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

Weighing the role of social cognition and executive functioning in pragmatics in the schizophrenia spectrum: A systematic review and meta-analysis

Pragmatic impairment is diffused in schizophrenia spectrum disorders, but the literature still debates its neurocognitive underpinnings. This systematic review and meta-analysis aimed to investigate the neurocognitive correlates of pragmatic disorders in schizophrenia and determine the weight of social cognition and executive functioning on such disorders. Of the 2,668 records retrieved from the literature, 16 papers were included in the systematic review, mostly focused on non-literal meanings and discourse production in schizophrenia. Ten studies were included in the meta-analysis: pragmatics was moderately associated with both social cognition and executive functions (especially inhibition), but the link with social cognition was stronger. The mediation analysis showed that social cognition mediated the relationship between executive functions and pragmatics. Based on this, we proposed a hierarchical neurocognitive model where pragmatics stems from social cognition, while executive functions are the fertile ground supporting the other two domains, and we discuss its theoretical and clinical implications.

Social skills in neurodevelopmental disorders: a study using role-plays to assess adolescents and young adults with 22q11.2 deletion syndrome and autism spectrum disorders

BACKGROUNDS

Social skills are frequently impaired in neurodevelopmental disorders and genetic conditions, including 22q11.2 deletion syndrome (22q11DS) and autism spectrum disorders (ASD). Although often assessed with questionnaires, direct assessment provides a more valid estimate of the constructs. Role-plays (i.e., simulates situational settings) therefore appear to be an appropriate indicator of social skills in daily life.

METHODS

This co-registered study involved 53 individuals with 22q11DS, 34 individuals with ASD, and 64 typically developing (TD) peers aged 12-30 years. All participants were assessed with role-plays as well as parent-reported questionnaires and clinical interviews focusing on social skills, functioning and anxiety.

RESULTS

Both clinical groups showed impaired social skills compared to TD, but distinct social profiles emerged between the groups. Individuals with 22q11DS displayed higher social appropriateness and clarity of speech but weaker general argumentation and negotiation skills, with the opposite pattern observed in participants with ASD. No association was found between social skills measured by direct observation and caregiver reports. Social anxiety, although higher in clinical groups than in TD, was not associated with role-plays.

CONCLUSIONS

This study highlights the need to train social skills through tailored interventions to target the specific difficulties of each clinical population. It also highlights the importance of combining measures as they do not necessarily provide the same outcome.

Meta-analysis of the relationship between metacognition and disorganized symptoms in psychosis

OBJECTIVE

Disorganized symptoms show associations with metacognitive deficits in psychosis. However, the magnitude of this relationship is unclear. This meta-analysis aimed to 1) quantify relationships between metacognition and both disorganized symptoms and disorganized speech; and 2) examine moderators of these relationships (e.g., metacognition type, neurocognition).

METHOD

A literature search was conducted using PsycINFO, Web of Science, PubMed, and EMBASE databases. English-language studies measuring disorganized symptoms and metacognition (i.e., introspective accuracy, metacognitive beliefs, or metacognitive capacity) in psychosis were included. Random effects meta-analyses were conducted using Pearson's r.

RESULTS

Meta-analysis of 20 studies (n = 1490) resulted in a significant negative medium correlation between disorganized symptoms and metacognition (r = -0.332, 95 % CI [-0.423, -0.235]). Magnitude was moderated by metacognition type. A significant negative small correlation between disorganized speech and metacognition (r = -0.173, 95 % CI [-0.254, -0.089], n = 1470) was observed, with no significant moderators.

CONCLUSIONS

Results clarify the magnitude of the relationships between metacognition and both disorganized symptoms and disorganized speech. Significant relationships may indicate conceptual links, yet the different magnitudes may reflect a distinction between disorganized symptoms and speech. The moderator finding highlights that metacognitive capacity has an especially strong link to disorganized symptoms and underscores the need for careful distinction between types of metacognition in future work.

Disorganized Communication and Social Dysfunction in Schizophrenia: Emerging Concepts and Methods

PURPOSE OF REVIEW

In this review, we embrace the emerging field of second-person neuroscience to address disorganization in schizophrenia. We argue that the focus of interest for disorganization is the interpersonal space where shared mental processes ('social mind') occur based on the bio-behavioural synchrony between two (or more) interacting people. We lay out several bio-behavioural measures that can capture the component parts of this process. In particular, we highlight the real-time imaging technology of hyperscanning that enables multi-person analysis of naturalistic social interaction. We illustrate how these measures can be used in empirical studies by posing disorganization as a problem of interpersonal processing.

RECENT FINDINGS

Traditionally, disorganized speech and behaviour have been studied as the product of hidden cognitive processes ('private mind'). A dysfunction in these processes was attributed to the brain afflicted by the illness ('brain-bound mechanisms'). But this approach has contributed to challenges in measuring and quantifying disorganization. Consequently, the single-brain focus has not provided satisfactory clarity or led to effective treatments for persistent social dysfunction in schizophrenia. Social dysfunction is a core feature of schizophrenia. This dysfunction arises from disorganized interpersonal interaction that typifies the social profile of affected individuals. We outline challenges in employing several emerging concepts and methods and how they can be addressed to investigate the mechanisms of social dysfunction in schizophrenia.

