Lexical analysis in schizophrenia: how emotion and social word use informs our understanding of clinical presentation

Narrative identity in addictive disorders: a conceptual review

Narrative identity allows individuals to integrate their personal experiences into a coherent and meaningful life story. Addictive disorders appear to be associated with a disturbed sense of self, reflected in problematic and disorganized self-narratives. In recent literature, a growing body of research has highlighted how narrative approaches can make a dual contribution to the understanding of addiction: on the one hand, by revealing crucial aspects of self structure, and, on the other, by supporting the idea that addiction is a disorder related to unintegrated self-states in which dissociative phenomena and the resulting sense of 'loss of self' are maladaptive strategies for coping with distress. This conceptual review identified the main measures of narrative identity, i.e., narrative coherence and complexity, agency, and emotions, and critically examines 9 quantitative and qualitative studies (out of 18 identified in literature), that have investigated the narrative dimension in people with an addictive disorder in order to provide a synthesis of the relationship between self, narrative and addiction. These studies revealed a difficulty in the organization of narrative identity of people with an addictive disorder, which is reflected in less coherent and less complex autobiographical narratives, in a prevalence of passivity and negative emotions, and in a widespread presence of themes related to a lack of self-efficacy. This review points out important conceptual, methodological and clinical implications encouraging further investigation of narrative dimension in addiction.

Quantifying abnormal emotion processing: A novel computational assessment method and application in schizophrenia

Abnormal emotion processing is a core feature of schizophrenia spectrum disorders (SSDs) that encompasses multiple operations. While deficits in some areas have been well-characterized, we understand less about abnormalities in the emotion processing that happens through language, which is highly relevant for social life. Here, we introduce a novel method using deep learning to estimate emotion processing rapidly from spoken language, testing this approach in male-identified patients with SSDs (n = 37) and healthy controls (n = 51). Using free responses to evocative stimuli, we derived a measure of appropriateness, or "emotional alignment" (EA). We examined psychometric characteristics of EA and its sensitivity to a single-dose challenge of oxytocin, a neuropeptide shown to enhance the salience of socioemotional information in SSDs. Patients showed impaired EA relative to controls, and impairment correlated with poorer social cognitive skill and more severe motivation and pleasure deficits. Adding EA to a logistic regression model with language-based measures of formal thought disorder (FTD) improved classification of patients versus controls. Lastly, oxytocin administration improved EA but not FTD among patients. While additional validation work is needed, these initial results suggest that an automated assay using spoken language may be a promising approach to assess emotion processing in SSDs.

Emotional tone in clinical high risk for psychosis: novel insights from a natural language analysis approach

Background

Individuals at clinical high risk (CHR) for psychosis experience subtle emotional disturbances that are traditionally difficult to assess, but natural language processing (NLP) methods may provide novel insight into these symptoms. We predicted that CHR individuals would express more negative emotionality and less emotional language when compared to controls. We also examined associations with symptomatology.

Methods

Participants included 49 CHR individuals and 42 healthy controls who completed a semi-structured narrative interview. Interview transcripts were analyzed using Linguistic Inquiry and Word Count (LIWC) to assess the emotional tone of the language (tone -the ratio of negative to positive language) and count positive/negative words used. Participants also completed clinical symptom assessments to determine CHR status and characterize symptoms (i.e., positive and negative symptom domains).

Results

The CHR group had more negative emotional tone compared to healthy controls (t=2.676, p=.009), which related to more severe positive symptoms (r2=.323, p=.013). The percentages of positive and negative words did not differ between groups (p's>.05).

Conclusions

Language analyses provided accessible, ecologically valid insight into affective dysfunction and psychosis risk symptoms. Natural language processing analyses unmasked differences in language for CHR that captured language tendencies that were more nuanced than the words that are chosen.

Automated linguistic analysis in speech samples of Turkish-speaking patients with schizophrenia-spectrum disorders

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.

Computerized text and voice analysis of patients with chronic schizophrenia in art therapy

This explorative study of patients with chronic schizophrenia aimed to clarify whether group art therapy followed by a therapist-guided picture review could influence patients' communication behaviour. Data on voice and speech characteristics were obtained via objective technological instruments, and these characteristics were selected as indicators of communication behaviour. Seven patients were recruited to participate in weekly group art therapy over a period of 6 months. Three days after each group meeting, they talked about their last picture during a standardized interview that was digitally recorded. The audio recordings were evaluated using validated computer-assisted procedures, the transcribed texts were evaluated using the German version of the LIWC2015 program, and the voice recordings were evaluated using the audio analysis software VocEmoApI. The dual methodological approach was intended to form an internal control of the study results. An exploratory factor analysis of the complete sets of output parameters was carried out with the expectation of obtaining typical speech and voice characteristics that map barriers to communication in patients with schizophrenia. The parameters of both methods were thus processed into five factors each, i.e., into a quantitative digitized classification of the texts and voices. The factor scores were subjected to a linear regression analysis to capture possible process-related changes. Most patients continued to participate in the study. This resulted in high-quality datasets for statistical analysis. To answer the study question, two results were summarized: First, text analysis factor called Presence proved to be a potential surrogate parameter for positive language development. Second, quantitative changes in vocal emotional factors were detected, demonstrating differentiated activation patterns of emotions. These results can be interpreted as an expression of a cathartic healing process. The methods presented in this study make a potentially significant contribution to quantitative research into the effectiveness and mode of action of art therapy.

