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Elleuch D, Chen Y, Luo Q, Palaniyappan L. Relationship between grammar and schizophrenia: a systematic review and meta-analysis. COMMUNICATIONS MEDICINE 2025; 5:235. [PMID: 40523895 DOI: 10.1038/s43856-025-00944-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2024] [Accepted: 05/30/2025] [Indexed: 06/19/2025] Open
Abstract
BACKGROUND Schizophrenia significantly impairs everyday communication, affecting education and employment. Such communication difficulties may arise from deficits in syntax-understanding and generating grammatical structures. Research on syntactic impairments in schizophrenia is underpowered, with inconsistent findings, and it is unclear if deficits are specific to certain patient subgroups, regardless of symptom profiles, age, sex, or illness severity. METHODS A pre-registered (Open Science Framework: https://doi.org/10.17605/OSF.IO/7FZUC ) search using PubMed, Scopus, PsycINFO, and Web of Science databases up to May 1, 2024, for all studies investigating syntax comprehension and production in schizophrenia vs. healthy controls. Excluding studies on those <18 years of age and qualitative research, we extracted Cohen's d and log coefficient of variation ratio and used Bayesian meta-analysis across 6 domains: 2 in comprehension and 4 in production in patient-control comparisons. Study quality was evaluated using a modified Newcastle-Ottawa Scale, with moderators (age, sex, study quality, language) tested via meta-regression. RESULTS We identify 86 relevant articles, of which 45 have sufficient data for meta-analysis (n = 2960 participants, 64.4% English, weighted mean age(sd) = 32.3(5.6)). Bayesian meta-analysis shows strong evidence of syntactic deficits in schizophrenia across all domains (d = 0.65-1.01, overall random-effects d = 0.86, 95% CrI [0.67-1.03]), with syntax comprehension being most affected, with weak publication bias. People with schizophrenia show increased variability in comprehension and production of long and complex utterances (lnCVR = 0.21, 95% CrI [0.07-0.36]), hinting at subgroups with differing performance. CONCLUSIONS Robust impairments in grammatical comprehension and production in schizophrenia suggest opportunities for targeted interventions focusing on syntax, a rule-based feature amenable to cognitive, educational, and linguistic interventions.
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Affiliation(s)
- Dalia Elleuch
- Higher School of Health Sciences and Techniques of Sfax, University of Sfax, Sfax, Tunisia
| | - Yinhan Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China
| | - Qiang Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China
- State Key Laboratory of Brain Function and Disorders and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Robarts Research Institute, London, ON, Canada.
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
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Vilenchik D, Cwikel J, Ezra Y, Hausdorff T, Lazarov M, Sergienko R, Abramovitz R, Schmidt I, Perez AS. Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework. Depress Anxiety 2025; 2025:4839334. [PMID: 40444180 PMCID: PMC12122156 DOI: 10.1155/da/4839334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Accepted: 04/29/2025] [Indexed: 06/02/2025] Open
Abstract
Depression accounts for a major share of global disability-adjusted life-years (DALYs). Diagnosis typically requires a psychiatrist or lengthy self-assessments, which can be challenging for symptomatic individuals. Developing reliable, noninvasive, and accessible detection methods is a healthcare priority. Voice analysis offers a promising approach for early depression detection, potentially improving treatment access and reducing costs. This paper presents a novel pipeline for depression detection that addresses several critical challenges in the field, including data imbalance, label quality, and model generalizability. Our study utilizes a high-quality, high-depression-prevalence dataset collected from a specialized chronic pain clinic, enabling robust depression detection even with a limited sample size. We obtained a lift in the accuracy of up to 15% over the 50-50 baseline in our 52-patient dataset using a 3-fold cross-validation test (which means the train set is n = 34, std 2.8%, p-value 0.01). We further show that combining voice-only acoustic features with a single self-report question (subject unit of distress [SUDs]) significantly improves predictive accuracy. While relying on SUDs is not always good practice, our data collection setting lacked incentives to misrepresent depression status; SUDs were highly reliable, giving 86% accuracy; adding acoustic features raises it to 92%, exceeding the stand-alone potential of SUDs with a p-value 0.1. Further data collection will enhance accuracy, supporting a rapid, noninvasive depression detection method that overcomes clinical barriers. These findings offer a promising tool for early depression detection across clinical settings.
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Affiliation(s)
- Dan Vilenchik
- School of Electrical and Computer Engineering, Ben Gurion University of the Negev, POB 653, Beer Sheva 84105, Israel
| | - Julie Cwikel
- Center for Women's Health Studies and Promotion, Ben Gurion University of the Negev, POB 653, Beer Sheva 84105, Israel
| | - Yacov Ezra
- Functional Neurology Out-patient Clinic, Soroka University Medical Center, Beer Sheva, Israel
| | - Tuvia Hausdorff
- School of Electrical and Computer Engineering, Ben Gurion University of the Negev, POB 653, Beer Sheva 84105, Israel
| | - Mor Lazarov
- Department of Computer Science, Ben Gurion University of the Negev, POB 653, Beer Sheva 84105, Israel
| | - Ruslan Sergienko
- Department of Health Policy and Management, Faculty of Health Sciences, Ben-Gurion University of the Negev, POB 653, Beer Sheva 84105, Israel
| | - Rachel Abramovitz
- Center for Women's Health Studies and Promotion, Ben Gurion University of the Negev, POB 653, Beer Sheva 84105, Israel
| | - Ilana Schmidt
- Center for Women's Health Studies and Promotion, Ben Gurion University of the Negev, POB 653, Beer Sheva 84105, Israel
| | - Alison Stern Perez
- Center for Women's Health Studies and Promotion, Ben Gurion University of the Negev, POB 653, Beer Sheva 84105, Israel
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Elleuch D, Chen Y, Luo Q, Palaniyappan L. Speaking of yourself: A meta-analysis of 80 years of research on pronoun use in schizophrenia. Schizophr Res 2025; 279:22-30. [PMID: 40157253 DOI: 10.1016/j.schres.2025.03.025] [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: 11/29/2024] [Revised: 02/16/2025] [Accepted: 03/19/2025] [Indexed: 04/01/2025]
Abstract
People with schizophrenia experience significant language disturbances that profoundly affect their everyday social interactions. Given its relevance to the referential function of language, aberrations in pronoun use are of particular interest in the study of schizophrenia. This systematic review and meta-analysis, adhering to PRISMA guidelines, examines the frequency of pronoun use in schizophrenia. PubMed, PsycINFO, Scopus, Google Scholar, and Web of Science were searched up to May 1, 2024. All studies analyzing pronoun frequency in various spoken language contexts in schizophrenia were included. Bias was assessed using a modified Newcastle-Ottawa Scale. A Bayesian meta-analysis with model averaging estimated effect sizes and moderating factors. 13 studies with n = 917 unique participants and 13 case-control contrasts were included. 37.9 % of patient samples were women, with a weighted mean (SD) age of 34.45 (9.72) years. 53.85 % of the studies were in languages other than English. We report a medium-sized effect for first-person pronoun impairment in schizophrenia (model-averaged d = 0.89, 95 % CrI (0.44, 1.33)). There was significant heterogeneity moderated by age. Evidence for publication bias was weak, with a strong support for first-person pronoun impairment after accounting for bias and heterogeneity. There was a small reduction of inter-individual variability in first-person pronoun use in patients compared to healthy controls (lnCVR = -0.12, 95 % CrI [-0.35, -0.13]). While all pronoun use was also high in patients, this was not robust due to heterogeneity and publication bias. Individuals with schizophrenia excessively use first-person pronouns. This may be a marker of a disturbed sense of self in this illness.
