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Bradley ER, Portanova J, Woolley JD, Buck B, Painter IS, Hankin M, Xu W, Cohen T. Quantifying abnormal emotion processing: A novel computational assessment method and application in schizophrenia. Psychiatry Res 2024; 336:115893. [PMID: 38657475 DOI: 10.1016/j.psychres.2024.115893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/31/2023] [Accepted: 04/03/2024] [Indexed: 04/26/2024]
Abstract
Abnormal emotion processing is a core feature of schizophrenia spectrum disorders (SSDs) that encompasses multiple operations. While deficits in some areas have been well-characterized, we understand less about abnormalities in the emotion processing that happens through language, which is highly relevant for social life. Here, we introduce a novel method using deep learning to estimate emotion processing rapidly from spoken language, testing this approach in male-identified patients with SSDs (n = 37) and healthy controls (n = 51). Using free responses to evocative stimuli, we derived a measure of appropriateness, or "emotional alignment" (EA). We examined psychometric characteristics of EA and its sensitivity to a single-dose challenge of oxytocin, a neuropeptide shown to enhance the salience of socioemotional information in SSDs. Patients showed impaired EA relative to controls, and impairment correlated with poorer social cognitive skill and more severe motivation and pleasure deficits. Adding EA to a logistic regression model with language-based measures of formal thought disorder (FTD) improved classification of patients versus controls. Lastly, oxytocin administration improved EA but not FTD among patients. While additional validation work is needed, these initial results suggest that an automated assay using spoken language may be a promising approach to assess emotion processing in SSDs.
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Affiliation(s)
- Ellen R Bradley
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; San Francisco Veterans Affairs Medical Center, CA, USA.
| | - Jake Portanova
- Department of Biomedical Informatics and Medical Education, University of Washington, WA, USA
| | - Josh D Woolley
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; San Francisco Veterans Affairs Medical Center, CA, USA
| | - Benjamin Buck
- Behavioral Research in Technology and Engineering (BRiTE) Center, Department of Psychiatry and Behavioral Sciences, University of Washington, USA
| | - Ian S Painter
- Department of Statistics, University of Washington, USA
| | | | - Weizhe Xu
- Department of Biomedical Informatics and Medical Education, University of Washington, WA, USA
| | - Trevor Cohen
- Department of Biomedical Informatics and Medical Education, University of Washington, WA, USA; Behavioral Research in Technology and Engineering (BRiTE) Center, Department of Psychiatry and Behavioral Sciences, University of Washington, USA
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Stein F, Gruber M, Mauritz M, Brosch K, Pfarr JK, Ringwald KG, Thomas-Odenthal F, Wroblewski A, Evermann U, Steinsträter O, Grumbach P, Thiel K, Winter A, Bonnekoh LM, Flinkenflügel K, Goltermann J, Meinert S, Grotegerd D, Bauer J, Opel N, Hahn T, Leehr EJ, Jansen A, de Lange SC, van den Heuvel MP, Nenadić I, Krug A, Dannlowski U, Repple J, Kircher T. Brain Structural Network Connectivity of Formal Thought Disorder Dimensions in Affective and Psychotic Disorders. Biol Psychiatry 2024; 95:629-638. [PMID: 37207935 DOI: 10.1016/j.biopsych.2023.05.010] [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: 11/03/2022] [Revised: 04/14/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND The psychopathological syndrome of formal thought disorder (FTD) is not only present in schizophrenia (SZ), but also highly prevalent in major depressive disorder and bipolar disorder. It remains unknown how alterations in the structural white matter connectome of the brain correlate with psychopathological FTD dimensions across affective and psychotic disorders. METHODS Using FTD items of the Scale for the Assessment of Positive Symptoms and Scale for the Assessment of Negative Symptoms, we performed exploratory and confirmatory factor analyses in 864 patients with major depressive disorder (n= 689), bipolar disorder (n = 108), or SZ (n = 67) to identify psychopathological FTD dimensions. We used T1- and diffusion-weighted magnetic resonance imaging to reconstruct the structural connectome of the brain. To investigate the association of FTD subdimensions and global structural connectome measures, we employed linear regression models. We used network-based statistic to identify subnetworks of white matter fiber tracts associated with FTD symptomatology. RESULTS Three psychopathological FTD dimensions were delineated, i.e., disorganization, emptiness, and incoherence. Disorganization and incoherence were associated with global dysconnectivity. Network-based statistics identified subnetworks associated with the FTD dimensions disorganization and emptiness but not with the FTD dimension incoherence. Post hoc analyses on subnetworks did not reveal diagnosis × FTD dimension interaction effects. Results remained stable after correcting for medication and disease severity. Confirmatory analyses showed a substantial overlap of nodes from both subnetworks with cortical brain regions previously associated with FTD in SZ. CONCLUSIONS We demonstrated white matter subnetwork dysconnectivity in major depressive disorder, bipolar disorder, and SZ associated with FTD dimensions that predominantly comprise brain regions implicated in speech. Results open an avenue for transdiagnostic, psychopathology-informed, dimensional studies in pathogenetic research.
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Affiliation(s)
- Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany.
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Marco Mauritz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Kai G Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Pascal Grumbach
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Linda M Bonnekoh
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Radiology, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich Schiller University Jena, Jena, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Siemon C de Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands; Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, the Netherlands
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
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Fradkin I, Nour MM, Dolan RJ. Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1013-1023. [PMID: 37257754 DOI: 10.1016/j.bpsc.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/19/2023] [Accepted: 05/19/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD. METHODS We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and tangentiality (how quickly meaning drifts away from the topic) in detecting and dissociating the 2 underlying impairments. RESULTS Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were nonmonotonic in that forgetting the global context resulted in sentences that were semantically closer to their local, intermediate context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance. CONCLUSIONS This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial.
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Affiliation(s)
- Isaac Fradkin
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom.
| | - Matthew M Nour
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Raymond J Dolan
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Wellcome Trust Centre for Human Neuroimaging, University College London, London, United Kingdom; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Corona-Hernández H, de Boer JN, Brederoo SG, Voppel AE, Sommer IEC. Assessing coherence through linguistic connectives: Analysis of speech in patients with schizophrenia-spectrum disorders. Schizophr Res 2023; 259:48-58. [PMID: 35778234 DOI: 10.1016/j.schres.2022.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND Incoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD. METHODS Connectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups. RESULTS SSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p < .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p < .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy. DISCUSSION Our results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD.
