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Angelopoulou G, Kasselimis D, Varkanitsa M, Tsolakopoulos D, Papageorgiou G, Velonakis G, Meier E, Karavassilis E, Pantoleon V, Laskaris N, Kelekis N, Tountopoulou A, Vassilopoulou S, Goutsos D, Kiran S, Weiller C, Rijntjes M, Potagas C. Investigating silent pauses in connected speech: integrating linguistic, neuropsychological, and neuroanatomical perspectives across narrative tasks in post-stroke aphasia. Front Neurol 2024; 15:1347514. [PMID: 38682034 PMCID: PMC11047180 DOI: 10.3389/fneur.2024.1347514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/07/2024] [Indexed: 05/01/2024] Open
Abstract
Introduction Silent pauses are regarded as integral components of the temporal organization of speech. However, it has also been hypothesized that they serve as markers for internal cognitive processes, including word access, monitoring, planning, and memory functions. Although existing evidence across various pathological populations underscores the importance of investigating silent pauses' characteristics, particularly in terms of frequency and duration, there is a scarcity of data within the domain of post-stroke aphasia. Methods The primary objective of the present study is to scrutinize the frequency and duration of silent pauses in two distinct narrative tasks within a cohort of 32 patients with chronic post-stroke aphasia, in comparison with a control group of healthy speakers. Subsequently, we investigate potential correlation patterns between silent pause measures, i.e., frequency and duration, across the two narrative tasks within the patient group, their performance in neuropsychological assessments, and lesion data. Results Our findings showed that patients exhibited a higher frequency of longer-duration pauses in both narrative tasks compared to healthy speakers. Furthermore, within-group comparisons revealed that patients tended to pause more frequently and for longer durations in the picture description task, while healthy participants exhibited the opposite trend. With regard to our second research question, a marginally significant interaction emerged between performance in semantic verbal fluency and the narrative task, in relation to the location of silent pauses-whether between or within clauses-predicting the duration of silent pauses in the patient group. However, no significant results were observed for the frequency of silent pauses. Lastly, our study identified that the duration of silent pauses could be predicted by distinct Regions of Interest (ROIs) in spared tissue within the left hemisphere, as a function of the narrative task. Discussion Overall, this study follows an integrative approach of linguistic, neuropsychological and neuroanatomical data to define silent pauses in connected speech, and illustrates interrelations between cognitive components, temporal aspects of speech, and anatomical indices, while it further highlights the importance of studying connected speech indices using different narrative tasks.
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Affiliation(s)
- G. Angelopoulou
- Neuropsychology&Language Disorders Unit, 1st Department of Neurology, Eginition Hospital, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - D. Kasselimis
- Neuropsychology&Language Disorders Unit, 1st Department of Neurology, Eginition Hospital, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Department of Psychology, Panteion University of Social and Political Sciences, Athens, Greece
| | - M. Varkanitsa
- Center for Brain Recovery, Boston University, Boston, MA, United States
| | - D. Tsolakopoulos
- Neuropsychology&Language Disorders Unit, 1st Department of Neurology, Eginition Hospital, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - G. Papageorgiou
- Neuropsychology&Language Disorders Unit, 1st Department of Neurology, Eginition Hospital, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - G. Velonakis
- 2nd Department of Radiology, General University Hospital “Attikon”, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - E. Meier
- The Aphasia Network Lab, Department of Communication Sciences and Disorders, Northeastern University, Boston, MA, United States
| | - E. Karavassilis
- 2nd Department of Radiology, General University Hospital “Attikon”, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- School of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - V. Pantoleon
- 2nd Department of Radiology, General University Hospital “Attikon”, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - N. Laskaris
- Neuropsychology&Language Disorders Unit, 1st Department of Neurology, Eginition Hospital, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Department of Industrial Design and Production Engineering, School of Engineering, University of West Attica, Athens, Greece
| | - N. Kelekis
- 2nd Department of Radiology, General University Hospital “Attikon”, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - A. Tountopoulou
- Stroke Unit, 1st Department of Neurology, Eginition Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - S. Vassilopoulou
- Stroke Unit, 1st Department of Neurology, Eginition Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - D. Goutsos
- Department of Linguistics, School of Philosophy, National and Kapodistrian University of Athens, Athens, Greece
| | - S. Kiran
- Center for Brain Recovery, Boston University, Boston, MA, United States