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Affiliation(s)
- Dalia Elleuch
- Higher School of Health Sciences and Techniques of Sfax, University of Sfax, Tunisia; Laboratory of Neuroscience, Faculty of Medicine of Sfax, University of Sfax, Tunisia
| | - Yinhan Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Qiang Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200433, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Quebec, Canada; Robarts Research Institute & Lawson Health Research Institute, London, Ontario, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
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Natsuyama T, Chibaatar E, Shibata Y, Okamoto N, Cruz Victorino JN, Ikenouchi A, Shibata T, Yoshimura R. Associations of Vocal Features, Psychiatric Symptoms, and Cognitive Functions in Schizophrenia. Neuropsychiatr Dis Treat 2025; 21:943-954. [PMID: 40291596 PMCID: PMC12034276 DOI: 10.2147/ndt.s514927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2024] [Accepted: 04/12/2025] [Indexed: 04/30/2025] Open
Abstract
Purpose This study explored the use of advanced computational techniques in vocal analysis to improve the assessment of psychiatric symptoms and cognitive functions in schizophrenia. We hypothesized that digital signal processing techniques, such as mel spectrogram and mel-frequency cepstral coefficients (MFCC), could be used for objective evaluation of psychiatric symptoms and cognitive functions based on the analysis of alterations in the vocal characteristics. Patients and Methods Voice samples from 14 participants diagnosed with schizophrenia (92.9% female) were collected using a microphone array, and vocal features were extracted from the samples using mel spectrogram and MFCC techniques. Psychiatric symptoms and cognitive functions were assessed using the Positive and Negative Syndrome Scale (PANSS) and the computer-based tool Cognitrax. Results We found significant negative correlations between specific vocal features (mel spectrogram and MFCC) and cognitive functions, particularly working memory (β = -0.645, p = 0.023) and sustained attention (β = -0.626, p = 0.029). No direct correlations were found between vocal features and psychiatric symptoms, as measured by PANSS scores. However, the correlations between cognitive functions and PANSS total scores were significant (β = -0.604, p = 0.037), suggesting that cognitive functions may mediate the relationship between psychiatric symptoms and vocal characteristics. Conclusion This study underscores the potential of vocal analysis as a non-invasive tool for assessing cognitive impairment in schizophrenia. Future research should focus on expanding the sample size and including diverse populations to enhance the generalizability of these findings.
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Affiliation(s)
- Tomoya Natsuyama
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, 807-8555, Japan
| | - Enkhmurun Chibaatar
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, 807-8555, Japan
| | - Yuko Shibata
- Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka, 808-0135, Japan
| | - Naomichi Okamoto
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, 807-8555, Japan
| | - John Noel Cruz Victorino
- Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka, 808-0135, Japan
| | - Atsuko Ikenouchi
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, 807-8555, Japan
| | - Tomohiro Shibata
- Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka, 808-0135, Japan
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, 807-8555, Japan
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Dalal TC, Liang L, Silva AM, Mackinley M, Voppel A, Palaniyappan L. Speech based natural language profile before, during and after the onset of psychosis: A cluster analysis. Acta Psychiatr Scand 2025; 151:332-347. [PMID: 38600593 PMCID: PMC11787926 DOI: 10.1111/acps.13685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 03/20/2024] [Accepted: 03/23/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND AND HYPOTHESIS Speech markers are digitally acquired, computationally derived, quantifiable set of measures that reflect the state of neurocognitive processes relevant for social functioning. "Oddities" in language and communication have historically been seen as a core feature of schizophrenia. The application of natural language processing (NLP) to speech samples can elucidate even the most subtle deviations in language. We aim to determine if NLP based profiles that are distinctive of schizophrenia can be observed across the various clinical phases of psychosis. DESIGN Our sample consisted of 147 participants and included 39 healthy controls (HC), 72 with first-episode psychosis (FEP), 18 in a clinical high-risk state (CHR), 18 with schizophrenia (SZ). A structured task elicited 3 minutes of speech, which was then transformed into quantitative measures on 12 linguistic variables (lexical, syntactic, and semantic). Cluster analysis that leveraged healthy variations was then applied to determine language-based subgroups. RESULTS We observed a three-cluster solution. The largest cluster included most HC and the majority of patients, indicating a 'typical linguistic profile (TLP)'. One of the atypical clusters had notably high semantic similarity in word choices with less perceptual words, lower cohesion and analytical structure; this cluster was almost entirely composed of patients in early stages of psychosis (EPP - early phase profile). The second atypical cluster had more patients with established schizophrenia (SPP - stable phase profile), with more perceptual but less cognitive/emotional word classes, simpler syntactic structure, and a lack of sufficient reference to prior information (reduced givenness). CONCLUSION The patterns of speech deviations in early and established stages of schizophrenia are distinguishable from each other and detectable when lexical, semantic and syntactic aspects are assessed in the pursuit of 'formal thought disorder'.