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Affiliation(s)
- H Corona-Hernández
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, the Netherlands.
| | - J N de Boer
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, the Netherlands; Department of Psychiatry, University Medical Center Utrecht, Utrecht University & Brain Center Rudolf Magnus, Utrecht, the Netherlands
| | - S G Brederoo
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, the Netherlands; Department of Psychiatry, University Medical Center Groningen, University of Groningen, the Netherlands
| | - A E Voppel
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, the Netherlands
| | - I E C Sommer
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, the Netherlands; Department of Psychiatry, University Medical Center Groningen, University of Groningen, the Netherlands
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Parola A, Lin JM, Simonsen A, Bliksted V, Zhou Y, Wang H, Inoue L, Koelkebeck K, Fusaroli R. Speech disturbances in schizophrenia: Assessing cross-linguistic generalizability of NLP automated measures of coherence. Schizophr Res 2023; 259:59-70. [PMID: 35927097 DOI: 10.1016/j.schres.2022.07.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/29/2022] [Accepted: 07/01/2022] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Language disorders - disorganized and incoherent speech in particular - are distinctive features of schizophrenia. Natural language processing (NLP) offers automated measures of incoherent speech as promising markers for schizophrenia. However, the scientific and clinical impact of NLP markers depends on their generalizability across contexts, samples, and languages, which we systematically assessed in the present study relying on a large, novel, cross-linguistic corpus. METHODS We collected a Danish (DK), German (GE), and Chinese (CH) cross-linguistic dataset involving transcripts from 187 participants with schizophrenia (111DK, 25GE, 51CH) and 200 matched controls (129DK, 29GE, 42CH) performing the Animated Triangles Task. Fourteen previously published NLP coherence measures were calculated, and between-groups differences and association with symptoms were tested for cross-linguistic generalizability. RESULTS One coherence measure, i.e. second-order coherence, robustly generalized across samples and languages. We found several language-specific effects, some of which partially replicated previous findings (lower coherence in German and Chinese patients), while others did not (higher coherence in Danish patients). We found several associations between symptoms and measures of coherence, but the effects were generally inconsistent across languages and rating scales. CONCLUSIONS Using a cumulative approach, we have shown that NLP findings of reduced semantic coherence in schizophrenia have limited generalizability across different languages, samples, and measures. We argue that several factors such as sociodemographic and clinical heterogeneity, cross-linguistic variation, and the different NLP measures reflecting different clinical aspects may be responsible for this variability. Future studies should take this variability into account in order to develop effective clinical applications targeting different patient populations.
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Affiliation(s)
- Alberto Parola
- Department of Linguistics, Semiotics and Cognitive Science, Aarhus University, Aarhus, Denmark; The Interacting Minds Centre, Institute of Culture and Society, Aarhus University, Aarhus, Denmark.
| | - Jessica Mary Lin
- Department of Linguistics, Semiotics and Cognitive Science, Aarhus University, Aarhus, Denmark; The Interacting Minds Centre, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
| | - Arndis Simonsen
- The Interacting Minds Centre, Institute of Culture and Society, Aarhus University, Aarhus, Denmark; Psychosis Research Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Vibeke Bliksted
- The Interacting Minds Centre, Institute of Culture and Society, Aarhus University, Aarhus, Denmark; Psychosis Research Unit, Department of Clinical Medicine, 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
| | - Lana Inoue
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Hospital and Institute of the University of Duisburg-Essen, Essen, Germany; Center for Translational Neuro- & Behavioral Sciences (C-TNBS), University Duisburg Essen, Germany
| | - 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- & Behavioral Sciences (C-TNBS), University Duisburg Essen, Germany
| | - Riccardo Fusaroli
- Department of Linguistics, Semiotics and Cognitive Science, Aarhus University, Aarhus, Denmark; The Interacting Minds Centre, Institute of Culture and Society, Aarhus University, Aarhus, Denmark; Linguistic Data Consortium, University of Pennsylvania, Philadelphia, USA
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Schneider K, Leinweber K, Jamalabadi H, Teutenberg L, Brosch K, Pfarr JK, Thomas-Odenthal F, Usemann P, Wroblewski A, Straube B, Alexander N, Nenadić I, Jansen A, Krug A, Dannlowski U, Kircher T, Nagels A, Stein F. Syntactic complexity and diversity of spontaneous speech production in schizophrenia spectrum and major depressive disorders. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:35. [PMID: 37248240 DOI: 10.1038/s41537-023-00359-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/25/2023] [Indexed: 05/31/2023]
Abstract
Syntax, the grammatical structure of sentences, is a fundamental aspect of language. It remains debated whether reduced syntactic complexity is unique to schizophrenia spectrum disorder (SSD) or whether it is also present in major depressive disorder (MDD). Furthermore, the association of syntax (including syntactic complexity and diversity) with language-related neuropsychology and psychopathological symptoms across disorders remains unclear. Thirty-four SSD patients and thirty-eight MDD patients diagnosed according to DSM-IV-TR as well as forty healthy controls (HC) were included and tasked with describing four pictures from the Thematic Apperception Test. We analyzed the produced speech regarding its syntax delineating measures for syntactic complexity (the total number of main clauses embedding subordinate clauses) and diversity (number of different types of complex sentences). We performed cluster analysis to identify clusters based on syntax and investigated associations of syntactic, to language-related neuropsychological (verbal fluency and verbal episodic memory), and psychopathological measures (positive and negative formal thought disorder) using network analyses. Syntax in SSD was significantly reduced in comparison to MDD and HC, whereas the comparison of HC and MDD revealed no significant differences. No associations were present between speech measures and current medication, duration and severity of illness, age or sex; the single association accounted for was education. A cluster analysis resulted in four clusters with different degrees of syntax across diagnoses. Subjects with less syntax exhibited pronounced positive and negative symptoms and displayed poorer performance in executive functioning, global functioning, and verbal episodic memory. All cluster-based networks indicated varying degrees of domain-specific and cross-domain connections. Measures of syntactic complexity were closely related while syntactic diversity appeared to be a separate node outside of the syntactic network. Cross-domain associations were more salient in more complex syntactic production.
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Affiliation(s)
- Katharina Schneider
- Department of English and Linguistics, General Linguistics, University of Mainz, Mainz, Germany.
| | - Katrin Leinweber
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Arne Nagels
- Department of English and Linguistics, General Linguistics, University of Mainz, Mainz, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
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Ciampelli S, Voppel AE, de Boer JN, Koops S, Sommer IEC. Combining automatic speech recognition with semantic natural language processing in schizophrenia. Psychiatry Res 2023; 325:115252. [PMID: 37236098 DOI: 10.1016/j.psychres.2023.115252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/21/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023]
Abstract
Natural language processing (NLP) tools are increasingly used to quantify semantic anomalies in schizophrenia. Automatic speech recognition (ASR) technology, if robust enough, could significantly speed up the NLP research process. In this study, we assessed the performance of a state-of-the-art ASR tool and its impact on diagnostic classification accuracy based on a NLP model. We compared ASR to human transcripts quantitatively (Word Error Rate (WER)) and qualitatively by analyzing error type and position. Subsequently, we evaluated the impact of ASR on classification accuracy using semantic similarity measures. Two random forest classifiers were trained with similarity measures derived from automatic and manual transcriptions, and their performance was compared. The ASR tool had a mean WER of 30.4%. Pronouns and words in sentence-final position had the highest WERs. The classification accuracy was 76.7% (sensitivity 70%; specificity 86%) using automated transcriptions and 79.8% (sensitivity 75%; specificity 86%) for manual transcriptions. The difference in performance between the models was not significant. These findings demonstrate that using ASR for semantic analysis is associated with only a small decrease in accuracy in classifying schizophrenia, compared to manual transcripts. Thus, combining ASR technology with semantic NLP models qualifies as a robust and efficient method for diagnosing schizophrenia.
