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Arslan B, Kizilay E, Verim B, Demirlek C, Dokuyan Y, Turan YE, Kucukakdag A, Demir M, Cesim E, Bora E. Automated linguistic analysis in speech samples of Turkish-speaking patients with schizophrenia-spectrum disorders. Schizophr Res 2024; 267:65-71. [PMID: 38518480 DOI: 10.1016/j.schres.2024.03.014] [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: 12/10/2023] [Revised: 02/05/2024] [Accepted: 03/14/2024] [Indexed: 03/24/2024]
Abstract
Modern natural language processing (NLP) methods provide ways to objectively quantify language disturbances for potential use in diagnostic classification. We performed computerized language analysis in speech samples of 82 Turkish-speaking subjects, including 44 patients with schizophrenia spectrum disorders (SSD) and 38 healthy controls (HC). Exploratory analysis of speech samples involved 16 sentence-level semantic similarity features using SBERT (Sentence Bidirectional Encoder Representation from Text) as well as 8 generic and 8 part-of-speech (POS) features. The random forest classifier using SBERT-derived semantic similarity features achieved a mean accuracy of 85.6 % for the classification of SSD and HC. When semantic similarity features were combined with generic and POS features, the classifier's mean accuracy reached to 86.8 %. Our analysis reflected increased sentence-level semantic similarity scores in SSD. Generic and POS analyses revealed an increase in the use of verbs, proper nouns and pronouns in SSD while our results showed a decrease in the utilization of conjunctions, determiners, and both average and maximum sentence length in SSD compared to HC. Quantitative language features were correlated with the expressive deficit domain of BNSS (Brief Negative Symptom Scale) as well as with the duration of illness. These findings from Turkish-speaking interviews contribute to the growing evidence-based NLP-derived assessments in non-English-speaking patients.
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Affiliation(s)
- Berat Arslan
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey.
| | - Elif Kizilay
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey
| | - Burcu Verim
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey
| | - Cemal Demirlek
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey; Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Yagmur Dokuyan
- Department of Psychiatry, Izmir City Hospital, Izmir, Turkey
| | - Yaren Ecesu Turan
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey
| | - Aybuke Kucukakdag
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey
| | - Muhammed Demir
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey
| | - Ezgi Cesim
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey
| | - Emre Bora
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey; Department of Psychiatry, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey; Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Carlton South, Victoria 3053, Australia
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López-Silva P, Harrow M, Jobe TH, Tufano M, Harrow H, Rosen C. 'Are these my thoughts?': A 20-year prospective study of thought insertion, thought withdrawal, thought broadcasting, and their relationship to auditory verbal hallucinations. Schizophr Res 2024; 265:46-57. [PMID: 35945121 DOI: 10.1016/j.schres.2022.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/27/2022] [Accepted: 07/08/2022] [Indexed: 11/22/2022]
Abstract
The co-occurrence of delusions and other symptoms at the onset of psychosis is a challenge for theories about the aetiology of psychosis. This paper explores the relatedness of delusions about the experience of thinking (thought insertion, thought withdrawal, and thought broadcasting) and auditory verbal hallucinations by describing their trajectories over a 20-year period in individuals diagnosed with schizophrenia, affective and other psychosis, and unipolar depression nonpsychosis. The sample consisted of 407 participants who were recruited at index hospitalization and evaluated over six follow-ups over 20 years. The symptom structure associated with thought insertion included auditory verbal hallucinations, somatic hallucinations, other hallucinations, delusions of thought-dissemination, delusions of control, delusion of self-depreciation, depersonalization and anxiety. The symptom constellation of thought withdrawal included somatic hallucinations, other hallucinations, delusions of thought dissemination, delusions of control, sexual delusions, depersonalization, negative symptoms, depression, and anxiety. The symptom constellation of thought broadcasting included auditory verbal hallucinations, somatic hallucinations, delusions of thought-dissemination, delusion of self-depreciation, fantastic delusions, sexual delusions, and depersonalization. Auditory verbal hallucinations and delusions of self-depreciation were significantly associated with both thought insertion and thought broadcasting. Thought insertion and thought withdrawal were significantly associated with other hallucinations, delusions of control, and anxiety; thought withdrawal and thought broadcasting were significantly related to sexual delusions. We hypothesize that specific symptom constellations over time might be explained as the product of pseudo-coherent realities created to give meaning to the experience of the world and the self of individuals in psychosis based on both prior top-down and ongoing bottom-up elements.
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Affiliation(s)
- Pablo López-Silva
- Faculty of Social Sciences, School of Psychology, Universidad de Valparaíso, Chile
| | - Martin Harrow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Thomas H Jobe
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Michele Tufano
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Helen Harrow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Cherise Rosen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States.