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Affiliation(s)
- Tyler C. Dalal
- Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | | | | | | | | | - Lena Palaniyappan
- Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
- Robarts Research InstituteLondonOntarioCanada
- Douglas Mental Health University InstituteMcGill UniversityMontrealQuebecCanada
- Department of PsychiatryWestern UniversityLondonOntarioCanada
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Verter V, E F, Frank D, Georghiou A. Text mining of outpatient narrative notes to predict the risk of psychiatric hospitalization. Transl Psychiatry 2025; 15:60. [PMID: 39979298 PMCID: PMC11842738 DOI: 10.1038/s41398-025-03276-9] [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: 05/10/2024] [Revised: 01/15/2025] [Accepted: 02/07/2025] [Indexed: 02/22/2025] Open
Abstract
The primary purpose of this paper is to investigate whether text mining of the outpatient narrative notes for patients with severe and persistent mental illness (SPMI) can strengthen the predictions concerning the probability of an upcoming hospital readmission. A five-year study of all clinical notes for SPMI patients at the outpatient clinic of a tertiary hospital was conducted. The clinical notes were studied using ensemble classification i.e., entity recognition. Confounding variables pertaining to the patient's health status were extracted by text mining. A mixed effects logistic regression model was used for estimating the re-hospitalization risk during a clinic visit. The factors included frequency and continuity of outpatient visits, alterations in medication prescriptions, the usage of long-acting anti-psychotic injections (LAIs), the presence or absence of a legal compulsory treatment order (CTO) and the hospitalizations. The appearance of certain words in the outpatient clinical notes has a statistically significant impact on the risk of an upcoming hospitalization. This study also reconfirms that the risk of a re-hospitalization of an SPMI patient is reduced by the presence of a CTO and the utilization of LAIs, whereas it is increased by the patient dropping out of outpatient care. Our findings pertaining to the risk of re-hospitalization could facilitate preventive interventions for SPMI patients with higher risk.
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Affiliation(s)
| | - Fan E
- Manulife, Toronto, ON, Canada
| | - Daniel Frank
- Montreal Jewish General Hospital & McGill University, Montreal, QC, Canada
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Argolo F, Ramos WHDP, Mota NB, Gondim JM, Lopes-Rocha AC, Andrade JC, van de Bilt MT, de Jesus LP, Jafet A, Cecchi G, Gattaz WF, Corcoran CM, Ara A, Loch AA. Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory. REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2024; 46:e20233419. [PMID: 39074334 PMCID: PMC11773321 DOI: 10.47626/1516-4446-2023-3419] [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: 09/29/2023] [Accepted: 06/26/2024] [Indexed: 07/31/2024]
Abstract
OBJECTIVE Verbal communication contains key information for mental health assessment. Researchers have linked psychopathology phenomena to certain counterparts in natural language processing. We characterized subtle impairments in the early stages of psychosis, developing new analysis techniques, which led to a comprehensive map associating features of natural language processing with the full range of clinical presentation. METHODS We used natural language processing to assess spontaneous and elicited speech by 60 individuals with at-risk mental states and 73 controls who were screened from 4,500 quota-sampled Portuguese speaking residents of São Paulo, Brazil. Psychotic symptoms were independently assessed with the Structured Interview for Psychosis-Risk Syndromes. Speech features (e.g., sentiments and semantic coherence), including novel ones, were correlated with psychotic traits (Spearman's-?) and at-risk mental state status (general linear models and machine-learning ensembles). RESULTS Natural language processing features were informative for classification, presenting a balanced accuracy of 86%. Features such as semantic laminarity (as perseveration), semantic recurrence time (as circumstantiality), and average centrality in word repetition graphs carried the most information and were directly correlated with psychotic symptoms. Grammatical tagging (e.g., use of adjectives) was the most relevant standard measure. CONCLUSION Subtle speech impairments can be detected by sensitive methods and can be used in at-risk mental states screening. We have outlined a blueprint for speech-based evaluation, pairing features to standard psychometric items for thought disorder.
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Affiliation(s)
- Felipe Argolo
- Laboratório de Neurociências (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | | | - Natalia Bezerra Mota
- Instituto de Psiquiatria, Departamento de Psiquiatria e Medicina Legal, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Research Department at Motrix Lab, Rio de Janeiro, RJ, Brazil
| | - João Medrado Gondim
- Instituto de Computação, Universidade Federal da Bahia, Salvador, BA, Brazil
| | - Ana Caroline Lopes-Rocha
- Laboratório de Neurociências (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Julio Cesar Andrade
- Laboratório de Neurociências (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Martinus Theodorus van de Bilt
- Laboratório de Neurociências (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil
| | - Leonardo Peroni de Jesus
- Laboratório de Neurociências (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Andrea Jafet
- Laboratório de Neurociências (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | | | - Wagner Farid Gattaz
- Laboratório de Neurociências (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil
| | - Cheryl Mary Corcoran
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY, USA
| | - Anderson Ara
- Departamento de Estatística, Universidade Federal do Paraná, Curitiba, PR, Brazil
| | - Alexandre Andrade Loch
- Laboratório de Neurociências (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil
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Premananth G, Siriwarden YM, Resnik P, Espy-Wilson C. A multi-modal approach for identifying schizophrenia using cross-modal attention. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2024; 2024:1-5. [PMID: 40039545 DOI: 10.1109/embc53108.2024.10782297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms. We developed a multi-modal schizophrenia classification system using audio, video, and text. Facial action units and vocal tract variables were extracted as low-level features from video and audio respectively, which were then used to compute high-level coordination features that served as the inputs from the audio and video modalities. Context-independent text embeddings extracted from transcriptions of speech were used as the input for the text modality. The multi-modal system is developed by fusing a segment-to-session-level classifier for video and audio modalities with a text model based on a Hierarchical Attention Network (HAN), with cross-modal attention. The proposed multi-modal system outperforms the previous state-of-the-art multi-modal system by 8.53% in the weighted average F1 score.
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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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Thakkar A, Gupta A, De Sousa A. Artificial intelligence in positive mental health: a narrative review. Front Digit Health 2024; 6:1280235. [PMID: 38562663 PMCID: PMC10982476 DOI: 10.3389/fdgth.2024.1280235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.
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Guiral JA. Neuropsychological dimensions related to alterations of verbal self-monitoring neural networks in schizophrenic language: systematic review. Front Psychiatry 2024; 15:1356726. [PMID: 38501094 PMCID: PMC10944891 DOI: 10.3389/fpsyt.2024.1356726] [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: 12/16/2023] [Accepted: 01/23/2024] [Indexed: 03/20/2024] Open
Abstract
Although schizophrenia has traditionally been interpreted as a disorder of thought, contemporary perspectives suggest that it may be more appropriate to conceptualize it as a disorder of language connectivity. The linguistic anomalies present in schizophrenia possess distinctive characteristics that, despite certain connections, are not comparable to aphasic disorders. It is proposed that these anomalies are the result of dysfunctions in verbal self-monitoring mechanisms, which may influence other neuropsychological dimensions. This study set out to examine the neuropsychological dimensions associated with alterations in the neural networks of verbal self-monitoring in schizophrenic language, based on the scientific evidence published to date. Exhaustive searches were conducted in PubMed, Web of Science, and Scopus to identify magnetic resonance studies that evaluated verbal self-monitoring mechanisms in schizophrenia. Of a total of 133 articles identified, 22 were selected for qualitative analysis. The general findings indicated alterations in frontotemporoparietal networks and in systems such as the insula, amygdala, anterior cingulate cortex, putamen, and hippocampus. Despite the heterogeneity of the data, it is concluded that language plays a fundamental role in schizophrenia and that its alterations are linked with other neuropsychological dimensions, particularly emotional and perceptual ones.