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Affiliation(s)
- S Ciampelli
- Department of Psychiatry, University Medical Center Groningen, Groningen, the Netherlands.
| | - A E Voppel
- Department of Psychiatry, University Medical Center Groningen, Groningen, the Netherlands
| | - J N de Boer
- Department of Psychiatry, University Medical Center Groningen, Groningen, the Netherlands; Department of Psychiatry, Department of Intensive Care Medicine, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - S Koops
- Department of Psychiatry, University Medical Center Groningen, Groningen, the Netherlands
| | - I E C Sommer
- Department of Psychiatry, University Medical Center Groningen, Groningen, the Netherlands
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8
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Voppel AE, de Boer JN, Brederoo SG, Schnack HG, Sommer IEC. Semantic and Acoustic Markers in Schizophrenia-Spectrum Disorders: A Combinatory Machine Learning Approach. Schizophr Bull 2023; 49:S163-S171. [PMID: 36305054 PMCID: PMC10031732 DOI: 10.1093/schbul/sbac142] [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] [Indexed: 11/13/2022]
Abstract
BACKGROUND AND HYPOTHESIS Speech is a promising marker to aid diagnosis of schizophrenia-spectrum disorders, as it reflects symptoms like thought disorder and negative symptoms. Previous approaches made use of different domains of speech for diagnostic classification, including features like coherence (semantic) and form (acoustic). However, an examination of the added value of each domain when combined is lacking as of yet. Here, we investigate the acoustic and semantic domains separately and combined. STUDY DESIGN Using semi-structured interviews, speech of 94 subjects with schizophrenia-spectrum disorders (SSD) and 73 healthy controls (HC) was recorded. Acoustic features were extracted using a standardized feature-set, and transcribed interviews were used to calculate semantic word similarity using word2vec. Random forest classifiers were trained for each domain. A third classifier was used to combine features from both domains; 10-fold cross-validation was used for each model. RESULTS The acoustic random forest classifier achieved 81% accuracy classifying SSD and HC, while the semantic domain classifier reached an accuracy of 80%. Joining features from the two domains, the combined classifier reached 85% accuracy, significantly improving on separate domain classifiers. For the combined classifier, top features were fragmented speech from the acoustic domain and variance of similarity from the semantic domain. CONCLUSIONS Both semantic and acoustic analyses of speech achieved ~80% accuracy in classifying SSD from HC. We replicate earlier findings per domain, additionally showing that combining these features significantly improves classification performance. Feature importance and accuracy in combined classification indicate that the domains measure different, complementing aspects of speech.
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Affiliation(s)
- Alban E Voppel
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Janna N de Boer
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Sanne G Brederoo
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Hugo G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Utrecht University, Utrecht Institute of Linguistics OTS, Utrecht, the Netherlands
| | - Iris E C Sommer
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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9
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Zengaffinen F, Stahnke A, Furger S, Wiest R, Dierks T, Strik W, Morishima Y. Computational analysis on verbal fluency reveals heterogeneity in subjective language interests and brain structure. NEUROIMAGE. REPORTS 2023; 3:100159. [PMID: 38606311 PMCID: PMC7615821 DOI: 10.1016/j.ynirp.2023.100159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Language is an essential higher cognitive function in humans and is often affected by psychiatric and neurological disorders. Objective measures like the verbal fluency test are often used to determine language dysfunction. Recent applications of computational approaches broaden insights into language-related functions. In addition, individuals diagnosed with a psychiatric or neurological disorder also often report subjective difficulties in language-related functions. Therefore, we investigated the association between objective and subjective measures of language functioning, on the one hand, and inter-individual structural variations in language-related brain areas, on the other hand. We performed a Latent Semantic analysis (LSA) on a semantic verbal fluency task in 101 healthy adult participants. To investigate if these objective measures are associated with a subjective one, we examined assessed subjective natural tendency of interest in language-related activity with a study-specific questionnaire. Lastly, a voxel-based brain morphometry (VBM) was conducted to reveal associations between objective (LSA) measures and structural changes in language-related brain areas. We found a positive correlation between the LSA measure cosine similarity and the subjective interest in language. Furthermore, we found that higher cosine similarity corresponds to higher gray matter volume in the right cerebellum. The results suggest that people with higher interests in language access semantic knowledge in a more organized way exhibited by higher cosine similarity and have larger grey matter volume in the right cerebellum, when compared to people with lower interests. In conclusion, we demonstrate that there is inter-individual diverseness of accessing the semantic knowledge space and that it is associated with subjective language interests as well as structural differences in the right cerebellum.
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Affiliation(s)
- Francilia Zengaffinen
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Antje Stahnke
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Stephan Furger
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Roland Wiest
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Thomas Dierks
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Werner Strik
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Yosuke Morishima
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
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10
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de Boer JN, Voppel AE, Brederoo SG, Schnack HG, Truong KP, Wijnen FNK, Sommer IEC. Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool. Psychol Med 2023; 53:1302-1312. [PMID: 34344490 PMCID: PMC10009369 DOI: 10.1017/s0033291721002804] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 06/10/2021] [Accepted: 06/21/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. METHODS Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. RESULTS The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. CONCLUSIONS Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.
