity appeared sensitive to prodromal symptoms in CHR individuals while the patterns of brain-language covariation seemed preserved. Further studies in larger samples are required to establish the reproducibility of these findings.
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Affiliation(s)
- S S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - G E Doucet
- Boys Town National Research Hospital, Omaha, Nebraska, USA
| | - S Garg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - S N Herrera
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - C Sarac
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Z R Bilgrami
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - R B Shaik
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - C M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Palaniyappan L, Al-Radaideh A, Gowland PA, Liddle PF. Cortical thickness and formal thought disorder in schizophrenia: An ultra high-field network-based morphometry study. Prog Neuropsychopharmacol Biol Psychiatry 2020; 101:109911. [PMID: 32151693 DOI: 10.1016/j.pnpbp.2020.109911] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 02/17/2020] [Accepted: 03/05/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND Persistent formal thought disorder (FTD) is a core feature of schizophrenia. Recent cognitive and neuroimaging studies indicate a distinct mechanistic pathway underlying the persistent positive FTD (pFTD or disorganized thinking), though its structural determinants are still elusive. Using network-based cortical thickness estimates from ultra-high field 7-Tesla Magnetic Resonance Imaging (7T MRI), we investigated the structural correlates of pFTD. METHODS We obtained speech samples and 7T MRI anatomical scans from medicated clinically stable patients with schizophrenia (n = 19) and healthy controls (n = 20). Network-based morphometry was used to estimate the mean cortical thickness of 17 functional networks covering the entire cortical surface from each subject. We also quantified the vertexwise variability of thickness within each network to quantify the spatial coherence of the 17 networks, estimated patients vs. controls differences, and related the thickness of the affected networks to the severity of pFTD. RESULTS Patients had reduced thickness of the frontoparietal and default mode networks, and reduced spatial coherence affecting the salience and the frontoparietal control network. A higher burden of positive FTD related to reduced frontoparietal thickness and reduced spatial coherence of the salience network. The presence of positive FTD, but not its severity, related to the reduced thickness of the language network comprising of the superior temporal cortex. CONCLUSIONS These results suggest that cortical thickness of both cognitive control and language networks underlie the positive FTD in schizophrenia. The structural integrity of cognitive control networks is a critical determinant of the expressed severity of persistent FTD in schizophrenia.
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Affiliation(s)
- Lena Palaniyappan
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada; Department of Psychiatry, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada.
| | - Ali Al-Radaideh
- Department of Medical Imaging, Faculty of Allied Health Sciences, The Hashemite University, Zarqa, Jordan.; Sir Peter Mansfield Imaging Centre (SPMIC), School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Penny A Gowland
- Sir Peter Mansfield Imaging Centre (SPMIC), School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Peter F Liddle
- Translational Neuroimaging for Mental Health, Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, UK
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Corcoran CM, Cecchi GA. Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:770-779. [PMID: 32771179 DOI: 10.1016/j.bpsc.2020.06.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/09/2020] [Accepted: 06/09/2020] [Indexed: 01/12/2023]
Abstract
Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents "big data" at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.
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Affiliation(s)
- Cheryl Mary Corcoran
- Icahn School of Medicine at Mount Sinai, New York; James J. Peters Veterans Administration Medical Center, Bronx.
| | - Guillermo A Cecchi
- Thomas J. Watson Research Center, IBM Corporation, Yorktown Heights, New York
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Abstract
PURPOSE OF REVIEW After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established. This article reviews current advances in evaluating the use of language as a diagnostic or prognostic tool in schizophrenia. RECENT FINDINGS The development of computational linguistic tools to quantify language disturbances is rapidly gaining ground in the field of schizophrenia research. Current applications are the use of semantic space models and acoustic analyses focused on phonetic markers. These features are used in machine learning models to distinguish patients with schizophrenia from healthy controls or to predict conversion to psychosis in high-risk groups, reaching accuracy scores (generally ranging from 80 to 90%) that exceed clinical raters. Other potential applications for a language biomarker in schizophrenia are monitoring of side effects, differential diagnostics and relapse prevention. SUMMARY Language disturbances are a key feature of schizophrenia. Although in its early stages, the emerging field of research focused on computational linguistics suggests an important role for language analyses in the diagnosis and prognosis of schizophrenia. Spoken language as a biomarker for schizophrenia has important advantages because it can be objectively and reproducibly quantified. Furthermore, language analyses are low-cost, time efficient and noninvasive in nature.
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Language in schizophrenia: relation with diagnosis, symptomatology and white matter tracts. NPJ SCHIZOPHRENIA 2020; 6:10. [PMID: 32313047 PMCID: PMC7171150 DOI: 10.1038/s41537-020-0099-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 02/28/2020] [Indexed: 01/04/2023]
Abstract
Language deviations are a core symptom of schizophrenia. With the advances in computational linguistics, language can be easily assessed in exact and reproducible measures. This study investigated how language characteristics relate to schizophrenia diagnosis, symptom, severity and integrity of the white matter language tracts in patients with schizophrenia and healthy controls. Spontaneous speech was recorded and diffusion tensor imaging was performed in 26 schizophrenia patients and 22 controls. We were able to classify both groups with a sensitivity of 89% and a specificity of 82%, based on mean length of utterance and clauses per utterance. Language disturbances were associated with negative symptom severity. Computational language measures predicted language tract integrity in patients (adjusted R2 = 0.467) and controls (adjusted R2 = 0.483). Quantitative language analyses have both clinical and biological validity, offer a simple, helpful marker of both severity and underlying pathology, and provide a promising tool for schizophrenia research and clinical practice.