| | - C. Weiller
- Department of Neurology and Clinical Neuroscience, University Hospital Freiburg, Freiburg, Germany
| | - M. Rijntjes
- Department of Neurology and Clinical Neuroscience, University Hospital Freiburg, Freiburg, Germany
| | - C. Potagas
- Neuropsychology&Language Disorders Unit, 1st Department of Neurology, Eginition Hospital, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
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Peng Z, Li Q, Liu X, Zhang H, Luosang-Zhuoma, Ran M, Liu M, Tan X, Stein MJ. A new schizophrenia screening instrument based on evaluating the patient's writing. Schizophr Res 2024; 266:127-135. [PMID: 38401411 DOI: 10.1016/j.schres.2024.02.003] [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: 02/22/2023] [Revised: 01/18/2024] [Accepted: 02/10/2024] [Indexed: 02/26/2024]
Abstract
Formal Thought Disorder (FTD) is a defining feature of schizophrenia, which is often assessed through patients' speech. Meanwhile, the written language is less studied. The aim of the present study is to establish and validate a comprehensive clinical screening scale, capturing the full variety of empirical characteristics of writing in patients with schizophrenia. The 16-item Screening Instrument for Schizophrenic Features in Writing (SISFiW) is derived from detailed literature review and a "brainstorming" discussion on 30 samples written by patients with schizophrenia. One hundred and fifty-seven participants (114 patients with an ICD-10 diagnoses of schizophrenia; 43 healthy control subjects) were interviewed and symptoms assessed with the Positive and Negative Syndrome Scale (PANSS) and the Scale for the Assessment of Thought, Language, and Communication (TLC). Article samples written by each participant were rated with the SISFiW. Results demonstrated significant difference of the SISFiW-total between the patient group and healthy controls [(3.61 ± 1.72) vs. (0.49 ± 0.63), t = 16.64, p<0.001]. The inter-rater reliability (weighted kappa = 0.72) and the internal consistency (Cronbach's alpha coefficient = 0.613) were acceptable, but correlations with the criterion (PANSS and TLC) were unremarkable. The ROC analysis indicated a cutoff point at 2 with the maximal sensitivity (93.0 %)/specificity (93.0 %). Discriminant analysis of the SISFiW items yielded 8 classifiers that discriminated between the diagnostic groups at a perfect overall performance (with 90.4 % of original and 88.5 % cross-validated grouped cases classified correctly). This instrument appears to be practicable and reliable, with relatively robust discriminatory power, and may serve as a complementary tool to existing FTD rating scales.
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Affiliation(s)
- Zulai Peng
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Qingjun Li
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Xinglan Liu
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Huangzhiheng Zhang
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Luosang-Zhuoma
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Manli Ran
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Maohang Liu
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Xiaolin Tan
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China.
| | - Mark J Stein
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
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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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Jørgensen LM, Jørgensen HP, Thranegaard C, Wang AG. Prosody and schizophrenia. Objective acoustic measurements of monotonous and flat intonation in young Danish people with a schizophrenia diagnosis. A pilot study. Nord J Psychiatry 2024; 78:30-36. [PMID: 37812153 DOI: 10.1080/08039488.2023.2255177] [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: 01/01/2023] [Accepted: 08/31/2023] [Indexed: 10/10/2023]
Abstract
PURPOSE Patients with schizophrenia have a flat and monotonous intonation. The purpose of the study was to find the variables of flat speech that differed in patients from those in healthy controls in Danish. MATERIALS AND METHODS We compared drug-naïve schizophrenic patients 5 men, 13 women and 18 controls, aged 18-35 years, which had all grown up in Copenhagen speaking modern Danish standard (rigsdansk). We used two different tasks that lay different demands on the speaker to elicit spontaneous speech: a retelling of a film clip and telling a story from pictures in a book. A linguist used the computer program Praat to extract the phonetic linguistic parameters. RESULTS We found different results for the two elicitation tasks (Task 1: a retelling of a film clip, task 2: telling a story from pictures in a book). There was higher intensity variation in task one in controls and higher pitch variation in task two in controls. We found a difference in intensity with higher intensity variation in the stresses in the controls in task one and fewer syllables between each stress in the controls. We also found higher F1 variation in task one and two in the patient group and higher F2 variation in the control group in both tasks. CONCLUSIONS The results varied between patients and controls, but the demands also made a difference. Further research is needed to elucidate the possibilities of acoustic measures in diagnostics or linguistic treatment related to schizophrenia.