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Affiliation(s)
- J. N. de Boer
- Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University & University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - A. E. Voppel
- Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - S. G. Brederoo
- Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - H. G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University & University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
- Utrecht Institute of Linguistics OTS, Utrecht University, Utrecht, the Netherlands
| | - K. P. Truong
- Department of Human Media Interaction, University of Twente, Enschede, the Netherlands
| | - F. N. K. Wijnen
- Utrecht Institute of Linguistics OTS, Utrecht University, Utrecht, the Netherlands
| | - I. E. C. Sommer
- Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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11
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Minor KS, Lundin NB, Myers EJ, Fernández-Villardón A, Lysaker PH. Automated measures of speech content and speech organization in schizophrenia: Test-retest reliability and generalizability across demographic variables. Psychiatry Res 2023; 320:115048. [PMID: 36645988 DOI: 10.1016/j.psychres.2023.115048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023]
Abstract
Technological advances in artificial intelligence and natural language processing have increased efficiency of assessing speech content and speech organization in schizophrenia. Despite these developments, there has been little focus on the psychometrics of these approaches. Using two common assessments, the current study addressed this gap by: 1) measuring test-retest reliability; and 2) assessing whether speech content and/or speech organization generalize across demographics. To test these aims, we examined psychometric properties of the Linguistic Inquiry Word Count (LIWC), a speech content measure, and the Coh-Metrix, a speech organization measure. Across baseline to six month (n = 101) and baseline to one year (n = 47) narrative speech samples, we generally observed fair reliability for speech content measures and fair to good reliability for speech organization measures. Regarding demographics, multiple speech indices varied by race, income, and education. The lack of excellent reliability scores for speech indices holds important implications for examining speech variables in clinical trials and highlights the dynamic nature of speech. This work illustrates the importance of designing speech content and speech organization measures with external validity across demographic factors. Future studies examining speech in schizophrenia should account for potential biases against demographic groups introduced by linguistic analysis tools.
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Affiliation(s)
- Kyle S Minor
- Department of Psychology, Indiana University- Purdue University Indianapolis, Indianapolis, IN, United States.
| | - Nancy B Lundin
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, United States
| | - Evan J Myers
- Department of Psychology, Indiana University- Purdue University Indianapolis, Indianapolis, IN, United States
| | | | - Paul H Lysaker
- Roudebush VA Medical Center, Indianapolis, IN, United States; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
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12
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Applications of Speech Analysis in Psychiatry. Harv Rev Psychiatry 2023; 31:1-13. [PMID: 36608078 DOI: 10.1097/hrp.0000000000000356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
ABSTRACT The need for objective measurement in psychiatry has stimulated interest in alternative indicators of the presence and severity of illness. Speech may offer a source of information that bridges the subjective and objective in the assessment of mental disorders. We systematically reviewed the literature for articles exploring speech analysis for psychiatric applications. The utility of speech analysis depends on how accurately speech features represent clinical symptoms within and across disorders. We identified four domains of the application of speech analysis in the literature: diagnostic classification, assessment of illness severity, prediction of onset of illness, and prognosis and treatment outcomes. We discuss the findings in each of these domains, with a focus on how types of speech features characterize different aspects of psychopathology. Models that bring together multiple speech features can distinguish speakers with psychiatric disorders from healthy controls with high accuracy. Differentiating between types of mental disorders and symptom dimensions are more complex problems that expose the transdiagnostic nature of speech features. Convergent progress in speech research and computer sciences opens avenues for implementing speech analysis to enhance objectivity of assessment in clinical practice. Application of speech analysis will need to address issues of ethics and equity, including the potential to perpetuate discriminatory bias through models that learn from clinical assessment data. Methods that mitigate bias are available and should play a key role in the implementation of speech analysis.
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13
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Koops S, Brederoo SG, de Boer JN, Nadema FG, Voppel AE, Sommer IE. Speech as a Biomarker for Depression. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:152-160. [PMID: 34961469 DOI: 10.2174/1871527320666211213125847] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/10/2021] [Accepted: 10/10/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Depression is a debilitating disorder that at present lacks a reliable biomarker to aid in diagnosis and early detection. Recent advances in computational analytic approaches have opened up new avenues in developing such a biomarker by taking advantage of the wealth of information that can be extracted from a person's speech. OBJECTIVE The current review provides an overview of the latest findings in the rapidly evolving field of computational language analysis for the detection of depression. We cover a wide range of both acoustic and content-related linguistic features, data types (i.e., spoken and written language), and data sources (i.e., lab settings, social media, and smartphone-based). We put special focus on the current methodological advances with regard to feature extraction and computational modeling techniques. Furthermore, we pay attention to potential hurdles in the implementation of automatic speech analysis. CONCLUSION Depressive speech is characterized by several anomalies, such as lower speech rate, less pitch variability and more self-referential speech. With current computational modeling techniques, such features can be used to detect depression with an accuracy of up to 91%. The performance of the models is optimized when machine learning techniques are implemented that suit the type and amount of data. Recent studies now work towards further optimization and generalizability of the computational language models to detect depression. Finally, privacy and ethical issues are of paramount importance to be addressed when automatic speech analysis techniques are further implemented in, for example, smartphones. Altogether, computational speech analysis is well underway towards becoming an effective diagnostic aid for depression.
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Affiliation(s)
- Sanne Koops
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Sanne G Brederoo
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
- University Center for Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
| | - Janna N de Boer
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University & Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Femke G Nadema
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Alban E Voppel
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
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14
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Hartopo D, Kalalo RT. Language disorder as a marker for schizophrenia. Asia Pac Psychiatry 2022; 14:e12485. [PMID: 34328267 DOI: 10.1111/appy.12485] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/24/2021] [Accepted: 07/05/2021] [Indexed: 11/26/2022]
Abstract
A language disorder is a significant symptom of schizophrenia. A psychiatrist can find this disorder when interviews with a patient. Screening and diagnosis in patients with schizophrenia alone rely heavily on interviews conducted on patients and any instructions captured from patients both verbally and nonverbally. A psychiatrist can also analyze the language aspects in schizophrenia from a language level perspective ranging from phonetic to pragmatic. This analysis paves the way for the process of interference detection since the prodromal phase. Language disorder in schizophrenia is often associated with impaired thinking processes. However, with the development of science and technology today, there is an objective and quantitative method of computational analysis of language through the Natural Language Processing process with a semantic space model that allows a psychiatrist to learn aspects of the human language process, especially in semantic and pragmatic aspects. The review provides a groundbreaking proposal for biomarkers for schizophrenia that have not been available so far through the assessment of language disorders in patients with schizophrenia. Objective and accurate detection of language disorders in schizophrenia can be a modality for psychiatrists to screen, make diagnoses, determine prognosis, evaluate therapies, and monitor recurrence using existing technology media.