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Affiliation(s)
- Julián Andrés Guiral
- Humanities, EAFIT University, Medellín, Colombia
- Instituto de Neuropsicología y Lenguaje, EAFIT University, Medellín, Colombia
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12
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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: 4] [Impact Index Per Article: 4.0] [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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13
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Li TMH, Chen J, Law FOC, Li CT, Chan NY, Chan JWY, Chau SWH, Liu Y, Li SX, Zhang J, Leung KS, Wing YK. Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study. JMIR Med Inform 2023; 11:e50221. [PMID: 38054498 DOI: 10.2196/50221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/31/2023] [Accepted: 08/23/2023] [Indexed: 12/07/2023] Open
Abstract
Background Assessing patients' suicide risk is challenging, especially among those who deny suicidal ideation. Primary care providers have poor agreement in screening suicide risk. Patients' speech may provide more objective, language-based clues about their underlying suicidal ideation. Text analysis to detect suicide risk in depression is lacking in the literature. Objective This study aimed to determine whether suicidal ideation can be detected via language features in clinical interviews for depression using natural language processing (NLP) and machine learning (ML). Methods This cross-sectional study recruited 305 participants between October 2020 and May 2022 (mean age 53.0, SD 11.77 years; female: n=176, 57%), of which 197 had lifetime depression and 108 were healthy. This study was part of ongoing research on characterizing depression with a case-control design. In this study, 236 participants were nonsuicidal, while 56 and 13 had low and high suicide risks, respectively. The structured interview guide for the Hamilton Depression Rating Scale (HAMD) was adopted to assess suicide risk and depression severity. Suicide risk was clinician rated based on a suicide-related question (H11). The interviews were transcribed and the words in participants' verbal responses were translated into psychologically meaningful categories using Linguistic Inquiry and Word Count (LIWC). Results Ordinal logistic regression revealed significant suicide-related language features in participants' responses to the HAMD questions. Increased use of anger words when talking about work and activities posed the highest suicide risk (odds ratio [OR] 2.91, 95% CI 1.22-8.55; P=.02). Random forest models demonstrated that text analysis of the direct responses to H11 was effective in identifying individuals with high suicide risk (AUC 0.76-0.89; P<.001) and detecting suicide risk in general, including both low and high suicide risk (AUC 0.83-0.92; P<.001). More importantly, suicide risk can be detected with satisfactory performance even without patients' disclosure of suicidal ideation. Based on the response to the question on hypochondriasis, ML models were trained to identify individuals with high suicide risk (AUC 0.76; P<.001). Conclusions This study examined the perspective of using NLP and ML to analyze the texts from clinical interviews for suicidality detection, which has the potential to provide more accurate and specific markers for suicidal ideation detection. The findings may pave the way for developing high-performance assessment of suicide risk for automated detection, including online chatbot-based interviews for universal screening.
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Affiliation(s)
- Tim M H Li
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Jie Chen
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Framenia O C Law
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Chun-Tung Li
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Ngan Yin Chan
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Joey W Y Chan
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Steven W H Chau
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Yaping Liu
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Shirley Xin Li
- Department of Psychology, The University of Hong Kong, Hong Kong, China (Hong Kong)
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Jihui Zhang
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
- Guangdong Mental Health Center, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangdong, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
- Department of Applied Data Science, Hong Kong Shue Yan University, Hong Kong, China (Hong Kong)
| | - Yun-Kwok Wing
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
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14
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Nour MM, McNamee DC, Liu Y, Dolan RJ. Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts. Proc Natl Acad Sci U S A 2023; 120:e2305290120. [PMID: 37816054 PMCID: PMC10589662 DOI: 10.1073/pnas.2305290120] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 07/31/2023] [Indexed: 10/12/2023] Open
Abstract
Human cognition is underpinned by structured internal representations that encode relationships between entities in the world (cognitive maps). Clinical features of schizophrenia-from thought disorder to delusions-are proposed to reflect disorganization in such conceptual representations. Schizophrenia is also linked to abnormalities in neural processes that support cognitive map representations, including hippocampal replay and high-frequency ripple oscillations. Here, we report a computational assay of semantically guided conceptual sampling and exploit this to test a hypothesis that people with schizophrenia (PScz) exhibit abnormalities in semantically guided cognition that relate to hippocampal replay and ripples. Fifty-two participants [26 PScz (13 unmedicated) and 26 age-, gender-, and intelligence quotient (IQ)-matched nonclinical controls] completed a category- and letter-verbal fluency task, followed by a magnetoencephalography (MEG) scan involving a separate sequence-learning task. We used a pretrained word embedding model of semantic similarity, coupled to a computational model of word selection, to quantify the degree to which each participant's verbal behavior was guided by semantic similarity. Using MEG, we indexed neural replay and ripple power in a post-task rest session. Across all participants, word selection was strongly influenced by semantic similarity. The strength of this influence showed sensitivity to task demands (category > letter fluency) and predicted performance. In line with our hypothesis, the influence of semantic similarity on behavior was reduced in schizophrenia relative to controls, predicted negative psychotic symptoms, and correlated with an MEG signature of hippocampal ripple power (but not replay). The findings bridge a gap between phenomenological and neurocomputational accounts of schizophrenia.