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Liddle PF. The Core Deficit of Classical Schizophrenia: Implications for Predicting the Functional Outcome of Psychotic Illness and Developing Effective Treatments. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2019; 64:680-685. [PMID: 31434513 PMCID: PMC6783668 DOI: 10.1177/0706743719870515] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Many people suffering from psychotic illnesses experience persisting impairment of occupational and social function. Evidence assembled since the classical description of schizophrenia over a century ago indicates that both disorganization and impoverishment of mental activity are associated with persisting impairment. Longitudinal studies of young people at risk of schizophrenia reveal that both mental impoverishment and disorganization predict poor long-term outcome. These clinical features are related to cognitive impairments. Evidence from brain imaging indicates overlap in the brain abnormalities implicated in these phenomena, including impaired function of long-range connections between sensory cortex and the salience network, a network engaged in recruiting cerebral systems for processing of information salient to current circumstances. The evidence suggests that the common features underlying these two groups of symptoms might reflect a core pathological process distinguishing nonaffective from affective psychosis. This pathological process might therefore justifiably be designated the "core deficit" of classical schizophrenia. To develop more effective treatments to prevent persisting disability, we require the ability to identify individuals at risk at an early stage. Recent studies provide pointers toward effective strategies for identifying cases at risk of poor outcome. Accumulating evidence confirms that appreciable potential for neuroplastic change in the brain persists into adult life. Furthermore, brain function can be enhanced by targeted neuromodulation treatments. We now have promising tools not only for investigating the psychological and neural mechanisms that underlie persisting functional impairment but also for identifying individuals at risk and for harnessing brain plasticity to improve treatment.
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Affiliation(s)
- Peter F. Liddle
- Division of Psychiatry and Applied Psychology, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
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Bora E, Yalincetin B, Akdede BB, Alptekin K. Neurocognitive and linguistic correlates of positive and negative formal thought disorder: A meta-analysis. Schizophr Res 2019; 209:2-11. [PMID: 31153670 DOI: 10.1016/j.schres.2019.05.025] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 04/15/2019] [Accepted: 05/13/2019] [Indexed: 12/31/2022]
Abstract
Executive dysfunction and language impairment are the most prominent neuropsychological models of formal thought disorder (FTD) in schizophrenia. However, available studies have provided contradictory findings regarding the accuracy of these models. Furthermore, specific neurocognitive underpinnings of positive FTD (PosFTD) and negative FTD (NegFTD) are not clear. Following the systematic review of schizophrenia studies, a random-effects meta-analysis of the relationship between FTD and neurocognition/language in schizophrenia was conducted in 52 reports including 2805 patients. Neurocognition was significantly associated with both PosFTD (r = -0.21, CI = -0.14 to -0.27) and NegFTD (r = -0.24, CI = -0.18 to -0.30). Both PosFTD (r = ranged from -0.18 to -0.27) and NegFTD (r = ranged from -0.19 to -0.23) were significantly correlated with verbal memory, visual memory, attention, and processing speed. In meta-analyses of executive functions, PosFTD was significantly associated with working memory (r = -0.21), planning (r = -0.19), and inhibition (r = -0.21) and NegFTD was significantly associated with planning (r = -0.27), fluency (r = -0.27), and working memory (r = -0.24). In meta-analyses of linguistic variables, PosFTD was associated with deficits in syntactic comprehension (r = -0.27) and semantic processing (r = -0.18). In contrast, NegFTD was associated only with semantic comprehension (r = -0.21). Both PosFTD and NegFTD were significantly associated with executive dysfunction, neurocognitive deficits and semantic dysfunction but syntactic deficits were more specific to PosFTD. There were also some distinct patterns of relationships between the pattern of executive dysfunction and types of FTD. Fluency deficit was associated more strongly with NegFTD and poor inhibition was more specifically related to PosFTD. Current findings suggest that neurocognitive and linguistic correlates of PosFTD and NegFTD might be partly different.
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Affiliation(s)
- Emre Bora
- Department of Psychiatry, Dokuz Eylul University Medical School, Izmir 35340, Turkey; Department of Neuroscience, Dokuz Eylul University, Izmir 35340, Turkey; Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Victoria 3053, Australia.
| | - Berna Yalincetin
- Department of Neuroscience, Dokuz Eylul University, Izmir 35340, Turkey
| | - Berna Binnur Akdede
- Department of Psychiatry, Dokuz Eylul University Medical School, Izmir 35340, Turkey; Department of Neuroscience, Dokuz Eylul University, Izmir 35340, Turkey
| | - Köksal Alptekin
- Department of Psychiatry, Dokuz Eylul University Medical School, Izmir 35340, Turkey; Department of Neuroscience, Dokuz Eylul University, Izmir 35340, Turkey
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