Clues from caregiver emotional language usage highlight the link between putative social environment and the psychosis-risk syndrome

Familial emotional word usage has long been implicated in symptom progression in schizophrenia. However, few studies have examined caregiver emotional word usage prior to the onset of psychosis, among those with a clinical high-risk (CHR) syndrome. The current study examined emotional word usage in a sample of caregivers of CHR individuals (N = 37) and caregivers of healthy controls (N = 40) and links with clinical symptoms in CHR individuals. Caregivers completed a speech sample task in which they were asked to speak about the participant; speech samples were then transcribed and analyzed for general positive (e.g. good) and negative (e.g., worthless) emotional words as well as words expressing three specific negative emotions (i.e., anxiety, anger, and sadness) using Linguistic Inquiry and Word Count (LIWC). Findings indicated that (1) CHR caregivers used more negative and anxiety words compared to control caregivers; and (2) less positive word usage among CHR caregivers were related to more positive symptomatology among CHR individuals. These findings point toward the utility of automated language analysis in assessing the intersections between caregiver emotional language use and psychopathology.

Automated measures of speech content and speech organization in schizophrenia: Test-retest reliability and generalizability across demographic variables

Technological advances in artificial intelligence and natural language processing have increased efficiency of assessing speech content and speech organization in schizophrenia. Despite these developments, there has been little focus on the psychometrics of these approaches. Using two common assessments, the current study addressed this gap by: 1) measuring test-retest reliability; and 2) assessing whether speech content and/or speech organization generalize across demographics. To test these aims, we examined psychometric properties of the Linguistic Inquiry Word Count (LIWC), a speech content measure, and the Coh-Metrix, a speech organization measure. Across baseline to six month (n = 101) and baseline to one year (n = 47) narrative speech samples, we generally observed fair reliability for speech content measures and fair to good reliability for speech organization measures. Regarding demographics, multiple speech indices varied by race, income, and education. The lack of excellent reliability scores for speech indices holds important implications for examining speech variables in clinical trials and highlights the dynamic nature of speech. This work illustrates the importance of designing speech content and speech organization measures with external validity across demographic factors. Future studies examining speech in schizophrenia should account for potential biases against demographic groups introduced by linguistic analysis tools.

Applications of Speech Analysis in Psychiatry

ABSTRACT

The need for objective measurement in psychiatry has stimulated interest in alternative indicators of the presence and severity of illness. Speech may offer a source of information that bridges the subjective and objective in the assessment of mental disorders. We systematically reviewed the literature for articles exploring speech analysis for psychiatric applications. The utility of speech analysis depends on how accurately speech features represent clinical symptoms within and across disorders. We identified four domains of the application of speech analysis in the literature: diagnostic classification, assessment of illness severity, prediction of onset of illness, and prognosis and treatment outcomes. We discuss the findings in each of these domains, with a focus on how types of speech features characterize different aspects of psychopathology. Models that bring together multiple speech features can distinguish speakers with psychiatric disorders from healthy controls with high accuracy. Differentiating between types of mental disorders and symptom dimensions are more complex problems that expose the transdiagnostic nature of speech features. Convergent progress in speech research and computer sciences opens avenues for implementing speech analysis to enhance objectivity of assessment in clinical practice. Application of speech analysis will need to address issues of ethics and equity, including the potential to perpetuate discriminatory bias through models that learn from clinical assessment data. Methods that mitigate bias are available and should play a key role in the implementation of speech analysis.

Deconstructing heterogeneity in schizophrenia through language: a semi-automated linguistic analysis and data-driven clustering approach

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.

Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development

BACKGROUND

In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions.

OBJECTIVE

We aimed to investigate whether reliable inferences-psychiatric signs, symptoms, and diagnoses-can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder.

METHODS

We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation.

RESULTS

The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner-pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61).

CONCLUSIONS

This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.

Schizophrenia Detection Using Machine Learning Approach from Social Media Content

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.

Narrative identity in the psychosis spectrum: A systematic review and developmental model

Individuals with schizophrenia-spectrum disorders face profound challenges as they attempt to maintain identity through the course of illness. Narrative identity-the study of internalized, evolving life stories-provides a rich theoretical and empirical perspective on these challenges. Based on evidence from a systematic review of narrative identity in the psychosis spectrum (30 studies, combined N = 3859), we argue that the narrative identities of individuals with schizophrenia-spectrum disorders are distinguished by three features: disjointed structure, a focus on suffering, and detached narration. Psychotic disorders typically begin to emerge during adolescence and emerging adulthood, which are formative developmental stages for narrative identity, so it is particularly informative to understand identity disturbances from a developmental perspective. We propose a developmental model in which a focus on suffering emerges in childhood; disjointed structure emerges in middle and late adolescence; and detached narration emerges before or around the time of a first psychotic episode. Further research with imminent risk and early course psychosis populations would be needed to test these predictions. The disrupted life stories of individuals on the psychosis spectrum provide multiple rich avenues for further research to understand narrative self-disturbances.