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Affiliation(s)
- Dhenni Hartopo
- Psychiatry Residency Program, Faculty of Medicine, Department of Psychiatry, Universitas Airlangga, Dr. Soetomo General Academic Hospital, Surabaya, Indonesia
| | - Royke Tony Kalalo
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Faculty of Medicine, Universitas Airlangga, Dr. Soetomo General Academic Hospital, Surabaya, Indonesia
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15
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Girard JM, Vail AK, Liebenthal E, Brown K, Kilciksiz CM, Pennant L, Liebson E, Öngür D, Morency LP, Baker JT. Computational analysis of spoken language in acute psychosis and mania. Schizophr Res 2022; 245:97-115. [PMID: 34456131 PMCID: PMC8881587 DOI: 10.1016/j.schres.2021.06.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/21/2021] [Accepted: 06/24/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES This study aimed to (1) determine the feasibility of collecting behavioral data from participants hospitalized with acute psychosis and (2) begin to evaluate the clinical information that can be computationally derived from such data. METHODS Behavioral data was collected across 99 sessions from 38 participants recruited from an inpatient psychiatric unit. Each session started with a semi-structured interview modeled on a typical "clinical rounds" encounter and included administration of the Positive and Negative Syndrome Scale (PANSS). ANALYSIS We quantified aspects of participants' verbal behavior during the interview using lexical, coherence, and disfluency features. We then used two complementary approaches to explore our second objective. The first approach used predictive models to estimate participants' PANSS scores from their language features. Our second approach used inferential models to quantify the relationships between individual language features and symptom measures. RESULTS Our predictive models showed promise but lacked sufficient data to achieve clinically useful accuracy. Our inferential models identified statistically significant relationships between numerous language features and symptom domains. CONCLUSION Our interview recording procedures were well-tolerated and produced adequate data for transcription and analysis. The results of our inferential modeling suggest that automatic measurements of expressive language contain signals highly relevant to the assessment of psychosis. These findings establish the potential of measuring language during a clinical interview in a naturalistic setting and generate specific hypotheses that can be tested in future studies. This, in turn, will lead to more accurate modeling and better understanding of the relationships between expressive language and psychosis.
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Affiliation(s)
- Jeffrey M. Girard
- Department of Psychology, University of Kansas, Lawrence, Kansas, USA
| | - Alexandria K. Vail
- Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Einat Liebenthal
- Division of Psychotic Disorders, McLean Hospital, Belmont, Massachusetts, USA,Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Katrina Brown
- Division of Psychotic Disorders, McLean Hospital, Belmont, Massachusetts, USA
| | - Can Misel Kilciksiz
- Division of Psychotic Disorders, McLean Hospital, Belmont, Massachusetts, USA
| | - Luciana Pennant
- Division of Psychotic Disorders, McLean Hospital, Belmont, Massachusetts, USA
| | - Elizabeth Liebson
- Division of Psychotic Disorders, McLean Hospital, Belmont, Massachusetts, USA,Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Dost Öngür
- Division of Psychotic Disorders, McLean Hospital, Belmont, Massachusetts, USA,Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Louis-Philippe Morency
- Language Technologies Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Justin T. Baker
- Division of Psychotic Disorders, McLean Hospital, Belmont, Massachusetts, USA,Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA,Corresponding author. (Justin T. Baker)
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16
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Birnbaum ML, Abrami A, Heisig S, Ali A, Arenare E, Agurto C, Lu N, Kane JM, Cecchi G. Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development. JMIR Ment Health 2022; 9:e24699. [PMID: 35072648 PMCID: PMC8822433 DOI: 10.2196/24699] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 04/29/2021] [Accepted: 12/01/2021] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions. OBJECTIVE We aimed to investigate whether reliable inferences-psychiatric signs, symptoms, and diagnoses-can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder. METHODS We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation. RESULTS The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner-pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61). CONCLUSIONS This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.
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Affiliation(s)
- Michael L Birnbaum
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States.,The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Avner Abrami
- Computational Biology Center, IBM Research, Yorktown Heights, NY, United States
| | - Stephen Heisig
- Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Asra Ali
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Elizabeth Arenare
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Carla Agurto
- Computational Biology Center, IBM Research, Yorktown Heights, NY, United States
| | - Nathaniel Lu
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States
| | - John M Kane
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States.,The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Guillermo Cecchi
- Computational Biology Center, IBM Research, Yorktown Heights, NY, United States
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17
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Crema C, Attardi G, Sartiano D, Redolfi A. Natural language processing in clinical neuroscience and psychiatry: A review. Front Psychiatry 2022; 13:946387. [PMID: 36186874 PMCID: PMC9515453 DOI: 10.3389/fpsyt.2022.946387] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services.
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Affiliation(s)
- Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Daniele Sartiano
- Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, Pisa, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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18
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Zinchuk M, Beghi M, Diotti S, Pashnin E, Kustov G, Rider F, Urh L, Guekht A, Cornaggia CM. Differential diagnosis between epileptic and psychogenic nonepileptic seizures through conversational analysis: A blinded prospective study in the Russian language. Epilepsy Behav 2021; 125:108441. [PMID: 34837840 DOI: 10.1016/j.yebeh.2021.108441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/09/2021] [Accepted: 11/09/2021] [Indexed: 10/19/2022]
Abstract
The current study examined the validity of conversational analysis (CA) in Russian patients with seizures, using a scoring table for the Simplified Linguistic Evaluation (SLE). The study sample was composed of 12 adult participants suffering either from epilepsy (ES) or psychogenic nonepileptic seizures (PNES) recruited in the Moscow Research and Clinical Center for Neuropsychiatry. Definitive diagnosis was established only after a habitual event was captured onvEEG. All participants with PNES or ES and at least one mental disorder underwent a 20-minute-long interview recorded on video. The interview then was evaluated by the external blinded physician already experienced in CA. Finally, that physician filled the SLE, consisting of 5 items analyzing the main characteristics of patient narrations. A score of ≥12 suggested a diagnosis of ES, while a score of <12 suggested a diagnosis of PNES. The blinded evaluator correctly identified 11 out of 12 cases. The concordance between the vEEG diagnosis and the CA diagnostic hypothesis was 91.67%. The sensitivity of the scoring table was 100%, while the specificity was 80%. The positive and the negative predictive values were, respectively, 87.5% and 100%. Our results suggested that the differences in seizure descriptions between patients with PNES and patients with ES are similar across Indo-European language family and are independent of psychiatric comorbidity.