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Affiliation(s)
- Matthew M. Nour
- Department of Psychiatry, University of Oxford, OxfordOX3 7JX, United Kingdom
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, LondonWC1B 5EH, United Kingdom
| | - Daniel C. McNamee
- Champalimaud Research, Centre for the Unknown, 1400-038Lisbon, Portugal
| | - Yunzhe Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing100875, China
- Chinese Institute for Brain Research, Beijing102206, China
| | - Raymond J. Dolan
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, LondonWC1B 5EH, United Kingdom
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing100875, China
- Wellcome Centre for Human Neuroimaging, University College London, LondonWC1N 3AR, United Kingdom
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15
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Le Boterff Q, Rabah A, Carment L, Bendjemaa N, Térémetz M, Alouit A, Levy A, Tanguy G, Morin V, Amado I, Cuenca M, Turc G, Maier MA, Krebs MO, Lindberg PG. A tablet-based quantitative assessment of manual dexterity for detection of early psychosis. Front Psychiatry 2023; 14:1200864. [PMID: 37435404 PMCID: PMC10330763 DOI: 10.3389/fpsyt.2023.1200864] [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: 04/05/2023] [Accepted: 06/06/2023] [Indexed: 07/13/2023] Open
Abstract
Background We performed a pilot study on whether tablet-based measures of manual dexterity can provide behavioral markers for detection of first-episode psychosis (FEP), and whether cortical excitability/inhibition was altered in FEP. Methods Behavioral and neurophysiological testing was undertaken in persons diagnosed with FEP (N = 20), schizophrenia (SCZ, N = 20), autism spectrum disorder (ASD, N = 20), and in healthy control subjects (N = 20). Five tablet tasks assessed different motor and cognitive functions: Finger Recognition for effector (finger) selection and mental rotation, Rhythm Tapping for temporal control, Sequence Tapping for control/memorization of motor sequences, Multi Finger Tapping for finger individuation, and Line Tracking for visuomotor control. Discrimination of FEP (from other groups) based on tablet-based measures was compared to discrimination through clinical neurological soft signs (NSS). Cortical excitability/inhibition, and cerebellar brain inhibition were assessed with transcranial magnetic stimulation. Results Compared to controls, FEP patients showed slower reaction times and higher errors in Finger Recognition, and more variability in Rhythm Tapping. Variability in Rhythm Tapping showed highest specificity for the identification of FEP patients compared to all other groups (FEP vs. ASD/SCZ/Controls; 75% sensitivity, 90% specificity, AUC = 0.83) compared to clinical NSS (95% sensitivity, 22% specificity, AUC = 0.49). Random Forest analysis confirmed FEP discrimination vs. other groups based on dexterity variables (100% sensitivity, 85% specificity, balanced accuracy = 92%). The FEP group had reduced short-latency intra-cortical inhibition (but similar excitability) compared to controls, SCZ, and ASD. Cerebellar inhibition showed a non-significant tendency to be weaker in FEP. Conclusion FEP patients show a distinctive pattern of dexterity impairments and weaker cortical inhibition. Easy-to-use tablet-based measures of manual dexterity capture neurological deficits in FEP and are promising markers for detection of FEP in clinical practice.
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Affiliation(s)
- Quentin Le Boterff
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
| | - Ayah Rabah
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
| | - Loïc Carment
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
| | - Narjes Bendjemaa
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
- GHU Paris Psychiatrie & Neurosciences, Paris, France
| | - Maxime Térémetz
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
| | - Anaëlle Alouit
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
| | - Agnes Levy
- GHU Paris Psychiatrie & Neurosciences, Paris, France
| | | | | | | | | | - Guillaume Turc
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
- GHU Paris Psychiatrie & Neurosciences, Paris, France
| | - Marc A. Maier
- CNRS, Integrative Neuroscience and Cognition Center, Université Paris Cité, Paris, France
| | - Marie-Odile Krebs
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
- GHU Paris Psychiatrie & Neurosciences, Paris, France
| | - Påvel G. Lindberg
- INSERM U1266 Institut de Psychiatrie et Neurosciences de Paris, Paris, France
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16
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Al Noman A, Tasneem Z, Abhi SH, Badal FR, Rafsanzane M, Islam MR, Alam F. Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach. Heliyon 2023; 9:e15672. [PMID: 37180909 PMCID: PMC10173602 DOI: 10.1016/j.heliyon.2023.e15672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/10/2023] [Accepted: 04/18/2023] [Indexed: 05/16/2023] Open
Abstract
The drag based Savonius wind turbine (SWT) has shown immense potential for renewable power generation in built-up areas under complex urban wind conditions. While a series of studies have been conducted on improving SWT's efficiency, optimal performance has yet to be achieved using traditional design approaches such as experimental and/or computational fluid dynamics methods. Recently, artificial intelligence and machine learning have been widely used in design optimization. As such, an ANN-based virtual clone can be an alternative to traditional design methods for wind turbine performance determination. Therefore, the main goal of this study is to investigate whether ANN-based virtual clones are capable of determining the performance of SWTs with a shorter timeframe and minimal resources compared to traditional methods. To achieve the objective, an ANN-based virtual clone model is developed. Two sets of data (computational and experimental) are used to validate and determine the efficacy of the proposed ANN-based virtual clone model. Using experimental data, the model's fidelity is over 98%. The proposed model produces results in one-fifth the time of the existing simulation (based on the combined ANN + GA metamodel) method. The model also reveals the location of the dataset's optimized point for augmenting the turbine's performance.
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Affiliation(s)
- Abdullah Al Noman
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Zinat Tasneem
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Sarafat Hussain Abhi
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Faisal R. Badal
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Rafsanzane
- Department of Mechanical Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Robiul Islam
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Firoz Alam
- School of Engineering (Aerospace, Mechanical and Manufacturing), RMIT University, Melbourne, Australia
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17
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
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18
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Parola A, Simonsen A, Lin JM, Zhou Y, Wang H, Ubukata S, Koelkebeck K, Bliksted V, Fusaroli R. Voice Patterns as Markers of Schizophrenia: Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation. Schizophr Bull 2023; 49:S125-S141. [PMID: 36946527 PMCID: PMC10031745 DOI: 10.1093/schbul/sbac128] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [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 Voice atypicalities are potential markers of clinical features of schizophrenia (eg, negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages. STUDY DESIGN We provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences. STUDY RESULTS We found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages. CONCLUSIONS The findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.
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Affiliation(s)
- Alberto Parola
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Department of Psychology, University of Turin, Turin, Italy
| | - Arndis Simonsen
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Psychosis Research Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jessica Mary Lin
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
| | - Yuan Zhou
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shiho Ubukata
- Department of Psychiatry, Kyoto University, Kyoto, Japan
| | - Katja Koelkebeck
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Hospital and Institute of the University of Duisburg-Essen, Essen, Germany
- Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Duisburg-Essen, Germany
| | - Vibeke Bliksted
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Psychosis Research Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Riccardo Fusaroli
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Linguistic Data Consortium, University of Pennsylvania, Philadelphia, USA
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19
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Limongi R, Silva AM, Mackinley M, Ford SD, Palaniyappan L. Active Inference, Epistemic Value, and Uncertainty in Conceptual Disorganization in First-Episode Schizophrenia. Schizophr Bull 2023; 49:S115-S124. [PMID: 36946528 PMCID: PMC10031740 DOI: 10.1093/schbul/sbac125] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
BACKGROUND AND HYPOTHESIS Active inference has become an influential concept in psychopathology. We apply active inference to investigate conceptual disorganization in first-episode schizophrenia. We conceptualize speech production as a decision-making process affected by the latent "conceptual organization"-as a special case of uncertainty about the causes of sensory information. Uncertainty is both minimized via speech production-in which function words index conceptual organization in terms of analytic thinking-and tracked by a domain-general salience network. We hypothesize that analytic thinking depends on conceptual organization. Therefore, conceptual disorganization in schizophrenia would be both indexed by low conceptual organization and reflected in the effective connectivity within the salience network. STUDY DESIGN With 1-minute speech samples from a picture description task and resting state fMRI from 30 patients and 30 healthy subjects, we employed dynamic causal and probabilistic graphical models to investigate if the effective connectivity of the salience network underwrites conceptual organization. STUDY RESULTS Low analytic thinking scores index low conceptual organization which affects diagnostic status. The influence of the anterior insula on the anterior cingulate cortex and the self-inhibition within the anterior cingulate cortex are elevated given low conceptual organization (ie, conceptual disorganization). CONCLUSIONS Conceptual organization, a construct that explains formal thought disorder, can be modeled in an active inference framework and studied in relation to putative neural substrates of disrupted language in schizophrenia. This provides a critical advance to move away from rating-scale scores to deeper constructs in the pursuit of the pathophysiology of formal thought disorder.