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He R, Palominos C, Zhang H, Alonso-Sánchez MF, Palaniyappan L, Hinzen W. Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational language models. Psychiatry Res 2024; 333:115752. [PMID: 38280291 DOI: 10.1016/j.psychres.2024.115752] [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: 07/28/2023] [Revised: 01/16/2024] [Accepted: 01/21/2024] [Indexed: 01/29/2024]
Abstract
Speech in psychosis has long been ascribed as involving 'loosening of associations'. We pursued the aim to elucidate its underlying cognitive mechanisms by analysing picture descriptions from 94 subjects (29 healthy controls, 18 participants at clinical high risk, 29 with first-episode psychosis, and 18 with chronic schizophrenia), using five language models with different computational architectures: FastText, which represents meaning non-contextually/statically; BERT, which represents contextual meaning sensitive to grammar and context; Infersent and SBERT, which provide sentential representations; and CLIP, which evaluates speech relative to a visual stimulus. These models were used to quantify semantic distances crossed between successive tokens/sentences, and semantic perplexity indicating unexpectedness in continuations. Results showed that, among patients, semantic similarity increased when measured with FastText, Infersent, and SBERT, while it decreased with CLIP and BERT. Higher perplexity was observed in first-episode psychosis. Static semantic measures were associated with clinically measured impoverishment of thought and referential semantic measures with disorganization. These patterns indicate a shrinking conceptual semantic space as represented by static language models, which co-occurs with a widening in the referential semantic space as represented by contextual models. This duality underlines the need to separate these two forms of meaning for understanding mechanisms involved in semantic change in psychosis.
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Affiliation(s)
- Rui He
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona, 08018, Spain.
| | - Claudio Palominos
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona, 08018, Spain
| | - Han Zhang
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona, 08018, Spain
| | | | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada; Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Wolfram Hinzen
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona, 08018, Spain; Intitut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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Jimeno N. Language and communication rehabilitation in patients with schizophrenia: A narrative review. Heliyon 2024; 10:e24897. [PMID: 38312547 PMCID: PMC10835363 DOI: 10.1016/j.heliyon.2024.e24897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/06/2024] Open
Abstract
Language impairments often appear in patients with schizophrenia and are potential targets for rehabilitation. Clinical practice and research should be intimately connected. The aim was to perform a narrative review of the assessment and intervention tools that have been used for the rehabilitation of schizophrenia patients with language and communication impairments. Two types of tools, general and specific, were developed for both purposes. General tools include the Positive and Negative Syndrome Scale for assessment, and the Integrated Psychological Therapy for intervention. The specific tools used to evaluate language and communication impairments include the Scale for the Assessment of Thought, Language and Communication, the Formal Thought Disorder scales (for caregivers and patients), and the Thought and Language Disorder scale. The most recent language-specific intervention tools include the Cognitive Pragmatic Treatment, Conecta-2, Let's talk! Multimodal Speech-Gesture training, Speech Therapy Intervention Group, and PragmaCom. These tools primarily involve psychopathology/psychiatry, psychology, linguistics, speech and language therapy, and nursing. In conclusion, a wide range of assessment and intervention tools are available for the rehabilitation of language and communication impairments associated with schizophrenia. An integrative and interdisciplinary approach should always be considered for rehabilitation of language and communication in patients with schizophrenia throughout their lifetime.
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Affiliation(s)
- Natalia Jimeno
- School of Medicine, University of Valladolid, Av. Ramón y Cajal 7, E-47005 Valladolid, Spain
- Research Group on Clinical Neuroscience of Castile and Leon, Av. Ramón y Cajal 7, E-47005 Valladolid, Spain
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Manic KS, Rajinikanth V, Al-Bimani AS, Taniar D, Kadry S. Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features. SENSORS (BASEL, SWITZERLAND) 2022; 23:280. [PMID: 36616876 PMCID: PMC9823879 DOI: 10.3390/s23010280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.
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Affiliation(s)
- K. Suresh Manic
- National University of Science and Technology, Muscat P.O. Box 112, Oman
| | - Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Ali Saud Al-Bimani
- National University of Science and Technology, Muscat P.O. Box 112, Oman
| | - David Taniar
- Faculty of Information Technology, Monash University, Wellington Rd, Clayton, VIC 3800, Australia
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 36, Lebanon
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Bambini V, Frau F, Bischetti L, Cuoco F, Bechi M, Buonocore M, Agostoni G, Ferri I, Sapienza J, Martini F, Spangaro M, Bigai G, Cocchi F, Cavallaro R, Bosia M. Deconstructing heterogeneity in schizophrenia through language: a semi-automated linguistic analysis and data-driven clustering approach. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:102. [PMID: 36446789 PMCID: PMC9708845 DOI: 10.1038/s41537-022-00306-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Previous works highlighted the relevance of automated language analysis for predicting diagnosis in schizophrenia, but a deeper language-based data-driven investigation of the clinical heterogeneity through the illness course has been generally neglected. Here we used a semiautomated multidimensional linguistic analysis innovatively combined with a machine-driven clustering technique to characterize the speech of 67 individuals with schizophrenia. Clusters were then compared for psychopathological, cognitive, and functional characteristics. We identified two subgroups with distinctive linguistic profiles: one with higher fluency, lower lexical variety but greater use of psychological lexicon; the other with reduced fluency, greater lexical variety but reduced psychological lexicon. The former cluster was associated with lower symptoms and better quality of life, pointing to the existence of specific language profiles, which also show clinically meaningful differences. These findings highlight the importance of considering language disturbances in schizophrenia as multifaceted and approaching them in automated and data-driven ways.
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Affiliation(s)
- Valentina Bambini
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy.
| | - Federico Frau
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy
| | - Luca Bischetti
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy
| | - Federica Cuoco
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Margherita Bechi
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Mariachiara Buonocore
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giulia Agostoni
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Ilaria Ferri
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jacopo Sapienza
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesca Martini
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Spangaro
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giorgia Bigai
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Federica Cocchi
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberto Cavallaro
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Marta Bosia
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
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Procesamiento de lenguaje natural para texto clínico en español: el caso de las listas de espera en Chile. REVISTA MÉDICA CLÍNICA LAS CONDES 2022. [PMCID: PMC9704358 DOI: 10.1016/j.rmclc.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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