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Affiliation(s)
| | | | - Camilla Thranegaard
- Faculty of Health Sciences, University of Faroe Islands, Torshavn, Faroe Islands
| | - August G Wang
- Centre of Psychiatry Amager, Copenhagen, Denmark
- Faculty of Health Sciences, University of Faroe Islands, Torshavn, Faroe Islands
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Liebenthal E, Ennis M, Rahimi-Eichi H, Lin E, Chung Y, Baker JT. Linguistic and non-linguistic markers of disorganization in psychotic illness. Schizophr Res 2023; 259:111-120. [PMID: 36564239 PMCID: PMC10282106 DOI: 10.1016/j.schres.2022.12.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Disorganization, presenting as impairment in thought, language and goal-directed behavior, is a core multidimensional syndrome of psychotic disorders. This study examined whether scalable computational measures of spoken language, and smartphone usage pattern, could serve as digital biomarkers of clinical disorganization symptoms. METHODS We examined in a longitudinal cohort of adults with a psychotic disorder, the associations between clinical measures of disorganization and computational measures of 1) spoken language derived from monthly, semi-structured, recorded clinical interviews; and 2) smartphone usage pattern derived via passive sensing technologies over the month prior to the interview. The language features included speech quantity, rate, fluency, and semantic regularity. The smartphone features included data missingness and phone usage during sleep time. The clinical measures consisted of the Positive and Negative Symptom Scale (PANSS) conceptual disorganization, difficulty in abstract thinking, and poor attention, items. Mixed linear regression analyses were used to estimate both fixed and random effects. RESULTS Greater severity of clinical symptoms of conceptual disorganization was associated with greater verbosity and more disfluent speech. Greater severity of conceptual disorganization was also associated with greater missingness of smartphone data, and greater smartphone usage during sleep time. While the observed associations were significant across the group, there was also significant variation between individuals. CONCLUSIONS The findings suggest that digital measures of speech disfluency may serve as scalable markers of conceptual disorganization. The findings warrant further investigation into the use of recorded interviews and passive sensing technologies to assist in the characterization and tracking of psychotic illness.
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Affiliation(s)
- Einat Liebenthal
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Michaela Ennis
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Habiballah Rahimi-Eichi
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Eric Lin
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Medical Informatics, Veterans Affairs Boston, Boston, MA, USA
| | - Yoonho Chung
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Justin T Baker
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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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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Parola A, Simonsen A, Lin JM, Zhou Y, Wang H, Ubukata S, Koelkebeck K, Bliksted V, Fusaroli R. Voice Patterns as Markers of Schizophrenia: Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation. Schizophr Bull 2023; 49:S125-S141. [PMID: 36946527 PMCID: PMC10031745 DOI: 10.1093/schbul/sbac128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
BACKGROUND AND HYPOTHESIS Voice atypicalities are potential markers of clinical features of schizophrenia (eg, negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages. STUDY DESIGN We provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences. STUDY RESULTS We found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages. CONCLUSIONS The findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.
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Affiliation(s)
- Alberto Parola
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Department of Psychology, University of Turin, Turin, Italy
| | - Arndis Simonsen
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Psychosis Research Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jessica Mary Lin
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
| | - Yuan Zhou
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shiho Ubukata
- Department of Psychiatry, Kyoto University, Kyoto, Japan
| | - Katja Koelkebeck
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Hospital and Institute of the University of Duisburg-Essen, Essen, Germany
- Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Duisburg-Essen, Germany
| | - Vibeke Bliksted
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Psychosis Research Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Riccardo Fusaroli
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- The Interacting Minds Center, Institute of Culture and Society, Aarhus University, Aarhus, Denmark
- Linguistic Data Consortium, University of Pennsylvania, Philadelphia, USA
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Tan EJ, Neill E, Kleiner JL, Rossell SL. Depressive symptoms are specifically related to speech pauses in schizophrenia spectrum disorders. Psychiatry Res 2023; 321:115079. [PMID: 36716551 DOI: 10.1016/j.psychres.2023.115079] [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: 06/17/2021] [Revised: 01/03/2023] [Accepted: 01/25/2023] [Indexed: 01/28/2023]
Abstract
Depression is a common and debilitating mental illness associated with sadness and negativity and is often comorbid with other psychiatric conditions, such as schizophrenia. Depressive symptoms are presently primarily assessed through clinical interviews, however there are other behavioural indicators being investigated as more objective methods of depressive symptom assessment. The present study aimed to evaluate the utility of assessing depression using quantitative speech parameters by comparing speech between 23 schizophrenia/schizoaffective patients with clinically significant depressive symptoms (DP) 19 schizophrenia/schizoaffective patients without depressive symptoms (NDP) and 22 healthy controls with no psychiatric history (HC). Participant audio recordings were transcribed and analyzed to extract five types of speech variables: utterances, words, speaking rate, formulation errors and pauses. The results indicated that DP patients produced significantly more pauses within utterances, and had more utterances with pauses compared to NDP patients and HCs (p = <.05), who performed similarly to each other. Word, speaking rate and formulation errors variables were not significantly different between the patient groups (p > .05). The findings suggest that depressive symptoms may have a specific relationship to speech pauses, and support the potential future use of speech pause assessments as an alternative and objective depression rating and monitoring tool.