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Affiliation(s)
- Roberto Limongi
- Department of Psychology, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, London, ON, Canada
| | | | - Michael Mackinley
- Robarts Research Institute, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
| | | | - Lena Palaniyappan
- Robarts Research Institute, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
- The Brain and Mind Institute, University of Western Ontario, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
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20
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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: 8] [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] [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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21
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Agurto C, Norel R, Wen B, Wei Y, Zhang D, Bilgrami Z, Hsi X, Zhang T, Pasternak O, Li H, Keshavan M, Seidman LJ, Whitfield-Gabrieli S, Shenton ME, Niznikiewicz MA, Wang J, Cecchi G, Corcoran C, Stone WS. Are language features associated with psychosis risk universal? A study in Mandarin-speaking youths at clinical high risk for psychosis. World Psychiatry 2023; 22:157-158. [PMID: 36640384 PMCID: PMC9840499 DOI: 10.1002/wps.21045] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/08/2022] [Indexed: 01/15/2023] Open
Affiliation(s)
- Carla Agurto
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Raquel Norel
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Bo Wen
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Yanyan Wei
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dan Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zarina Bilgrami
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Xiaolu Hsi
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Huijun Li
- Department of Psychology, Florida A&M University, Tallahassee, FL, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Larry J Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | | | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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22
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Mackinley M, Limongi R, Silva AM, Richard J, Subramanian P, Ganjavi H, Palaniyappan L. More than words: Speech production in first-episode psychosis predicts later social and vocational functioning. Front Psychiatry 2023; 14:1144281. [PMID: 37124249 PMCID: PMC10140590 DOI: 10.3389/fpsyt.2023.1144281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/20/2023] [Indexed: 05/02/2023] Open
Abstract
Background Several disturbances in speech are present in psychosis; however, the relationship between these disturbances during the first-episode of psychosis (FEP) and later vocational functioning is unclear. Demonstrating this relationship is critical if we expect speech and communication deficits to emerge as targets for early intervention. Method We analyzed three 1-min speech samples using automated speech analysis and Bayes networks in an antipsychotic-naive sample of 39 FEP patients and followed them longitudinally to determine their vocational status (engaged or not engaged in employment education or training-EET vs. NEET) after 6-12 months of treatment. Five baseline linguistic variables with prior evidence of clinical relevance (total and acausal connectives use, pronoun use, analytic thinking, and total words uttered in a limited period) were included in a Bayes network along with follow-up NEET status and Social and Occupational Functioning Assessment Scale (SOFAS) scores to determine dependencies among these variables. We also included clinical (Positive and Negative Syndrome Scale 8-item version (PANSS-8)), social (parental socioeconomic status), and cognitive features (processing speed) at the time of presentation as covariates. Results The Bayes network revealed that only total words spoken at the baseline assessment were directly associated with later NEET status and had an indirect association with SOFAS, with a second set of dependencies emerging among the remaining linguistic variables. The primary (speech-only) model outperformed models including parental socioeconomic status, processing speed or both as latent variables. Conclusion Impoverished speech, even at subclinical levels, may hold prognostic value for functional outcomes and warrant consideration when providing measurement based care for first-episode psychosis.
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Affiliation(s)
- Michael Mackinley
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
| | - Roberto Limongi
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | | | - Julie Richard
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
| | - Priya Subramanian
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
| | - Hooman Ganjavi
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- *Correspondence: Lena Palaniyappan,
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23
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Baxter T, Shenoy S, Lee HS, Griffith T, Rivas-Baxter A, Park S. Unequal outcomes: The effects of the COVID-19 pandemic on mental health and wellbeing among Hispanic/Latinos with varying degrees of 'Belonging'. Int J Soc Psychiatry 2022:207640221140285. [PMID: 36573293 PMCID: PMC9806202 DOI: 10.1177/00207640221140285] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND The COVID-19 pandemic has brought disparities in mental and physical health faced by ethnic minorities to the forefront. In the U.S., Hispanic/Latino communities are plagued by elevated rates of psychiatric conditions and trauma. Exacerbating this burden, common discourse often implicates Hispanic/Latino ethnicity as a causal factor, despite clear evidence of systemic causes, including lack of access to resources, and discrimination. AIMS To parse apart Hispanic/Latino ethnicity from determinants of wellbeing (such as trauma, financial status, and loneliness), we examined mental and physical health during COVID-19 via an online, anonymous survey available in both English and Spanish. METHODS We examined wellbeing across three participant groups, including two groups of Hispanic/Latino adults with varying degrees of 'belonging' to the dominant culture in their country of residence: Hispanic/Latino individuals living in Spanish-speaking and/or Central or Latin American countries (Group 1), Hispanic/Latino individuals living in the U.S. (Group 2), and non-Hispanic/Latino individuals living in the U.S. (Group 3). RESULTS Results demonstrated there were significant differences between groups in specific aspects of wellbeing. Most importantly, results showed Hispanic/Latino ethnicity does not significantly predict psychosocial wellbeing or psychosis risk, and identified several predictors of these outcomes, including U.S. residence, trauma, loneliness, and age. CONCLUSION Our results demonstrate that Hispanic/Latino ethnicity itself is not a causal factor of poor psychosocial wellbeing or elevated psychosis risk and instead identify several social and systemic causal factors commonly faced by Hispanic/Latino Americans. We suggest that language reporting on minority mental health acknowledge systemic factors as contributing to poor outcome rather than referring to ethnicity as if it were a causal factor.