What gets in the way of social engagement in schizophrenia?

BACKGROUND

Social engagement-important for health and well-being-can be difficult for people with schizophrenia. Past research indicates that despite expressing interest in social interactions, people with schizophrenia report spending less time with others and feeling lonely. Social motivations and barriers may play an important role for understanding social engagement in schizophrenia.

AIM

To investigate how people with schizophrenia describe factors that impede and promote social engagement.

METHODS

We interviewed a community sample of people with (n = 35) and without (n = 27) schizophrenia or schizoaffective disorder about their social interactions with friends and family over the past week and planned social activities for the coming week. We reviewed the interview transcripts and developed a novel coding system to capture whether interactions occurred, who had initiated the contact, and frequency of reported social barriers (i.e., internal, conflict-based, logistical) and social motivations (i.e., instrumental, affiliative, obligation-based). We also assessed symptoms and functioning.

RESULTS

People with schizophrenia were less likely than people without schizophrenia to have spent time with friends [t (51.04) = 2.09, P = 0.042, d = 0.51)], but not family. People with schizophrenia reported more social barriers than people without schizophrenia [F (1, 60) = 10.55, P = 0.002, ηp2 = 0.15)] but did not differ in reported social motivations. Specifically, people with schizophrenia reported more internal [t (45.75) = 3.40, P = 0.001, d = 0.83)] and conflict-based [t (40.11) = 3.03, P = 0.004, d = 0.73)] barriers than people without schizophrenia. Social barriers and motivations were related to real-world social functioning for people with schizophrenia, such that more barriers were associated with more difficulty in close relationships (r = -0.37, P = 0.027) and more motivations were associated with better community functioning (r = 0.38, P = 0.024).

CONCLUSION

These findings highlight the importance of assessing first person accounts of social barriers and motivations to better understand social engagement in schizophrenia.

Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook

Prior research has identified associations between social media activity and psychiatric diagnoses; however, diagnoses are rarely clinically confirmed. Toward the goal of applying novel approaches to improve outcomes, research using real patient data is necessary. We collected 3,404,959 Facebook messages and 142,390 images across 223 participants (mean age = 23.7; 41.7% male) with schizophrenia spectrum disorders (SSD), mood disorders (MD), and healthy volunteers (HV). We analyzed features uploaded up to 18 months before the first hospitalization using machine learning and built classifiers that distinguished SSD and MD from HV, and SSD from MD. Classification achieved AUC of 0.77 (HV vs. MD), 0.76 (HV vs. SSD), and 0.72 (SSD vs. MD). SSD used more (P < 0.01) perception words (hear, see, feel) than MD or HV. SSD and MD used more (P < 0.01) swear words compared to HV. SSD were more likely to express negative emotions compared to HV (P < 0.01). MD used more words related to biological processes (blood/pain) compared to HV (P < 0.01). The height and width of photos posted by SSD and MD were smaller (P < 0.01) than HV. MD photos contained more blues and less yellows (P < 0.01). Closer to hospitalization, use of punctuation increased (SSD vs HV), use of negative emotion words increased (MD vs. HV), and use of swear words increased (P < 0.01) for SSD and MD compared to HV. Machine-learning algorithms are capable of differentiating SSD and MD using Facebook activity alone over a year in advance of hospitalization. Integrating Facebook data with clinical information could one day serve to inform clinical decision-making.

Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls

This study investigates clinically valid signals about psychiatric symptoms in social media data, by rating severity of psychiatric symptoms in donated, de-identified Facebook posts and comparing to in-person clinical assessments. Participants with schizophrenia (N=8), depression (N=7), or who were healthy controls (N=8) also consented to the collection of their Facebook activity from three months before the in-person assessments to six weeks after this evaluation. Depressive symptoms were assessed in- person using the Montgomery-Åsberg Depression Rating Scale (MADRS), psychotic symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS), and global functioning was assessed using the Community Assessment of Psychotic Experiences (CAPE-42). Independent raters (psychiatrists, non-psychiatrist mental health clinicians, and two staff members) rated depression, psychosis, and global functioning symptoms from the social media activity of deidentified participants. The correlations between in-person clinical ratings and blinded ratings based on social media data were evaluated. Significant correlations (and trends for significance in the mixed model controlling for multiple raters) were found for psychotic symptoms, global symptom ratings and depressive symptoms. Results like these, indicating the presence of clinically valid signal in social media, are an important step toward developing computational tools that could assist clinicians by providing additional data outside the context of clinical encounters.

Identifying emerging mental illness utilizing search engine activity: A feasibility study

Mental illness often emerges during the formative years of adolescence and young adult development and interferes with the establishment of healthy educational, vocational, and social foundations. Despite the severity of symptoms and decline in functioning, the time between illness onset and receiving appropriate care can be lengthy. A method by which to objectively identify early signs of emerging psychiatric symptoms could improve early intervention strategies. We analyzed a total of 405,523 search queries from 105 individuals with schizophrenia spectrum disorders (SSD, N = 36), non-psychotic mood disorders (MD, N = 38) and healthy volunteers (HV, N = 31) utilizing one year's worth of data prior to the first psychiatric hospitalization. Across 52 weeks, we found significant differences in the timing (p<0.05) and frequency (p<0.001) of searches between individuals with SSD and MD compared to HV up to a year in advance of the first psychiatric hospitalization. We additionally identified significant linguistic differences in search content among the three groups including use of words related to sadness and perception, use of first and second person pronouns, and use of punctuation (all p<0.05). In the weeks before hospitalization, both participants with SSD and MD displayed significant shifts in search timing (p<0.05), and participants with SSD displayed significant shifts in search content (p<0.05). Our findings demonstrate promise for utilizing personal patterns of online search activity to inform clinical care.