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Affiliation(s)
- Eric J Tan
- Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St Vincent's Hospital, Melbourne, Australia.
| | - Erica Neill
- Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St Vincent's Hospital, Melbourne, Australia
| | - Jacqui L Kleiner
- Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Susan L Rossell
- Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St Vincent's Hospital, Melbourne, Australia
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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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10
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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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11
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Potagas C, Nikitopoulou Z, Angelopoulou G, Kasselimis D, Laskaris N, Kourtidou E, Constantinides VC, Bougea A, Paraskevas GP, Papageorgiou G, Tsolakopoulos D, Papageorgiou SG, Kapaki E. Silent Pauses and Speech Indices as Biomarkers for Primary Progressive Aphasia. Medicina (B Aires) 2022; 58:medicina58101352. [PMID: 36295513 PMCID: PMC9611099 DOI: 10.3390/medicina58101352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 12/30/2022] Open
Abstract
Background and Objectives: Recent studies highlight the importance of investigating biomarkers for diagnosing and classifying patients with primary progressive aphasia (PPA). Even though there is ongoing research on pathophysiological indices in this field, the use of behavioral variables, and especially speech-derived factors, has drawn little attention in the relevant literature. The present study aims to investigate the possible utility of speech-derived indices, particularly silent pauses, as biomarkers for primary progressive aphasia (PPA). Materials and Methods: We recruited 22 PPA patients and 17 healthy controls, from whom we obtained speech samples based on two elicitation tasks, i.e., cookie theft picture description (CTP) and the patients’ personal narration of the disease onset and course. Results: Four main indices were derived from these speech samples: speech rate, articulation rate, pause frequency, and pause duration. In order to investigate whether these indices could be used to discriminate between the four groups of participants (healthy individuals and the three patient subgroups corresponding to the three variants of PPA), we conducted three sets of analyses: a series of ANOVAs, two principal component analyses (PCAs), and two hierarchical cluster analyses (HCAs). The ANOVAs revealed significant differences between the four subgroups for all four variables, with the CTP results being more robust. The subsequent PCAs and HCAs were in accordance with the initial statistical comparisons, revealing that the speech-derived indices for CTP provided a clearer classification and were especially useful for distinguishing the non-fluent variant from healthy participants as well as from the two other PPA taxonomic categories. Conclusions: In sum, we argue that speech-derived indices, and especially silent pauses, could be used as complementary biomarkers to efficiently discriminate between PPA and healthy speakers, as well as between the three variants of the disease.
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Affiliation(s)
- Constantin Potagas
- Neuropsychology and Language Disorders Unit, 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
- Correspondence:
| | - Zoi Nikitopoulou
- Neuropsychology and Language Disorders Unit, 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Georgia Angelopoulou
- Neuropsychology and Language Disorders Unit, 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
- Department of Speech and Language Therapy, School of Health Sciences, University of Peloponnese, 241 00 Kalamata, Greece
| | - Dimitrios Kasselimis
- Neuropsychology and Language Disorders Unit, 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
- Department of Psychology, Panteion University of Social and Political Sciences, 176 71 Athens, Greece
| | - Nikolaos Laskaris
- Neuropsychology and Language Disorders Unit, 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
- Department of Industrial Design and Production Engineering, School of Engineering, University of West Attica, 122 43 Athens, Greece
| | - Evie Kourtidou
- Neuropsychology and Language Disorders Unit, 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Vasilios C. Constantinides
- 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Anastasia Bougea
- 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - George P. Paraskevas
- 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
- 2nd Department of Neurology, School of Medicine, National and Kapodistrian University of Athens, Attikon University Hospital, 115 28 Athens, Greece
| | - Georgios Papageorgiou
- Neuropsychology and Language Disorders Unit, 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Dimitrios Tsolakopoulos
- Neuropsychology and Language Disorders Unit, 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Sokratis G. Papageorgiou
- 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
| | - Elisabeth Kapaki
- 1st Department of Neurology, Eginitio Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece
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12
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Diaz-Asper M, Holmlund TB, Chandler C, Diaz-Asper C, Foltz PW, Cohen AS, Elvevåg B. Using automated syllable counting to detect missing information in speech transcripts from clinical settings. Psychiatry Res 2022; 315:114712. [PMID: 35839638 PMCID: PMC9378537 DOI: 10.1016/j.psychres.2022.114712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 11/19/2022]
Abstract
Speech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality recordings omitted significant portions of speech. An automated syllable counter estimated actual speech output and quantified the amount of missing information. The efficacy of this method differed by audio quality: the correlation between missing syllables and word error rate was only significant when quality was low. Automatically counting syllables could be useful to measure and flag transcription omissions in clinical contexts where speaker characteristics and recording quality are problematic.