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Affiliation(s)
- Tatiana Baxter
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - Sunil Shenoy
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - Hyeon-Seung Lee
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - Taylor Griffith
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | | | - Sohee Park
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
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24
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Chang X, Zhao W, Kang J, Xiang S, Xie C, Corona-Hernández H, Palaniyappan L, Feng J. Language abnormalities in schizophrenia: binding core symptoms through contemporary empirical evidence. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:95. [PMID: 36371445 PMCID: PMC9653408 DOI: 10.1038/s41537-022-00308-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Both the ability to speak and to infer complex linguistic messages from sounds have been claimed as uniquely human phenomena. In schizophrenia, formal thought disorder (FTD) and auditory verbal hallucinations (AVHs) are manifestations respectively relating to concrete disruptions of those abilities. From an evolutionary perspective, Crow (1997) proposed that "schizophrenia is the price that Homo sapiens pays for the faculty of language". Epidemiological and experimental evidence points to an overlap between FTD and AVHs, yet a thorough investigation examining their shared neural mechanism in schizophrenia is lacking. In this review, we synthesize observations from three key domains. First, neuroanatomical evidence indicates substantial shared abnormalities in language-processing regions between FTD and AVHs, even in the early phases of schizophrenia. Second, neurochemical studies point to a glutamate-related dysfunction in these language-processing brain regions, contributing to verbal production deficits. Third, genetic findings further show how genes that overlap between schizophrenia and language disorders influence neurodevelopment and neurotransmission. We argue that these observations converge into the possibility that a glutamatergic dysfunction in language-processing brain regions might be a shared neural basis of both FTD and AVHs. Investigations of language pathology in schizophrenia could facilitate the development of diagnostic tools and treatments, so we call for multilevel confirmatory analyses focused on modulations of the language network as a therapeutic goal in schizophrenia.
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Affiliation(s)
- Xiao Chang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Hugo Corona-Hernández
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada.
- Lawson Health Research Institute, London, Ontario, Canada.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Shanghai Center for Mathematical Sciences, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
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25
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Loch AA, Lopes-Rocha AC, Ara A, Gondim JM, Cecchi GA, Corcoran CM, Mota NB, Argolo FC. Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders. JMIR Ment Health 2022; 9:e41014. [PMID: 36318266 PMCID: PMC9667377 DOI: 10.2196/41014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/09/2022] [Accepted: 10/04/2022] [Indexed: 11/05/2022] Open
Abstract
Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.
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Affiliation(s)
- Alexandre Andrade Loch
- Institute of Psychiatry, University of Sao Paulo, Sao Paulo, Brazil.,Instituto Nacional de Biomarcadores em Neuropsiquiatria, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazilia, Brazil
| | | | - Anderson Ara
- Departamento de Estatística, Universidade Federal do Paraná, Curitiba, Brazil
| | | | - Guillermo A Cecchi
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, United States
| | | | - Natália Bezerra Mota
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.,Research Department at Motrix Lab, Motrix, Rio de Janeiro, Brazil
| | - Felipe C Argolo
- Institute of Psychiatry, University of Sao Paulo, Sao Paulo, Brazil
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26
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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: 13] [Impact Index Per Article: 4.3] [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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27
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Aramaki E, Wakamiya S, Yada S, Nakamura Y. Natural Language Processing: from Bedside to Everywhere. Yearb Med Inform 2022; 31:243-253. [PMID: 35654422 PMCID: PMC9719781 DOI: 10.1055/s-0042-1742510] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
OBJECTIVES Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions. METHODS We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas. RESULTS This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP. CONCLUSIONS These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.
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Affiliation(s)
- Eiji Aramaki
- Nara Institute of Science and Technology (NAIST), Nara, Japan
| | - Shoko Wakamiya
- Nara Institute of Science and Technology (NAIST), Nara, Japan
| | - Shuntaro Yada
- Nara Institute of Science and Technology (NAIST), Nara, Japan
| | - Yuta Nakamura
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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28
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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: 7] [Impact Index Per Article: 2.3] [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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29
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de la Salle S, Choueiry J, McIntosh J, Bowers H, Ilivitsky V, Knott V. N-methyl-D-aspartate receptor antagonism impairs sensory gating in the auditory cortex in response to speech stimuli. Psychopharmacology (Berl) 2022; 239:2155-2169. [PMID: 35348805 DOI: 10.1007/s00213-022-06090-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 02/15/2022] [Indexed: 10/18/2022]
Abstract
Deficits in early auditory sensory processing in schizophrenia have been linked to N-methyl-D-aspartate receptor (NMDAR) hypofunction, but the role of NMDARs in aberrant auditory sensory gating (SG) in this disorder is unclear. This study, conducted in 22 healthy humans, examined the acute effects of a subanesthetic dose of the NMDAR antagonist ketamine on SG as measured electrophysiologically by suppression of the P50 event-related potential (ERP) to the second (S2) relative to the first (S1) of two closely paired (500 ms) identical speech stimuli. Ketamine induced impairment in SG indices at sensor (scalp)-level and at source-level in the auditory cortex (as assessed with eLORETA). Together with preliminary evidence of modest positive associations between impaired gating and dissociative symptoms elicited by ketamine, tentatively support a model of NMDAR hypofunction underlying disturbances in auditory SG in schizophrenia.
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Affiliation(s)
- Sara de la Salle
- The Royal's Institute of Mental Health Research, Royal Ottawa Mental Health Centre, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
| | - Joelle Choueiry
- The Royal's Institute of Mental Health Research, Royal Ottawa Mental Health Centre, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada.,Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Judy McIntosh
- The Royal's Institute of Mental Health Research, Royal Ottawa Mental Health Centre, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
| | - Hayley Bowers
- Department of Psychology, University of Guelph, Guelph, ON, Canada
| | - Vadim Ilivitsky
- The Royal's Institute of Mental Health Research, Royal Ottawa Mental Health Centre, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
| | - Verner Knott
- The Royal's Institute of Mental Health Research, Royal Ottawa Mental Health Centre, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada. .,Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.
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30
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Jang J, Yoon S, Son G, Kang M, Choeh JY, Choi KH. Predicting Personality and Psychological Distress Using Natural Language Processing: A Study Protocol. Front Psychol 2022; 13:865541. [PMID: 35465529 PMCID: PMC9022676 DOI: 10.3389/fpsyg.2022.865541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Background Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one's personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological construct to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that of psychology due to small data sets and unvalidated modeling practices. Aims The current article introduces the study method and procedure of phase II which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods Phase I (pilot) study was conducted to fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 300 Korean adults will be recruited using a convenience sampling method via online survey. The text data collected from interviews will be analyzed using the natural language processing. The results of the online survey including demographic data, depression, anxiety, and personality inventories will be analyzed together in the model to predict individuals' FFM of personality and the level of psychological distress (phase 2).