A Randomized Clinical Trial of Oxytocin or Galantamine in Schizophrenia: Assessing the Impact on Behavioral, Lexical, and Self-Report Indicators of Social Affiliation

Prior studies examining the impact of oxytocin on negative symptoms in schizophrenia have yielded mixed results. The current study explored whether oxytocin can improve more proximal indicators of social affiliation as indicated by changes in behavior, language and subjective indices of social affiliation among individuals with schizophrenia spectrum disorders during a role-play designed to elicit affiliative responses. We tested the hypothesis that daily intranasal oxytocin administered for 6 weeks would improve social affiliation as manifested by increased social skill ratings, use of positive, affiliative, and social words, and subjective responses from a previously published randomized controlled trial. Forty outpatients with schizophrenia or schizoaffective disorder were randomized to the oxytocin, galantamine, or placebo group and completed affiliative role-plays and self-report questionnaires of affect, reactions to the affiliative confederate, and willingness to interact at baseline and post-treatment. Results demonstrated that oxytocin was not effective at improving behavioral or subjective indicators of social affiliation. This study adds to a growing literature that the prosocial effects of oxytocin in schizophrenia are limited or null.

A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.

Automated Verbal and Non-verbal Speech Analysis of Interviews of Individuals with Schizophrenia and Depression

Schizophrenia and depression are the two most common mental disorders associated with negative symptoms that contribute to poor functioning and quality of life for millions of patients globally. This study is part of a larger research project. The overall aim of the project is to develop an automated objective pipeline that aids clinical diagnosis and provides more insights into symptoms of mental illnesses. In our previous work, we have analyzed non-verbal cues and linguistic cues of individuals with schizophrenia. In this study, we extend our work to include participants with depression. Powered by natural language processing techniques, we extract verbal features, both dictionary-based and vector-based, from participants' interviews that were automatically transcribed. We also extracted conversational, phonatory, articulatory and prosodic features from the interviews to understand the conversational and acoustic characteristics of schizophrenia and depression. Combining these features, we applied ensemble learning with leave-one-out cross-validation to classify healthy controls, schizophrenic and depressive patients, achieving an accuracy of 69%-75% in paired classification. From those same features, we also predict the subjective Negative Symptoms Assessment 16 scores of patients with schizophrenia or depression, yielding an accuracy of 90.5% for NSA2 but lower accuracy for other NSA indices. Our analysis also revealed significant linguistic and non-verbal differences that are potentially symptomatic of schizophrenia and depression respectively.

Lexical analysis of emotional responses to "real-world" experiences in individuals with schizophrenia

Abnormalities in emotion perception, expression, and experience are considered a core component of schizophrenia. Previous laboratory studies have demonstrated that while individuals with schizophrenia report levels of positive emotions comparable to healthy individuals in response to positive stimuli, they also report co-occurring negative emotions in response to such stimuli. However, it is unknown whether this response pattern extends to "real world" naturalistic environments. To examine this question, we employed an experience sampling method (ESM) approach using mobile electronic devices to collect information up to 10 times/day over a two-day period from 53 individuals with schizophrenia and 19 non-clinical controls. As part of each experience sample, participants completed brief open-ended responses and answered questions about their emotional responses to three recent events (neutral, positive, and negative). Additionally, participants completed diagnostic and clinical measures. Lexical analyses were used to analyze ESM-based word production and characterize emotion word use. Compared to non-clinical controls, individuals with schizophrenia reported similar levels of positive emotion, but significantly higher negative emotion, which was associated with increased negative symptoms. The schizophrenia group used more anxiety words in response to negative and neutral events, and more anger words in response to positive events. Increased use of anger words was linked with elevations in positive symptoms as well as symptoms of depression, while use of sadness words was linked with anhedonia. Our findings support the co-activation of negative emotion hypothesis documented in laboratory settings and provide evidence of its ecological validity. Implications for functioning and future directions are discussed.

Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook

Although most patients who experience a first-episode of psychosis achieve remission of positive psychotic symptoms, relapse is common. Existing relapse evaluation strategies are limited by their reliance on direct and timely contact with professionals, and accurate reporting of symptoms. A method by which to objectively identify early relapse warning signs could facilitate swift intervention. We collected 52,815 Facebook posts across 51 participants with recent onset psychosis (mean age = 23.96 years; 70.58% male) and applied anomaly detection to explore linguistic and behavioral changes associated with psychotic relapse. We built a one-class classification model that makes patient-specific personalized predictions on risk to relapse. Significant differences were identified in the words posted to Facebook in the month preceding a relapse hospitalization compared to periods of relative health, including increased usage of words belonging to the swear (p < 0.0001, Wilcoxon signed rank test), anger (p < 0.001), and death (p < 0.0001) categories, decreased usage of words belonging to work (p = 0.00579), friends (p < 0.0001), and health (p < 0.0001) categories, as well as a significantly increased use of first (p < 0.0001) and second-person (p  < 0.001) pronouns. We additionally observed a significant increase in co-tagging (p < 0.001) and friending (p < 0.0001) behaviors in the month before a relapse hospitalization. Our classifier achieved a specificity of 0.71 in predicting relapse. Results indicate that social media activity captures objective linguistic and behavioral markers of psychotic relapse in young individuals with recent onset psychosis. Machine-learning models were capable of making personalized predictions of imminent relapse hospitalizations at the patient-specific level.

Deictic Navigation Network: Linguistic Viewpoint Disturbances in Schizophrenia

This paper introduces the Deictic Navigation Network, a cognitive-linguistic framework to analyze and clarify the nature of viewpoint disturbances in language, applied to schizophrenia. We argue that such disturbances have linguistic counterparts in the use of deixis: linguistic elements of which the interpretation relies on the situational context of the discourse and their connection to a subject-bound perspective. The DNN connects such linguistic phenomena to three viewpoint disturbances, which can manifest in different degrees of extremity: (i) the reduced capacity to recognize one's own subjective perspective and the subjective perspectives of others; (ii) the reduced capacity to separate present perspectives from distinct past, future, and hypothetical perspectives; and (iii) the reduced capacity to integrate projected viewpoint structures into the actual here-and-now. We explain how application of the DNN to language in schizophrenia enables the localization of perspectivization disturbances and helps to clarify the nature of disturbances in the ability to build complex viewpoint structures in language as well as cognition.

Measuring disorganized speech in schizophrenia: automated analysis explains variance in cognitive deficits beyond clinician-rated scales

BACKGROUND

Conveying information cohesively is an essential element of communication that is disrupted in schizophrenia. These disruptions are typically expressed through disorganized symptoms, which have been linked to neurocognitive, social cognitive, and metacognitive deficits. Automated analysis can objectively assess disorganization within sentences, between sentences, and across paragraphs by comparing explicit communication to a large text corpus.

METHOD

Little work in schizophrenia has tested: (1) links between disorganized symptoms measured via automated analysis and neurocognition, social cognition, or metacognition; and (2) if automated analysis explains incremental variance in cognitive processes beyond clinician-rated scales. Disorganization was measured in schizophrenia (n = 81) with Coh-Metrix 3.0, an automated program that calculates basic and complex language indices. Trained staff also assessed neurocognition, social cognition, metacognition, and clinician-rated disorganization.

RESULTS

Findings showed that all three cognitive processes were significantly associated with at least one automated index of disorganization. When automated analysis was compared with a clinician-rated scale, it accounted for significant variance in neurocognition and metacognition beyond the clinician-rated measure. When combined, these two methods explained 28-31% of the variance in neurocognition, social cognition, and metacognition.

CONCLUSIONS

This study illustrated how automated analysis can highlight the specific role of disorganization in neurocognition, social cognition, and metacognition. Generally, those with poor cognition also displayed more disorganization in their speech-making it difficult for listeners to process essential information needed to tie the speaker's ideas together. Our findings showcase how implementing a mixed-methods approach in schizophrenia can explain substantial variance in cognitive processes.

Women's Word Use in Pregnancy: Associations With Maternal Characteristics, Prenatal Stress, and Neonatal Birth Outcome

Background: Experiencing high levels of stress during pregnancy can impair maternal well-being and fetal development. Consequently, unbiased assessment of maternal psychological state is crucial. Self-report measures are vulnerable to social desirability effects. Thus, implicit measures, such as word choice analysis, may offer an alternative.

Methods: In this longitudinal online-study, 427 pregnant women described their emotional experiences in writing and additionally responded to self-report questionnaires assessing symptoms of prenatal stress and depression. The written texts were analyzed with a computerized text analysis program. After birth, 253 women provided information on birth outcome.

Results: Word use differed significantly depending on maternal socioeconomic (e.g., marital status) and pregnancy-related characteristics (e.g., parity). Prenatal stress and depressive symptoms were associated with more frequent use of negative emotion words and words of anxiety, as well as with less first-person plural, but not singular pronoun use. Negative emotion and cognitive mechanism words predicted birth outcome, while self-report measures did not.

Conclusion: In addition to self-report measures, word choice may serve as a useful screening tool for symptoms of depression and stress in pregnant women. The findings on pronoun use may reflect women’s changing experience of self-identity during the transition to motherhood.