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Affiliation(s)
| | - Terje B Holmlund
- Department of Clinical Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
| | - Chelsea Chandler
- Department of Computer Science, University of Colorado Boulder, CO, United States
| | | | - Peter W Foltz
- Institute of Cognitive Science, University of Colorado Boulder, CO, United States
| | - Alex S Cohen
- Department of Psychology, Louisiana State University, LA, United States
| | - Brita Elvevåg
- Department of Clinical Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway; Norwegian Center for eHealth Research, University Hospital of North Norway, Tromsø, Norway.
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13
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Lofgren M, Hinzen W. Breaking the flow of thought: Increase of empty pauses in the connected speech of people with mild and moderate Alzheimer's disease. JOURNAL OF COMMUNICATION DISORDERS 2022; 97:106214. [PMID: 35397387 DOI: 10.1016/j.jcomdis.2022.106214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/03/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
INTRODUCTION The profile of spontaneous speech in Alzheimer's disease (AD) includes increased pausing as a window into cognitive decline. We here aimed to further characterize the pausing profile of AD by linking pauses to the syntactic positions in which they appear and disease progression. METHODS Speech was obtained through a picture description task, thus minimizing demands on episodic memory (EM), from a group of mild (N = 21) and moderate AD (N = 19), and healthy elderly controls (N = 40). Pauses were sub-indexed according to whether they occurred within-clauses, clause-initially, or utterance-initially, and whether they preceded nouns, verbs, or adjectives/adverbs, when occurring within-clauses. Additionally, relations to verbal fluency (VF) measures at the single-word level were explored. RESULTS Pause rate but not duration distinguished controls from both AD groups, while fillers did not distinguish any groups. The analysis by syntactic position revealed a highly differentiated picture, with largest effect sizes of significant group differences seen in the utterance-initial pause rate. The two AD groups patterned differently when compared to controls, while none of the measures differentiated the AD groups. Specifically, moderate but not mild AD differed from controls in clause-initial pauses, while mild but not moderate AD differed from controls in within-clause positions. At the within-clause level, the effect dividing controls from mild-AD was specifically driven by pauses ahead of nouns. A significant negative correlation emerged between pausing rate in spontaneous speech and VF measures in the mild-AD group only. CONCLUSIONS Increased empty (non-filled) pauses in AD are not confined to pauses in within-clause positions, which are most directly related to problems in the retrieval of words. Even in early disease stages, where these within-clause pause effects are seen, they are confined to nouns, revealing a grammatically specific problem possibly related to the referencing of objects. At all disease stages, pauses increase in utterance-sized units of structure, indicating progressive problems in the creative configuration of complete thoughts.
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Affiliation(s)
- Mary Lofgren
- Dept. Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona 08018, Spain.
| | - Wolfram Hinzen
- Dept. Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona 08018, Spain; Intitut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain, Passeig de Lluís Companys, 23, 08010 Barcelona, Spain
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14
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Abbas A, Hansen BJ, Koesmahargyo V, Yadav V, Rosenfield PJ, Patil O, Dockendorf MF, Moyer M, Shipley LA, Perez-Rodriguez MM, Galatzer-Levy IR. Facial and Vocal Markers of Schizophrenia Measured Using Remote Smartphone Assessments: Observational Study. JMIR Form Res 2022; 6:e26276. [PMID: 35060906 PMCID: PMC8817208 DOI: 10.2196/26276] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/02/2021] [Accepted: 11/22/2021] [Indexed: 12/24/2022] Open
Abstract
Background Machine learning–based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage. Objective This study aimed to determine the accuracy of machine learning–based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones. Methods Measurements of facial and vocal characteristics including facial expressivity, vocal acoustics, and speech prevalence were assessed in 20 patients with schizophrenia over the course of 2 weeks in response to two classes of prompts previously utilized in experimental laboratory assessments: evoked prompts, where subjects are guided to produce specific facial expressions and speech; and spontaneous prompts, where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Facial and vocal measurements were assessed in relation to schizophrenia symptom severity using the Positive and Negative Syndrome Scale. Results Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity, while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia symptom severity. Conclusions Established facial and vocal measurements, collected remotely in schizophrenia patients via smartphones in response to automated task prompts, demonstrated accuracy as markers of schizophrenia symptom severity. Clinical implications are discussed.