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Affiliation(s)
- Mikhail Zinchuk
- Moscow Research and Clinical Center for Neuropsychiatry, Moscow, Russian Federation.
| | | | - Silvia Diotti
- University of Milano Bicocca, GSD Research, Milan, Italy
| | - Evgenii Pashnin
- Moscow Research and Clinical Center for Neuropsychiatry, Moscow, Russian Federation
| | - Georgii Kustov
- Moscow Research and Clinical Center for Neuropsychiatry, Moscow, Russian Federation
| | - Flora Rider
- Moscow Research and Clinical Center for Neuropsychiatry, Moscow, Russian Federation
| | - Lina Urh
- University of Milano Bicocca, GSD Research, Milan, Italy
| | - Alla Guekht
- Moscow Research and Clinical Center for Neuropsychiatry, Moscow, Russian Federation; Pirogov Russian National Research Medical University, Moscow, Russian Federation
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19
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Voppel AE, de Boer JN, Brederoo SG, Schnack HG, Sommer I. Quantified language connectedness in schizophrenia-spectrum disorders. Psychiatry Res 2021; 304:114130. [PMID: 34332431 DOI: 10.1016/j.psychres.2021.114130] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/13/2021] [Accepted: 07/16/2021] [Indexed: 01/02/2023]
Abstract
Language abnormalities are a core symptom of schizophrenia-spectrum disorders and could serve as a potential diagnostic marker. Natural language processing enables quantification of language connectedness, which may be lower in schizophrenia-spectrum disorders. Here, we investigated connectedness of spontaneous speech in schizophrenia-spectrum patients and controls and determine its accuracy in classification. Using a semi-structured interview, speech of 50 patients with a schizophrenia-spectrum disorder and 50 controls was recorded. Language connectedness in a semantic word2vec model was calculated using consecutive word similarity in moving windows of increasing sizes (2-20 words). Mean, minimal and variance of similarity were calculated per window size and used in a random forest classifier to distinguish patients and healthy controls. Classification based on connectedness reached 85% cross-validated accuracy, with 84% specificity and 86% sensitivity. Features that best discriminated patients from controls were variance of similarity at window sizes between 5 and 10. We show impaired connectedness in spontaneous speech of patients with schizophrenia-spectrum disorders even in patients with low ratings of positive symptoms. Effects were most prominent at the level of sentence connectedness. The high sensitivity, specificity and tolerability of this method show that language analysis is an accurate and feasible digital assistant in diagnosing schizophrenia-spectrum disorders.
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Affiliation(s)
- A E Voppel
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
| | - J N de Boer
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - S G Brederoo
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - H G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Utrecht University, Utrecht Institute of Linguistics OTS, Utrecht, the Netherlands
| | - Iec Sommer
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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20
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Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA. COMPLEXITY 2021. [DOI: 10.1155/2021/4551067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.
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21
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Birnbaum ML, Norel R, Van Meter A, Ali AF, Arenare E, Eyigoz E, Agurto C, Germano N, Kane JM, Cecchi GA. Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook. NPJ SCHIZOPHRENIA 2020; 6:38. [PMID: 33273468 PMCID: PMC7713057 DOI: 10.1038/s41537-020-00125-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 10/09/2020] [Indexed: 01/03/2023]
Abstract
Prior research has identified associations between social media activity and psychiatric diagnoses; however, diagnoses are rarely clinically confirmed. Toward the goal of applying novel approaches to improve outcomes, research using real patient data is necessary. We collected 3,404,959 Facebook messages and 142,390 images across 223 participants (mean age = 23.7; 41.7% male) with schizophrenia spectrum disorders (SSD), mood disorders (MD), and healthy volunteers (HV). We analyzed features uploaded up to 18 months before the first hospitalization using machine learning and built classifiers that distinguished SSD and MD from HV, and SSD from MD. Classification achieved AUC of 0.77 (HV vs. MD), 0.76 (HV vs. SSD), and 0.72 (SSD vs. MD). SSD used more (P < 0.01) perception words (hear, see, feel) than MD or HV. SSD and MD used more (P < 0.01) swear words compared to HV. SSD were more likely to express negative emotions compared to HV (P < 0.01). MD used more words related to biological processes (blood/pain) compared to HV (P < 0.01). The height and width of photos posted by SSD and MD were smaller (P < 0.01) than HV. MD photos contained more blues and less yellows (P < 0.01). Closer to hospitalization, use of punctuation increased (SSD vs HV), use of negative emotion words increased (MD vs. HV), and use of swear words increased (P < 0.01) for SSD and MD compared to HV. Machine-learning algorithms are capable of differentiating SSD and MD using Facebook activity alone over a year in advance of hospitalization. Integrating Facebook data with clinical information could one day serve to inform clinical decision-making.
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Affiliation(s)
- Michael L Birnbaum
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA.
- The Feinstein Institute for Medical Research, Manhasset, NY, USA.
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
| | - Raquel Norel
- IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, USA
| | - Anna Van Meter
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- The Feinstein Institute for Medical Research, Manhasset, NY, USA
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Asra F Ali
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Elizabeth Arenare
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Elif Eyigoz
- IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, USA
| | - Carla Agurto
- IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, USA
| | - Nicole Germano
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - John M Kane
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- The Feinstein Institute for Medical Research, Manhasset, NY, USA
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Guillermo A Cecchi
- IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, USA
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Birnbaum ML, Wen H, Van Meter A, Ernala SK, Rizvi AF, Arenare E, Estrin D, De Choudhury M, Kane JM. Identifying emerging mental illness utilizing search engine activity: A feasibility study. PLoS One 2020; 15:e0240820. [PMID: 33064759 PMCID: PMC7567375 DOI: 10.1371/journal.pone.0240820] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 10/04/2020] [Indexed: 11/18/2022] Open
Abstract
Mental illness often emerges during the formative years of adolescence and young adult development and interferes with the establishment of healthy educational, vocational, and social foundations. Despite the severity of symptoms and decline in functioning, the time between illness onset and receiving appropriate care can be lengthy. A method by which to objectively identify early signs of emerging psychiatric symptoms could improve early intervention strategies. We analyzed a total of 405,523 search queries from 105 individuals with schizophrenia spectrum disorders (SSD, N = 36), non-psychotic mood disorders (MD, N = 38) and healthy volunteers (HV, N = 31) utilizing one year's worth of data prior to the first psychiatric hospitalization. Across 52 weeks, we found significant differences in the timing (p<0.05) and frequency (p<0.001) of searches between individuals with SSD and MD compared to HV up to a year in advance of the first psychiatric hospitalization. We additionally identified significant linguistic differences in search content among the three groups including use of words related to sadness and perception, use of first and second person pronouns, and use of punctuation (all p<0.05). In the weeks before hospitalization, both participants with SSD and MD displayed significant shifts in search timing (p<0.05), and participants with SSD displayed significant shifts in search content (p<0.05). Our findings demonstrate promise for utilizing personal patterns of online search activity to inform clinical care.