Evidence of disturbances of deep levels of semantic cohesion within personal narratives in schizophrenia

Since initial conceptualizations, schizophrenia has been thought to involve core disturbances in the ability to form complex, integrated ideas. Although this has been studied in terms of formal thought disorder, the level of involvement of altered latent semantic structure is less clear. To explore this question, we compared the personal narratives of adults with schizophrenia (n=200) to those produced by an HIV+ sample (n=55) using selected indices from Coh-Metrix. Coh-Metrix is a software system designed to compute various language usage statistics from transcribed written and spoken language documents. It differs from many other frequency-based systems in that Coh-Metrix measures a wide range of language processes, ranging from basic descriptors (e.g., total words) to indices assessing more sophisticated processes within sentences, between sentences, and across paragraphs (e.g., deep cohesion). Consistent with predictions, the narratives in schizophrenia exhibited less cohesion even after controlling for age and education. Specifically, the schizophrenia group spoke fewer words, demonstrated less connection between ideas and clauses, provided fewer causal/intentional markers, and displayed lower levels of deep cohesion. A classification model using only Coh-Metrix indices found language markers correctly classified participants in nearly three-fourths of cases. These findings suggest a particular pattern of difficulties cohesively connecting thoughts about oneself and the world results in a perceived lack of coherence in schizophrenia. These results are consistent with Bleuler's model of schizophrenia and offer a novel way to understand and measure alterations in thought and speech over time.

Consumer satisfaction with antipsychotic medication-monitoring appointments: the role of consumer-prescriber communication patterns

OBJECTIVE

The study was designed to explore patterns of prescriber communication behaviors as they relate to consumer satisfaction among a serious mental illness sample.

METHODS

Recordings from 175 antipsychotic medication-monitoring appointments between veterans with psychiatric disorders and their prescribers were coded using the Roter Interaction Analysis System (RIAS) for communication behavioral patterns.

RESULTS

The frequency of prescriber communication behaviors (i.e., facilitation, rapport, procedural, psychosocial, biomedical, and total utterances) did not reliably predict consumer satisfaction. The ratio of prescriber to consumer utterances did predict consumer satisfaction.

CONCLUSIONS

Consistent with client-centered care theory, antipsychotic medication consumers were more satisfied with their encounters when their prescriber did not dominate the conversation.

PRACTICE IMPLICATIONS

Therefore, one potential recommendation from these findings could be for medication prescribers to spend more of their time listening to, rather than speaking with, their SMI consumers.

An Update on Promising Biomarkers in Schizophrenia

Given the heterogeneity of symptoms in patients with schizophrenia and current treatment limitations, biomarkers may play an important role in diagnosis, subtype stratification, and the assessment of treatment response. Though many potential biomarkers have been studied, we have chosen to focus on some of the most promising and potentially clinically relevant biomarkers to review herein. These include markers of inflammation, neuroimaging biomarkers, brain-derived neurotrophic factor, genetic/epigenetic markers, and speech analysis. This will provide a broad overview of putative biomarkers that could become clinically relevant in the future, though none currently appear ready to assist the clinician in identifying cases of schizophrenia, subtypes of the disorder, treatment choice, or response. Nonetheless, some biomarkers, such as C-reactive protein (CRP), may be useful at identifying individuals who may be more highly inflamed, which could drive treatment choice. Though checking CRP is not a standard of practice, this is one example of how biomarkers may drive treatment decisions in the future, supporting precision medicine. Similarly, technological advances may one day allow clinicians to detect changes in speech patterns, which could represent a noninvasive, clinically useful tool in the future. We conclude the review by highlighting two important potential clinical uses for biomarkers in schizophrenia: the identification of individuals who may convert from clinical high risk and the stratification of patients via different biomarkers that may supersede clinical diagnosis. Given the enormous burden of illness of schizophrenia, the search for clinically relevant biomarkers is of great importance to improve the lives of patients with the disorder.

Using lexical analysis to identify emotional distress in psychometric schizotypy

Through the use of lexical analysis software, researchers have demonstrated a greater frequency of negative affect word use in those with schizophrenia and schizotypy compared to the general population. In addition, those with schizotypy endorse greater emotional distress than healthy controls. In this study, our aim was to expand on previous findings in schizotypy to determine whether negative affect word use could be linked to emotional distress. Schizotypy (n=33) and non-schizotypy groups (n=33) completed an open-ended, semi-structured interview and negative affect word use was analyzed using a validated lexical analysis instrument. Emotional distress was assessed using subjective questionnaires of depression and psychological quality of life (QOL). When groups were compared, those with schizotypy used significantly more negative affect words; endorsed greater depression; and reported lower QOL. Within schizotypy, a trend level association between depression and negative affect word use was observed; QOL and negative affect word use showed a significant inverse association. Our findings offer preliminary evidence of the potential effectiveness of lexical analysis as an objective, behavior-based method for identifying emotional distress throughout the schizophrenia-spectrum. Utilizing lexical analysis in schizotypy offers promise for providing researchers with an assessment capable of objectively detecting emotional distress.

A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals

Background

Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts have included expert input to evaluate the authenticity of diagnostic self-disclosures.

Objective

This study aims to move from noisy self-reports of schizophrenia on social media to more accurate identification of diagnoses by exploring a human-machine partnered approach, wherein computational linguistic analysis of shared content is combined with clinical appraisals.

Methods

Twitter timeline data, extracted from 671 users with self-disclosed diagnoses of schizophrenia, was appraised for authenticity by expert clinicians. Data from disclosures deemed true were used to build a classifier aiming to distinguish users with schizophrenia from healthy controls. Results from the classifier were compared to expert appraisals on new, unseen Twitter users.