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Affiliation(s)
| | | | | | | | - Paul J Rosenfield
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Omkar Patil
- Merck & Co, Inc, Kenilworth, NJ, United States
| | | | | | | | | | - Isaac R Galatzer-Levy
- AiCure, New York, NY, United States
- Department of Psychiatry, New York University School of Medicine, New York, NY, United States
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15
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Tan EJ, Meyer D, Neill E, Rossell SL. Investigating the diagnostic utility of speech patterns in schizophrenia and their symptom associations. Schizophr Res 2021; 238:91-98. [PMID: 34649084 DOI: 10.1016/j.schres.2021.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 09/19/2021] [Accepted: 10/03/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND Speech disturbances are a recognised aspect of schizophrenia that may have potential utility as a diagnostic indicator. Recent advances in quantitative speech assessment methods have led to more reproducible and precise metrics making this possible. The current study sought firstly to characterise the speech profile of schizophrenia patients using quantitative speech measures, then examine the diagnostic utility of these measures and explore their relationship to symptoms. METHODS Speech recordings from 43 schizophrenia/schizoaffective disorder (SZ) patients and 46 healthy controls (HC) were obtained and transcribed. Cognitive and symptom measures were also administered. RESULTS Compared to HCs, SZ patients had higher incidences of aberrance across five types of quantitative speech variables: utterances, single words, time/speaking rate, turns and formulation errors, but not pauses. Based on two machine learning algorithms, 21 speech variables across the same five speech variable types (again not including pauses) were identified as significant classifiers for a schizophrenia diagnosis with 90-100% specificity and 80-90% sensitivity for both models. Selective relationships were also observed between these speech variables and only positive, disorganisation, excitement and formal thought disorder symptoms. CONCLUSIONS The findings support pervasive speech impairments in schizophrenia patients relative to HCs, and the potential diagnostic utility of these speech disturbances. Continued work is needed to build the evidence base for quantitative speech assessment as a future objective diagnostic tool for schizophrenia. It holds the promise of improved diagnostic accuracy leading to increased treatment efficacy and better patient outcomes.
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Affiliation(s)
- Eric J Tan
- Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St. Vincent's Hospital, Melbourne, Australia.
| | - Denny Meyer
- Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia
| | - Erica Neill
- Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St. Vincent's Hospital, Melbourne, Australia
| | - Susan L Rossell
- Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St. Vincent's Hospital, Melbourne, Australia
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16
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Mutlu E, Abaoğlu H, Barışkın E, Gürel ŞC, Ertuğrul A, Yazıcı MK, Akı E, Yağcıoğlu AEA. The cognitive aspect of formal thought disorder and its relationship with global social functioning and the quality of life in schizophrenia. Soc Psychiatry Psychiatr Epidemiol 2021; 56:1399-1410. [PMID: 33458782 DOI: 10.1007/s00127-021-02024-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/06/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE It was expected that using a comprehensive scale like the Thought and Language Disorder Scale (TALD) for measurement of FTD would enable assessing its heterogeneity and its associations with cognitive impairment and functionality. This study has aimed to analyze the relationship between formal thought disorder (FTD) and cognitive functions, functionality, and quality of life in schizophrenia. METHODS This cross-sectional exploratory study included 46 clinical participants meeting the DSM-5 diagnostic criteria for schizophrenia and 35 healthy individuals as the control groups. Data were acquired by means of the Turkish language version of the TALD, the Positive and Negative Syndrome Scale, the Clinical Global Impression Scale, the Functioning Assessment Short Test, the Social Functioning Scale, the World Health Organization Quality of Life Instrument-Short Form, and a neuropsychological test battery on executive functions, working memory, verbal fluency, abstract thinking, and response inhibition. Correlation analyses were conducted to detect significant relationships. RESULTS The clinical group scored failures in all cognitive tests. The objective positive FTD was associated with deficits in executive functions and social functioning. The objective negative FTD was associated with poor performance in all cognitive domains, physical quality of life, and social and global functioning. The subjective negative FTD was negatively correlated with psychological quality of life. CONCLUSION This study demonstrated that objective FTD factors reflect different underlying cognitive deficits and correlate with different functioning domains. Significant correlation was determined between subjective negative FTD and psychological quality of life. Given the close relationship of FTD with functioning and quality of life, the FTD-related cognitive deficits should be the key treatment goal in schizophrenia.