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Affiliation(s)
- Michael L. Birnbaum
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America
- The Feinstein Institute for Medical Research, Manhasset, NY, United States of America
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States of America
- * E-mail:
| | - Hongyi Wen
- Cornell Tech, Cornell University, New York, NY, United States of America
| | - Anna Van Meter
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America
- The Feinstein Institute for Medical Research, Manhasset, NY, United States of America
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States of America
| | - Sindhu K. Ernala
- Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Asra F. Rizvi
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America
- The Feinstein Institute for Medical Research, Manhasset, NY, United States of America
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States of America
| | - Elizabeth Arenare
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America
- The Feinstein Institute for Medical Research, Manhasset, NY, United States of America
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States of America
| | - Deborah Estrin
- Cornell Tech, Cornell University, New York, NY, United States of America
| | | | - John M. Kane
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America
- The Feinstein Institute for Medical Research, Manhasset, NY, United States of America
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States of America
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Language disturbances in schizophrenia: the relation with antipsychotic medication. NPJ SCHIZOPHRENIA 2020; 6:24. [PMID: 32895389 PMCID: PMC7477551 DOI: 10.1038/s41537-020-00114-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 06/23/2020] [Indexed: 12/12/2022]
Abstract
Language disturbances are key aberrations in schizophrenia. Little is known about the influence of antipsychotic medication on these symptoms. Using computational language methods, this study evaluated the impact of high versus low dopamine D2 receptor (D2R) occupancy antipsychotics on language disturbances in 41 patients with schizophrenia, relative to 40 healthy controls. Patients with high versus low D2R occupancy antipsychotics differed by total number of words and type-token ratio, suggesting medication effects. Both patient groups differed from the healthy controls on percentage of time speaking and clauses per utterance, suggesting illness effects. Overall, more severe negative language disturbances (i.e. slower articulation rate, increased pausing, and shorter utterances) were seen in the patients that used high D2R occupancy antipsychotics, while less prominent disturbances were seen in low D2R occupancy patients. Language analyses successfully predicted drug type (sensitivity = 80.0%, specificity = 76.5%). Several language disturbances were more related to drug type and dose, than to other psychotic symptoms, suggesting that language disturbances may be aggravated by high D2R antipsychotics. This negative impact of high D2R occupancy drugs may have clinical implications, as impaired language production predicts functional outcome and degrades the quality of life.
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Haas SS, Doucet GE, Garg S, Herrera SN, Sarac C, Bilgrami ZR, Shaik RB, Corcoran CM. Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis. Eur Psychiatry 2020; 63:e72. [PMID: 32778184 PMCID: PMC7443790 DOI: 10.1192/j.eurpsy.2020.73] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Abnormalities in the semantic and syntactic organization of speech have been reported in individuals at clinical high-risk (CHR) for psychosis. The current study seeks to examine whether such abnormalities are associated with changes in brain structure and functional connectivity in CHR individuals. METHODS Automated natural language processing analysis was applied to speech samples obtained from 46 CHR and 22 healthy individuals. Brain structural and resting-state functional imaging data were also acquired from all participants. Sparse canonical correlation analysis (sCCA) was used to ascertain patterns of covariation between linguistic features, clinical symptoms, and measures of brain morphometry and functional connectivity related to the language network. RESULTS In CHR individuals, we found a significant mode of covariation between linguistic and clinical features (r = 0.73; p = 0.003), with negative symptoms and bizarre thinking covarying mostly with measures of syntactic complexity. In the entire sample, separate sCCAs identified a single mode of covariation linking linguistic features with brain morphometry (r = 0.65; p = 0.05) and resting-state network connectivity (r = 0.63; p = 0.01). In both models, semantic and syntactic features covaried with brain structural and functional connectivity measures of the language network. However, the contribution of diagnosis to both models was negligible. CONCLUSIONS Syntactic complexity appeared sensitive to prodromal symptoms in CHR individuals while the patterns of brain-language covariation seemed preserved. Further studies in larger samples are required to establish the reproducibility of these findings.
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Affiliation(s)
- S S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - G E Doucet
- Boys Town National Research Hospital, Omaha, Nebraska, USA
| | - S Garg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - S N Herrera
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - C Sarac
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Z R Bilgrami
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - R B Shaik
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - C M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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25
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Corcoran CM, Cecchi GA. Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:770-779. [PMID: 32771179 DOI: 10.1016/j.bpsc.2020.06.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/09/2020] [Accepted: 06/09/2020] [Indexed: 01/12/2023]
Abstract
Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents "big data" at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.
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Affiliation(s)
- Cheryl Mary Corcoran
- Icahn School of Medicine at Mount Sinai, New York; James J. Peters Veterans Administration Medical Center, Bronx.
| | - Guillermo A Cecchi
- Thomas J. Watson Research Center, IBM Corporation, Yorktown Heights, New York
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26
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Abstract
PURPOSE OF REVIEW After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established. This article reviews current advances in evaluating the use of language as a diagnostic or prognostic tool in schizophrenia. RECENT FINDINGS The development of computational linguistic tools to quantify language disturbances is rapidly gaining ground in the field of schizophrenia research. Current applications are the use of semantic space models and acoustic analyses focused on phonetic markers. These features are used in machine learning models to distinguish patients with schizophrenia from healthy controls or to predict conversion to psychosis in high-risk groups, reaching accuracy scores (generally ranging from 80 to 90%) that exceed clinical raters. Other potential applications for a language biomarker in schizophrenia are monitoring of side effects, differential diagnostics and relapse prevention. SUMMARY Language disturbances are a key feature of schizophrenia. Although in its early stages, the emerging field of research focused on computational linguistics suggests an important role for language analyses in the diagnosis and prognosis of schizophrenia. Spoken language as a biomarker for schizophrenia has important advantages because it can be objectively and reproducibly quantified. Furthermore, language analyses are low-cost, time efficient and noninvasive in nature.
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27
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Language in schizophrenia: relation with diagnosis, symptomatology and white matter tracts. NPJ SCHIZOPHRENIA 2020; 6:10. [PMID: 32313047 PMCID: PMC7171150 DOI: 10.1038/s41537-020-0099-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 02/28/2020] [Indexed: 01/04/2023]
Abstract
Language deviations are a core symptom of schizophrenia. With the advances in computational linguistics, language can be easily assessed in exact and reproducible measures. This study investigated how language characteristics relate to schizophrenia diagnosis, symptom, severity and integrity of the white matter language tracts in patients with schizophrenia and healthy controls. Spontaneous speech was recorded and diffusion tensor imaging was performed in 26 schizophrenia patients and 22 controls. We were able to classify both groups with a sensitivity of 89% and a specificity of 82%, based on mean length of utterance and clauses per utterance. Language disturbances were associated with negative symptom severity. Computational language measures predicted language tract integrity in patients (adjusted R2 = 0.467) and controls (adjusted R2 = 0.483). Quantitative language analyses have both clinical and biological validity, offer a simple, helpful marker of both severity and underlying pathology, and provide a promising tool for schizophrenia research and clinical practice.