Results

Significant linguistic differences were identified in the schizophrenia group including greater use of interpersonal pronouns (P<.001), decreased emphasis on friendship (P<.001), and greater emphasis on biological processes (P<.001). The resulting classifier distinguished users with disclosures of schizophrenia deemed genuine from control users with a mean accuracy of 88% using linguistic data alone. Compared to clinicians on new, unseen users, the classifier’s precision, recall, and accuracy measures were 0.27, 0.77, and 0.59, respectively.

Conclusions

These data reinforce the need for ongoing collaborations integrating expertise from multiple fields to strengthen our ability to accurately identify and effectively engage individuals with mental illness online. These collaborations are crucial to overcome some of mental illnesses’ biggest challenges by using digital technology.

Emotional word usage in groups at risk for schizophrenia-spectrum disorders: An objective investigation of attention to emotion

Both extreme levels of social anhedonia (SocAnh) and extreme levels of perceptual aberration/magical ideation (PerMag) indicate increased risk for schizophrenia-spectrum disorders and are associated with emotional deficits. For SocAnh, there is evidence of self-reported decreased trait positive affect and abnormalities in emotional attention. For PerMag, there is evidence of increased trait negative affect and increased attention to negative emotion. Yet, the nature of more objective emotional abnormalities in these groups is unclear. The goal of this study was to assess attention to emotions more objectively in a SocAnh, PerMag, and control group by using a positive (vs. neutral) mood induction procedure followed by a free writing period. Linguistic analyses revealed that the SocAnh group used fewer positive emotion words than the control group, with the PerMag group falling in between the others. In addition, both at-risk groups used more negative emotion words than the control group. Also, for the control group only, those in the positive mood induction used more positive emotion words, suggesting their emotions influenced their linguistic expression. Overall, SocAnh is associated with decreased positive emotional expression and at-risk groups are associated with increased negative emotional expression and a decreased influence of emotions on linguistic expression.

The effect of limited cognitive resources on communication disturbances in serious mental illness

Semantically incoherent speech is a pernicious clinical feature of serious mental illness (SMI). The precise mechanisms underlying this deficit remain unclear. Prior studies have found that arousal of negative emotion exaggerates the severity of these communication disturbances; this has been coined "affective reactivity". Recent research suggests that "cognitive reactivity" may also occur, namely reflecting reduced "on-line" cognitive resources in SMI. We tested the hypothesis that communication disturbances manifest as a function of limited cognitive resources in SMI above and beyond that associated with state affectivity. We also investigated individual differences in symptoms, cognitive ability, and trait affect that may be related to cognitive reactivity. We compared individuals with SMI (n=52) to nonpsychiatric controls (n=27) on a behavioral-based coding of communication disturbances during separate baseline and experimentally-manipulated high cognitive-load dual tasks. Controlling for state affective reactivity, a significant interaction was observed such that communication disturbances decreased in the SMI group under high cognitive-load. Furthermore, a reduction in communication disturbances was related to lower trait and state positive affectivity in the SMI group. Contrary to our expectations, limited cognitive resources temporarily relieved language dysfunction. Implications, particularly with respect to interventions, are discussed.

Language and hope in schizophrenia-spectrum disorders

Hope is integral to recovery for those with schizophrenia. Considering recent advancements in the examination of clients' lexical qualities, we were interested in how clients' words reflect hope. Using computerized lexical analysis, we examined social, emotion, and future words' relations to hope and its pathways and agency components. Forty-five clients provided detailed narratives about their life and mental illness. Transcripts were analyzed using the Linguistic Inquiry and Word Count program (LIWC), which assigns words to categories (e.g., "anxiety") based on a pre-existing dictionary. Correlations and linear multiple regression were used to examine relationships between lexical qualities and hope. Hope and its subcomponents had significant or trending bivariate correlations in expected directions with several emotion-related word categories (anger and sadness) but were not associated with expected categories such as social words, positive emotions, optimism, achievement, and future words. In linear multiple regressions, no LIWC variable significantly predicted hope agency, but anger words significantly predicted both total hope and hope pathways. Our findings indicate lexical analysis tools can be used to investigate recovery-oriented concepts such as hope, and results may inform clinical practice. Future research should aim to replicate our findings in larger samples.

Self-reference in psychosis and depression: a language marker of illness

BACKGROUND

Language use is of increasing interest in the study of mental illness. Analytical approaches range from phenomenological and qualitative to formal computational quantitative methods. Practically, the approach may have utility in predicting clinical outcomes. We harnessed a real-world sample (blog entries) from groups with psychosis, strong beliefs, odd beliefs, illness, mental illness and/or social isolation to validate and extend laboratory findings about lexical differences between psychosis and control subjects.

METHOD

We describe the results of two experiments using Linguistic Inquiry and Word Count software to assess word category frequencies. In experiment 1, we compared word use in psychosis and control subjects in the laboratory (23 per group), and related results to subject symptoms. In experiment 2, we examined lexical patterns in blog entries written by people with psychosis and eight comparison groups. In addition to between-group comparisons, we used factor analysis followed by clustering to discern the contributions of strong belief, odd belief and illness identity to lexical patterns.