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Affiliation(s)
- Emre Mutlu
- Department of Psychiatry, Faculty of Medicine, Hacettepe University, Ankara, Turkey. .,Psychiatry Clinic, Etimesgut Şehit Sait Ertürk State Hospital, Ankara, Turkey.
| | - Hatice Abaoğlu
- Department of Occupational Therapy, Faculty of Health Sciences, Hacettepe University, Ankara, Turkey
| | - Elif Barışkın
- Department of Psychiatry, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Ş Can Gürel
- Department of Psychiatry, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Aygün Ertuğrul
- Department of Psychiatry, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - M Kazım Yazıcı
- Department of Psychiatry, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Esra Akı
- Department of Occupational Therapy, Faculty of Health Sciences, Hacettepe University, Ankara, Turkey
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17
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Tang SX, Kriz R, Cho S, Park SJ, Harowitz J, Gur RE, Bhati MT, Wolf DH, Sedoc J, Liberman MY. Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders. NPJ SCHIZOPHRENIA 2021; 7:25. [PMID: 33990615 PMCID: PMC8121795 DOI: 10.1038/s41537-021-00154-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 03/26/2021] [Indexed: 01/11/2023]
Abstract
Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., "the," "a,"). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.
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Affiliation(s)
- Sunny X Tang
- Zucker Hillside Hospital, Department of Psychiatry, 75-59 263rd St., Glen Oaks, NY, USA.
- University of Pennsylvania, Department of Psychiatry, 3400 Spruce St, Gates Building, Philadelphia, PA, USA.
- Linguistics Data Consortium, 3600 Market St, Suite 810, Philadelphia, PA, USA.
| | - Reno Kriz
- University of Pennsylvania, Department of Computer Science, 3330 Walnut St, Levine Hall, Philadelphia, PA, USA
| | - Sunghye Cho
- Linguistics Data Consortium, 3600 Market St, Suite 810, Philadelphia, PA, USA
| | - Suh Jung Park
- University of Pennsylvania, Department of Psychiatry, 3400 Spruce St, Gates Building, Philadelphia, PA, USA
| | - Jenna Harowitz
- University of Pennsylvania, Department of Psychiatry, 3400 Spruce St, Gates Building, Philadelphia, PA, USA
| | - Raquel E Gur
- University of Pennsylvania, Department of Psychiatry, 3400 Spruce St, Gates Building, Philadelphia, PA, USA
| | - Mahendra T Bhati
- University of Pennsylvania, Department of Psychiatry, 3400 Spruce St, Gates Building, Philadelphia, PA, USA
- Stanford University, Department of Psychiatry and Neurosurgery, 401 Quarry Road, Stanford, CA, USA
| | - Daniel H Wolf
- University of Pennsylvania, Department of Psychiatry, 3400 Spruce St, Gates Building, Philadelphia, PA, USA
| | - João Sedoc
- New York University, Department of Technology, Operations, and Statistics, 44 West Fourth Street, Kaufman Management Center, New York, NY, USA
| | - Mark Y Liberman
- Linguistics Data Consortium, 3600 Market St, Suite 810, Philadelphia, PA, USA
- University of Pennsylvania, Department of Linguistics, 3401-C Walnut St, Suite 300, C Wing, Philadelphia, PA, USA
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18
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Shimizu J, Kuwata H, Kuwata K. Differences in fractal patterns and characteristic periodicities between word salads and normal sentences: Interference of meaning and sound. PLoS One 2021; 16:e0247133. [PMID: 33600483 PMCID: PMC7891721 DOI: 10.1371/journal.pone.0247133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/01/2021] [Indexed: 11/19/2022] Open
Abstract
Fractal dimensions and characteristic periodicities were evaluated in normal sentences, computer-generated word salads, and word salads from schizophrenia patients, in both Japanese and English, using the random walk patterns of vowels. In normal sentences, the walking curves were smooth with gentle undulations, whereas computer-generated word salads were rugged with mechanical repetitions, and word salads from patients with schizophrenia were unreasonably winding with meaningless repetitive patterns or even artistic cohesion. These tendencies were similar in both languages. Fractal dimensions between normal sentences and word salads of schizophrenia were significantly different in Japanese [1.19 ± 0.09 (n = 90) and 1.15 ± 0.08 (n = 45), respectively] and English [1.20 ± 0.08 (n = 91), and 1.16 ± 0.08 (n = 42)] (p < 0.05 for both). Differences in long-range (>10) periodicities between normal sentences and word salads from schizophrenia patients were predominantly observed at 25.6 (p < 0.01) in Japanese and 10.7 (p < 0.01) in English. The differences in fractal dimension and characteristic periodicities of relatively long-range (>10) presented here are sensitive to discriminate between schizophrenia and healthy mental state, and could be implemented in social robots to assess the mental state of people in care.