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28
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Bias, noise, and interpretability in machine learning. Mach Learn 2020. [DOI: 10.1016/b978-0-12-815739-8.00017-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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29
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Language impairment in adults with end-stage liver disease: application of natural language processing towards patient-generated health records. NPJ Digit Med 2019; 2:106. [PMID: 31701020 PMCID: PMC6828779 DOI: 10.1038/s41746-019-0179-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 09/23/2019] [Indexed: 12/17/2022] Open
Abstract
End-stage liver disease (ESLD) is associated with cognitive impairment ranging from subtle alterations in attention to overt hepatic encephalopathy that resolves after transplant. Natural language processing (NLP) may provide a useful method to assess cognitive status in this population. We identified 81 liver transplant recipients with ESLD (4/2013–2/2018) who sent at least one patient-to-provider electronic message pre-transplant and post-transplant, and matched them 1:1 to “healthy” controls—who had similar disease, but had not been evaluated for liver transplant—by age, gender, race/ethnicity, and liver disease. Messages written by patients pre-transplant and post-transplant and controls was compared across 19 NLP measures using paired Wilcoxon signed-rank tests. While there was no difference overall in word length, patients with Model for End-Stage Liver Disease Score (MELD) ≥ 30 (n = 31) had decreased word length in pre-transplant messages (3.95 [interquartile range (IQR) 3.79, 4.14]) compared to post-transplant (4.13 [3.96, 4.28], p = 0.01) and controls (4.2 [4.0, 4.4], p = 0.01); there was no difference between post-transplant and controls (p = 0.4). Patients with MELD ≥ 30 had fewer 6+ letter words in pre-transplant messages (19.5% [16.4, 25.9] compared to post-transplant (23.4% [20.0, 26.7] p = 0.02) and controls (25.0% [19.2, 29.4]; p = 0.01). Overall, patients had increased sentence length pre-transplant (12.0 [9.8, 13.7]) compared to post-transplant (11.0 [9.2, 13.3]; p = 0.046); the same was seen for MELD ≥ 30 (12.3 [9.8, 13.7] pre-transplant vs. 10.8 [9.6, 13.0] post-transplant; p = 0.050). Application of NLP to patient-generated messages identified language differences—longer sentences with shorter words—that resolved after transplant. NLP may provide opportunities to detect cognitive impairment in ESLD.
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Birnbaum ML, Ernala SK, Rizvi AF, Arenare E, R Van Meter A, De Choudhury M, Kane JM. Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook. NPJ SCHIZOPHRENIA 2019; 5:17. [PMID: 31591400 PMCID: PMC6779748 DOI: 10.1038/s41537-019-0085-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 09/10/2019] [Indexed: 12/26/2022]
Abstract
Although most patients who experience a first-episode of psychosis achieve remission of positive psychotic symptoms, relapse is common. Existing relapse evaluation strategies are limited by their reliance on direct and timely contact with professionals, and accurate reporting of symptoms. A method by which to objectively identify early relapse warning signs could facilitate swift intervention. We collected 52,815 Facebook posts across 51 participants with recent onset psychosis (mean age = 23.96 years; 70.58% male) and applied anomaly detection to explore linguistic and behavioral changes associated with psychotic relapse. We built a one-class classification model that makes patient-specific personalized predictions on risk to relapse. Significant differences were identified in the words posted to Facebook in the month preceding a relapse hospitalization compared to periods of relative health, including increased usage of words belonging to the swear (p < 0.0001, Wilcoxon signed rank test), anger (p < 0.001), and death (p < 0.0001) categories, decreased usage of words belonging to work (p = 0.00579), friends (p < 0.0001), and health (p < 0.0001) categories, as well as a significantly increased use of first (p < 0.0001) and second-person (p < 0.001) pronouns. We additionally observed a significant increase in co-tagging (p < 0.001) and friending (p < 0.0001) behaviors in the month before a relapse hospitalization. Our classifier achieved a specificity of 0.71 in predicting relapse. Results indicate that social media activity captures objective linguistic and behavioral markers of psychotic relapse in young individuals with recent onset psychosis. Machine-learning models were capable of making personalized predictions of imminent relapse hospitalizations at the patient-specific level.
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Affiliation(s)
- M L Birnbaum
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA. .,Feinstein Institute of Medical Research, Manhasset, NY, USA. .,Hofstra Northwell School of Medicine, Hempstead, NY, USA.
| | - S K Ernala
- Georgia Institute of Technology, Atlanta, GA, USA
| | - A F Rizvi
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA.,Feinstein Institute of Medical Research, Manhasset, NY, USA.,Hofstra Northwell School of Medicine, Hempstead, NY, USA
| | - E Arenare
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA.,Feinstein Institute of Medical Research, Manhasset, NY, USA.,Hofstra Northwell School of Medicine, Hempstead, NY, USA
| | - A R Van Meter
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA.,Feinstein Institute of Medical Research, Manhasset, NY, USA.,Hofstra Northwell School of Medicine, Hempstead, NY, USA
| | | | - J M Kane
- The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA.,Feinstein Institute of Medical Research, Manhasset, NY, USA.,Hofstra Northwell School of Medicine, Hempstead, NY, USA
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Bruffaerts R, De Deyne S, Meersmans K, Liuzzi AG, Storms G, Vandenberghe R. Redefining the resolution of semantic knowledge in the brain: Advances made by the introduction of models of semantics in neuroimaging. Neurosci Biobehav Rev 2019; 103:3-13. [PMID: 31132379 DOI: 10.1016/j.neubiorev.2019.05.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/15/2019] [Accepted: 05/17/2019] [Indexed: 12/12/2022]
Abstract
The boundaries of our understanding of conceptual representation in the brain have been redrawn since the introduction of explicit models of semantics. These models are grounded in vast behavioural datasets acquired in healthy volunteers. Here, we review the most important techniques which have been applied to detect semantic information in neuroimaging data and argue why semantic models are possibly the most valuable addition to the research of semantics in recent years. Using multivariate analysis, predictions based on patient lesion data have been confirmed during semantic processing in healthy controls. Secondly, this new method has given rise to new research avenues, e.g. the detection of semantic processing outside of the temporal cortex. As a future line of work, the same research strategy could be useful to study neurological conditions such as the semantic variant of primary progressive aphasia, which is characterized by pathological semantic processing.
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Affiliation(s)
- Rose Bruffaerts
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium; Neurology Department, University Hospitals Leuven, 3000 Leuven, Belgium.
| | - Simon De Deyne
- Laboratory of Experimental Psychology, Humanities and Social Sciences Group, KU Leuven, Belgium
| | - Karen Meersmans
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium
| | | | - Gert Storms
- Laboratory of Experimental Psychology, Humanities and Social Sciences Group, KU Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium; Neurology Department, University Hospitals Leuven, 3000 Leuven, Belgium
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32
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Corcoran CM, Benavides C, Cecchi G. Natural Language Processing: Opportunities and Challenges for Patients, Providers, and Hospital Systems. Psychiatr Ann 2019. [DOI: 10.3928/00485713-20190411-01] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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