RESULTS

Consistent with others' work, we found that first-person pronouns, biological process words and negative emotion words were more frequent in psychosis language. We tested lexical differences between bloggers with psychosis and multiple relevant comparison groups. Clustering analysis revealed that word use frequencies did not group individuals with strong or odd beliefs, but instead grouped individuals with any illness (mental or physical).

CONCLUSIONS

Pairing of laboratory and real-world samples reveals that lexical markers previously identified as specific language changes in depression and psychosis are probably markers of illness in general.

Psychologist in a Pocket: Lexicon Development and Content Validation of a Mobile-Based App for Depression Screening

Background

Language reflects the state of one’s mental health and personal characteristics. It also reveals preoccupations with a particular schema, thus possibly providing insights into psychological conditions. Using text or lexical analysis in exploring depression, negative schemas and self-focusing tendencies may be depicted. As mobile technology has become highly integrated in daily routine, mobile devices have the capacity for ecological momentary assessment (EMA), specifically the experience sampling method (ESM), where behavior is captured in real-time or closer in time to experience in one’s natural environment. Extending mobile technology to psychological health could augment initial clinical assessment, particularly of mood disturbances, such as depression and analyze daily activities, such as language use in communication. Here, we present the process of lexicon generation and development and the initial validation of Psychologist in a Pocket (PiaP), a mobile app designed to screen signs of depression through text analysis.

Objective

The main objectives of the study are (1) to generate and develop a depressive lexicon that can be used for screening text-input in mobile apps to be used in the PiaP; and (2) to conduct content validation as initial validation.

Methods

The first phase of our research focused on lexicon development. Words related to depression and its symptoms based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and in the ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines classification systems were gathered from focus group discussions with Filipino college students, interviews with mental health professionals, and the review of established scales for depression and other related constructs.

Results

The lexicon development phase yielded a database consisting of 13 categories based on the criteria depressive symptoms in the DSM-5 and ICD-10. For the draft of the depression lexicon for PiaP, we were able to gather 1762 main keywords and 9655 derivatives of main keywords. In addition, we compiled 823,869 spelling variations. Keywords included negatively-valenced words like “sad”, “unworthy”, or “tired” which are almost always accompanied by personal pronouns, such as “I”, “I’m” or “my” and in Filipino, “ako” or “ko”. For the content validation, only keywords with CVR equal to or more than 0.75 were included in the depression lexicon test-run version. The mean of all CVRs yielded a high overall CVI of 0.90. A total of 1498 main keywords, 8911 derivatives of main keywords, and 783,140 spelling variations, with a total of 793, 553 keywords now comprise the test-run version.

Conclusions

The generation of the depression lexicon is relatively exhaustive. The breadth of keywords used in text analysis incorporates the characteristic expressions of depression and its related constructs by a particular culture and age group. A content-validated mobile health app, PiaP may help augment a more effective and early detection of depressive symptoms.

Conceptual disorganization weakens links in cognitive pathways: Disentangling neurocognition, social cognition, and metacognition in schizophrenia

Disentangling links between neurocognition, social cognition, and metacognition offers the potential to improve interventions for these cognitive processes. Disorganized symptoms have shown promise for explaining the limiting relationship that neurocognition holds with both social cognition and metacognition. In this study, primary aims included: 1) testing whether conceptual disorganization, a specific disorganized symptom, moderated relationships between cognitive processes, and 2) examining the level of conceptual disorganization necessary for links between cognitive processes to break down. To accomplish these aims, comprehensive assessments of conceptual disorganization, neurocognition, social cognition, and metacognition were administered to 67 people with schizophrenia-spectrum disorders. We found that conceptual disorganization significantly moderated the relationship between neurocognition and metacognition, with links between cognitive processes weakening when conceptual disorganization is present even at minimal levels of severity. There was no evidence that conceptual disorganization-or any other specific disorganized symptom-drove the limiting relationship of neurocognition on social cognition. Based on our findings, conceptual disorganization appears to be a critical piece of the puzzle when disentangling the relationship between neurocognition and metacognition. Roles of specific disorganized symptoms in the neurocognition - social cognition relationship were less clear. Findings from this study suggest that disorganized symptoms are an important treatment consideration when aiming to improve cognitive impairments.

Lexical Characteristics of Emotional Narratives in Schizophrenia: Relationships With Symptoms, Functioning, and Social Cognition

Previous research has suggested that complexity of speech, speech rate, use of emotion words, and use of pronouns are all potential indicators of important clinical components of schizophrenia, but little research has examined the relationships of these disturbances to cognitive variables impaired in schizophrenia, including social cognition. The current study examined these lexical differences to better characterize the cognitive substrates of speech disturbances in schizophrenia. Brief narratives of individuals with schizophrenia (n = 42) and non-clinical controls (n = 48) were compared according to their lexical characteristics, and these were examined for relationships to social cognition and real-world functioning. Significant differences between the groups were found in words per sentence (related to functioning, but not negative symptoms) as well as pronoun use (related to attributional style and theory of mind). Additionally, lexical characteristics effectively distinguished individuals with schizophrenia from non-clinical controls. Language disturbances in schizophrenia seem related to social cognition impairments and real-world functioning, and are a robust indicator of clinical status.