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Affiliation(s)
- Jun Shimizu
- United Graduate School of Drug Discovery and Medical Information Sciences, Tokai National Higher Education and Research System, Gifu University, Gifu, Japan
| | - Hiromi Kuwata
- Dept. of Pediatric Nursing, Shiga University of Medical Science, Otsu, Japan
| | - Kazuo Kuwata
- United Graduate School of Drug Discovery and Medical Information Sciences, Tokai National Higher Education and Research System, Gifu University, Gifu, Japan
- * E-mail:
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19
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Stanislawski ER, Bilgrami ZR, Sarac C, Garg S, Heisig S, Cecchi GA, Agurto C, Corcoran CM. Negative symptoms and speech pauses in youths at clinical high risk for psychosis. NPJ SCHIZOPHRENIA 2021; 7:3. [PMID: 33483485 PMCID: PMC7822906 DOI: 10.1038/s41537-020-00132-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 11/24/2020] [Indexed: 12/23/2022]
Abstract
Aberrant pauses are characteristic of schizophrenia and are robustly associated with its negative symptoms. Here, we found that pause behavior was associated with negative symptoms in individuals at clinical high risk (CHR) for psychosis, and with measures of syntactic complexity—phrase length and usage of determiners that introduce clauses—that we previously showed in this same CHR cohort to help comprise a classifier that predicted psychosis. These findings suggest a common impairment in discourse planning and verbal self-monitoring that affects both speech and language, and which is detected in clinical ratings of negative symptoms.
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Affiliation(s)
| | | | - Cansu Sarac
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sahil Garg
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen Heisig
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Carla Agurto
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Cheryl M Corcoran
- Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,James J. Peters VA Medical Center, Bronx, NY, USA.
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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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21
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Parola A, Simonsen A, Bliksted V, Fusaroli R. Voice patterns in schizophrenia: A systematic review and Bayesian meta-analysis. Schizophr Res 2020; 216:24-40. [PMID: 31839552 DOI: 10.1016/j.schres.2019.11.031] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 09/13/2019] [Accepted: 11/19/2019] [Indexed: 12/28/2022]
Abstract
Voice atypicalities have been a characteristic feature of schizophrenia since its first definitions. They are often associated with core negative symptoms such as flat affect and alogia, and with the social impairments seen in the disorder. This suggests that voice atypicalities may represent a marker of clinical features and social functioning in schizophrenia. We systematically reviewed and meta-analyzed the evidence for distinctive acoustic patterns in schizophrenia, as well as their relation to clinical features. We identified 46 articles, including 55 studies with a total of 1254 patients with schizophrenia and 699 healthy controls. Summary effect sizes (Hedges'g and Pearson's r) estimates were calculated using multilevel Bayesian modeling. We identified weak atypicalities in pitch variability (g = -0.55) related to flat affect, and stronger atypicalities in proportion of spoken time, speech rate, and pauses (g's between -0.75 and -1.89) related to alogia and flat affect. However, the effects were mostly modest (with the important exception of pause duration) compared to perceptual and clinical judgments, and characterized by large heterogeneity between studies. Moderator analyses revealed that tasks with a more demanding cognitive and social component showed larger effects both in contrasting patients and controls and in assessing symptomatology. In conclusion, studies of acoustic patterns are a promising but, yet unsystematic avenue for establishing markers of schizophrenia. We outline recommendations towards more cumulative, open, and theory-driven research.
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Affiliation(s)
| | - Arndis Simonsen
- Psychosis Research Unit - Department of Clinical Medicine, Aarhus University, Denmark; The Interacting Minds Center - School of Culture and Society, Aarhus University, Denmark
| | - Vibeke Bliksted
- Psychosis Research Unit - Department of Clinical Medicine, Aarhus University, Denmark; The Interacting Minds Center - School of Culture and Society, Aarhus University, Denmark
| | - Riccardo Fusaroli
- The Interacting Minds Center - School of Culture and Society, Aarhus University, Denmark; Department of Linguistics, Semiotics and Cognitive Science - School of Communication and Culture, Aarhus University, Denmark
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