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Affiliation(s)
- Jihee Jang
- School of Psychology, Korea University, Seoul, South Korea
| | - Seowon Yoon
- School of Psychology, Korea University, Seoul, South Korea
| | - Gaeun Son
- School of Psychology, Korea University, Seoul, South Korea
| | - Minjung Kang
- School of Psychology, Korea University, Seoul, South Korea
| | - Joon Yeon Choeh
- Department of Software, Sejong University, Seoul, South Korea
| | - Kee-Hong Choi
- School of Psychology, Korea University, Seoul, South Korea
- KU Mind Health Institute, Korea University, Seoul, South Korea
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31
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Exploring Language Markers of Mental Health in Psychiatric Stories. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042179] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Diagnosing mental disorders is complex due to the genetic, environmental and psychological contributors and the individual risk factors. Language markers for mental disorders can help to diagnose a person. Research thus far on language markers and the associated mental disorders has been done mainly with the Linguistic Inquiry and Word Count (LIWC) program. In order to improve on this research, we employed a range of Natural Language Processing (NLP) techniques using LIWC, spaCy, fastText and RobBERT to analyse Dutch psychiatric interview transcriptions with both rule-based and vector-based approaches. Our primary objective was to predict whether a patient had been diagnosed with a mental disorder, and if so, the specific mental disorder type. Furthermore, the second goal of this research was to find out which words are language markers for which mental disorder. LIWC in combination with the random forest classification algorithm performed best in predicting whether a person had a mental disorder or not (accuracy: 0.952; Cohen’s kappa: 0.889). SpaCy in combination with random forest predicted best which particular mental disorder a patient had been diagnosed with (accuracy: 0.429; Cohen’s kappa: 0.304).
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32
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Morozova A, Zorkina Y, Abramova O, Pavlova O, Pavlov K, Soloveva K, Volkova M, Alekseeva P, Andryshchenko A, Kostyuk G, Gurina O, Chekhonin V. Neurobiological Highlights of Cognitive Impairment in Psychiatric Disorders. Int J Mol Sci 2022; 23:1217. [PMID: 35163141 PMCID: PMC8835608 DOI: 10.3390/ijms23031217] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/18/2022] [Accepted: 01/20/2022] [Indexed: 02/07/2023] Open
Abstract
This review is focused on several psychiatric disorders in which cognitive impairment is a major component of the disease, influencing life quality. There are plenty of data proving that cognitive impairment accompanies and even underlies some psychiatric disorders. In addition, sources provide information on the biological background of cognitive problems associated with mental illness. This scientific review aims to summarize the current knowledge about neurobiological mechanisms of cognitive impairment in people with schizophrenia, depression, mild cognitive impairment and dementia (including Alzheimer's disease).The review provides data about the prevalence of cognitive impairment in people with mental illness and associated biological markers.
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Affiliation(s)
- Anna Morozova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Yana Zorkina
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Olga Abramova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Olga Pavlova
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Konstantin Pavlov
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Kristina Soloveva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Maria Volkova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Polina Alekseeva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Alisa Andryshchenko
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Georgiy Kostyuk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Olga Gurina
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Vladimir Chekhonin
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
- Department of Medical Nanobiotechnology, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
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Lekkas D, Gyorda JA, Price GD, Wortzman ZM, Jacobson NC. The Language of the Times: Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior across the United States. J Med Internet Res 2021; 24:e32731. [PMID: 34932494 PMCID: PMC8805454 DOI: 10.2196/32731] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 11/29/2021] [Accepted: 11/30/2021] [Indexed: 12/14/2022] Open
Abstract
Background The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Objective Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Methods Using COVID-19–related news headlines from a database of online news stories in conjunction with mental health–related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Results Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA β=–.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA β=.221; P<.001) contributing generally to an increase in online mental health search term frequency. Conclusions This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health–related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.
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Affiliation(s)
- Damien Lekkas
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra ParkwaySuite 300, Office #313S, Lebanon, US.,Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, US
| | - Joseph A Gyorda
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra ParkwaySuite 300, Office #313S, Lebanon, US
| | - George D Price
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra ParkwaySuite 300, Office #313S, Lebanon, US.,Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, US
| | - Zoe M Wortzman
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra ParkwaySuite 300, Office #313S, Lebanon, US
| | - Nicholas C Jacobson
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra ParkwaySuite 300, Office #313S, Lebanon, US.,Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, US.,Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, US.,Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, US
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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: 7] [Impact Index Per Article: 1.8] [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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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: 31] [Impact Index Per Article: 7.8] [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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Abstract
Telepsychiatry refers to the use of technology to support the remote provision of psychiatric services. Discussions of this technology have often focussed on the use of video conferencing in place of in-person visits and how such care is found to be non-inferior to traditional care. New developments in the fields of remote-sensing and digital phenotyping have the potential to overcome the limitations inherent in remote visits as well as the limitations of current outpatient care models more generally. Such technologies may enable the collection of more relevant, objective clinical data which could lead to improved care quality and transformed care delivery models. The development and implementation of these new technologies raise important ethical questions.
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Affiliation(s)
- John Zulueta
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Olusola A Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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Singh S, Germine L. Technology meets tradition: a hybrid model for implementing digital tools in neuropsychology. Int Rev Psychiatry 2021; 33:382-393. [PMID: 33236657 DOI: 10.1080/09540261.2020.1835839] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The COVID-19 pandemic has significantly impacted the provision of mental health care services and the ability to provide neuropsychological evaluations. The inability to conduct traditional evaluations has left neuropsychologists with the unprecedented task of determining how to modify existing paradigms while balancing the need to provide services and adhere to safety parameters. The COVID-19 literature suggests clinicians are modifying their evaluations based on the following models: (1) continuing to administer in-person evaluations; (2) discontinuing all evaluations due to issues related to standardization, test security, and patient-specific characteristics; (3) conducting virtual evaluations; and/or (4) adopting a hybrid model incorporating both traditional and technology-based modalities. Given the challenges with models 1-3, along with the modifications in telehealth guidelines and insurance reimbursement rates, neuropsychologists are more poised than ever to solidify the implementation of a hybrid model that lasts beyond COVID-19. We introduce the term Hybrid Neuropsychology, a model for the future of neuropsychological evaluations that includes three Action Items: (1) building a technology-based practice; (2) integrating data science; and (3) engaging with innovators in other fields. Hybrid Neuropsychology will enable clinicians to effectively modernize their practice, improve health care equity, and ensure neuropsychology secures its place in a technology-based world.
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Affiliation(s)
- Shifali Singh
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Laura Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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38
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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: 17] [Impact Index Per Article: 4.3] [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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39
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Barch DM. Understanding the Nature and Treatment of Psychopathology: Can the Data Guide the Way? BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:719-722. [PMID: 32771177 DOI: 10.1016/j.bpsc.2020.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 10/23/2022]
Affiliation(s)
- Deanna M Barch
- Departments of Psychological and Brain Sciences, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri.
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