Language as a biomarker for psychosis: A natural language processing approach

Corcoran, Cheryl; Mittal, Vijay A.; Bearden, Carrie E.; Gur, Raquel E.; Hitczenko, Kasia; Bilgrami, Zarina; Savić, Aleksandar; Cecchi, Guillermo A.; Wolff, Phillip

doi:10.1016/j.schres.2020.04.032

Cited by 110 publications

(64 citation statements)

References 79 publications

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“…In our meta-analysis, both phonological processing and comprehension were greatly impaired. These impairments may be associated with ineffective use of contextual information [91] and contribute to poor speech in SZ, especially in close association with thought disorder [92]. Reading rate was low but the deficit in reading accuracy was lower.…”

Section: Effect Of Diagnosis In Nonforensic Samplesmentioning

confidence: 98%

Reading skills deficits in people with mental illness: A systematic review and meta-analysis

et al. 2020

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Background: Good reading skills are important for appropriate functioning in everyday life, scholastic performance, and the chances of acquiring a higher socioeconomic status. We conducted the first systematic review and meta-analysis to quantify possible deficits in specific reading skills in people with a variety of mental illnesses, including personality disorders. Methods: We performed a systematic search of databases (Academic Search Complete, CINAHL Plus, PsycInfo, PsycARTICLES, SocINDEX, MEDLINE, and PubMed) from inception until February 2020 and conducted random-effects meta-analyses. Results: The search yielded 34 studies with standardised assessments of reading skills in people with one or more mental illnesses. Of these, 19 studies provided data for the meta-analysis. Most studies (n=27; meta-analysis, n=17) were in people with schizophrenia and revealed large deficits in phonological processing (Hedge's g=-0.88, p<0.00001), comprehension (Hedge's g=-0.96, p<0.00001) and reading rate (Hedge's g=-1.22, p=0.002), relative to healthy controls; the single-word reading was less affected (Hedge's g=-0.70, p<0.00001). A few studies in affective disorders and non-forensic personality disorders suggested weaker deficits (for all, Hedge's g<-0.60). In forensic populations with personality disorders, there was evidence of marked phonological processing (Hedge's g=-0.85, p<0.0001) and comprehension deficits (Hedge's g=-0.95, p=0.0003). Conclusions: People with schizophrenia, and possibly forensic populations with personality disorders, demonstrate a range of reading skills deficits. Future studies are needed to establish how these deficits directly compare to those seen in developmental or acquired dyslexia and to explore the potential of dyslexia interventions to improve reading skills in these populations.

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Section: Effect Of Diagnosis In Nonforensic Samplesmentioning

confidence: 98%

Reading skills deficits in people with mental illness: A systematic review and meta-analysis

et al. 2020

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“… 7 Linguistic approaches offer specific conceptual and procedural frameworks through Natural Language Processing analytics 8 to investigate the differential mechanisms 9 underlying a wide-ranging set of communication-discourse disturbances. 10 In this work, we focus on the relationship between these disruptions in transcribed speech samples and the clinical impression of formal thought disorder (FTD) in schizophrenia (SZ). We directly address the need to use computational linguistics in a hypothesis-driven approach, to move from exploratory to explanatory and predictive frameworks.…”

Section: Introductionmentioning

confidence: 99%

Small Words That Matter: Linguistic Style and Conceptual Disorganization in Untreated First-Episode Schizophrenia

Silva

Limongi

Mackinley

et al. 2021

Schizophrenia Bulletin Open

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This study aimed to shed light on the linguistic style affecting the communication-discourse in First Episode Schizophrenia (FES) by investigating the analytic thinking index in relation to clinical scores of conceptual and thought disorganisation (Positive and Negative Syndrome Scale, PANSS-P2 and Thought and Language Index, TLI). Using robust Bayesian modeling, we report three major findings: (1) FES subjects showed reduced analytic thinking, exhibiting a less categorical linguistic style than healthy control (HC) subjects (Bayes factor, BF10 > 1000), despite using the same proportion of function and content words as HCs; (2) the lower the analytic thinking score, the higher the symptoms scores of conceptual disorganisation (PANSS-P2, BF = 22.66) and global disorganisation of thinking (TLI, BF10 = 112.73); (3) The linguistic style is a better predictor of conceptual disorganisation than the cognitive measure of processing speed in schizophrenia. These findings provide an objectively detectable linguistic style with a focus on Natural Language Processing Analytics of transcribed speech samples of patients with schizophrenia that requires no clinical judgment. These findings also offer a crucial insight into the primacy of linguistic structural disruption in clinically ascertained disorganized thinking in schizophrenia. Our work contributes to an emerging body of literature on the psychopathology of schizophrenia using a first-order lexeme-level analysis and a hypothesis-driven approach. At a utilitarian level, this has implications for improving educational and social outcomes in patients with schizophrenia.

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“…There has been an increase in recent years in the number of reported studies that have attempted to diagnose psychiatric disorders using natural language processing. Target disorders include schizophrenia [15,16], depression [17], bipolar affective disorder, obsessive-compulsive disorder [18], autism spectrum disorders [19], dementia [20,21] . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.…”

Section: Discussionmentioning

confidence: 99%

Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN): Rationale, Design, and Methodology

Kishimoto

Nakamura²,

Kano

et al. 2021

Preprint

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Introduction: Psychiatric disorders are diagnosed according to diagnostic criteria such as the DSM-5 and ICD-11. Basically, psychiatrists extract symptoms and make a diagnosis by conversing with patients. However, such processes often lack objectivity. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. The purposes of the present study are to quantify the language features of psychiatric disorders and neurocognitive disorders using natural language processing and to identify features that differentiate disorders from one another and from healthy subjects. Methods: This study will have a multi-center prospective design. Major depressive disorder, bipolar disorder, schizophrenia, anxiety disorder including obsessive compulsive disorder and, major and minor neurocognitive disorders, as well as healthy subjects will be recruited. A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant and these interviews will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times. Discussion: The overall goal of this proposed study, the Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN), is to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of each psychiatric disorder using natural language processing. As of August 2021, we have collected a total of >900 datasets from >350 participants. To the best of our knowledge, this data sample is one of the largest in this field.

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Language as a biomarker for psychosis: A natural language processing approach

Cited by 110 publications

References 79 publications

Reading skills deficits in people with mental illness: A systematic review and meta-analysis

Reading skills deficits in people with mental illness: A systematic review and meta-analysis

Small Words That Matter: Linguistic Style and Conceptual Disorganization in Untreated First-Episode Schizophrenia

Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN): Rationale, Design, and Methodology

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