The other side of the social interaction: Theory of mind impairments in people with schizophrenia are linked to other people's difficulties in understanding them

BACKGROUND

People with schizophrenia (SZ) often present with theory of mind (ToM) deficits and with speech production deficits. While a link has been established between ToM abilities and symptoms of thought disorder, much less is known about other aspects of speech production in SZ.

STUDY DESIGN

This is a case-control study in which 25 stable outpatients with recent-onset SZ (27.1 years, 22 men) and 22 matched healthy controls (25.6 years, 16 men) performed a collaborative, verbal production task with a real interaction partner. Blind raters scored how easy participants made it to understand them (Facility ratings), how interesting they were to listen to (Interest ratings) and how expressive they were (Expressivity ratings). ToM was assessed with the Combined Stories Test and Sarfati's cartoon task. Symptoms were assessed with the PANSS five-factor version.

STUDY RESULTS

Compared to healthy controls, SZ received significantly lower ratings for all three aspects of their verbal productions (Facility, Interest and Expressivity), despite the raters being blind to group membership. Interestingly, the Facility ratings were linked to ToM performance in the SZ group, which suggest that SZ participants who have difficulties understanding others (ToM deficits) also make it harder for others to understand them. Other notable findings include a strong link between the Expressivity ratings and the Interest ratings for both groups, and significant correlations between the Facility ratings and Cognitive/Disorganisation symptoms, and between the Expressivity ratings and both Negative and Depression/Anxiety symptoms in SZ.

CONCLUSION

Studying speech production during real, collaborative social interactions could help move beyond the individual approach to SZ deficits, making it possible to involve the interaction partners to promote more efficient communication for people with schizophrenia.

Syntactic complexity of spoken language in the diagnosis of schizophrenia: A probabilistic Bayes network model

In the clinical linguistics of schizophrenia, syntactic complexity has received much attention. In this study, we address whether syntactic complexity deteriorates within the six months following the first episode of psychosis in those who develop a diagnosis of schizophrenia. We collected data from a cohort of twenty-six first-episode psychosis and 12 healthy control subjects using the Thought and Language Index interview in response to three pictures from the Thematic Apperception Test at first assessment and after six months (the time of consensus diagnosis). An automated labeling (part-of-speech tagging) for specific syntactic elements calculated large and granular syntactic complexity indices with a focus on clause complexity as a particular case from this spoken language data. Probabilistic reasoning leveraging the conditional independence properties of Bayes networks revealed that consensus diagnosis of schizophrenia predicted a decrease in nominal subjects per clause among individuals with first episode psychosis. From the entire sample, we estimate a 95.4 % probability that a 50 % decrease in mean nominal subjects per clause after six months is explained by the presence of first episode psychosis. Among those with psychosis, a 30 % decrease in this clause-complexity index after six months of experiencing the first episode predicted with 95 % probability a consensus diagnosis of schizophrenia, representing a conditional relationship between a longitudinal decrease in syntactic complexity and a diagnosis of schizophrenia. We conclude that an early drift towards linguistic disorganization/impoverishment of clause complexity-at the granular level of nominal subject per clause-is a distinctive feature of schizophrenia that decreases longitudinally, thus differentiating schizophrenia from other psychotic illnesses with shared phenomenology.

Clinical and computational speech measures are associated with social cognition in schizophrenia spectrum disorders

In this study, we compared three domains of social cognition (emotion processing, mentalizing, and attribution bias) to clinical and computational language measures in 63 participants with schizophrenia spectrum disorders. Based on the active inference model for discourse, we hypothesized that emotion processing and mentalizing, but not attribution bias, would be related to language disturbances. Clinical ratings for speech disturbance assessed disorganized and underproductive dimensions. Computational features included speech graph metrics, use of modal verbs, use of first-person pronouns, cosine similarity of adjacent utterances, and measures of sentiment; these were represented by four principal components. We found that higher clinical ratings for disorganized speech were predicted by greater impairments in both emotion processing and mentalizing, and that these relationships remained significant when accounting for demographic variables, overall psychosis symptoms, and verbal ability. Similarly, a computational speech component reflecting insular speech was consistently predicted by impairment in emotion processing. There were notable trends for computational speech components reflecting underproductive speech and decreased content-rich speech predicting mentalizing ability. Exploratory longitudinal analyses in a small subset of participants (n = 17) found that improvements in both emotion processing and mentalizing predicted improvements in disorganized speech. Attribution bias did not demonstrate strong relationships with language measures. Altogether, our findings are consistent with the active inference model of discourse and suggest greater emphasis on treatments that target social cognitive and language systems.

Lower cohesion and altered first-person pronoun usage in the spoken life narratives of individuals with schizophrenia

Usage of computational tools to quantify language disturbances among individuals with psychosis is increasing, improving measurement efficiency and access to fine-grained constructs. However, few studies apply automated linguistic analysis to life narratives in this population. Such research could facilitate the measurement of psychosis-relevant constructs such as sense of agency, capacity to organize one's personal history, narrative richness, and perceptions of the roles that others play in one's life. Furthermore, research is needed to understand how narrative linguistic features relate to cognitive and social functioning. In the present study, individuals with schizophrenia (n = 32) and individuals without a psychotic disorder (n = 15) produced personal life narratives within the Indiana Psychiatric Illness Interview. Narratives were analyzed using the Coh-Metrix computational tool. Linguistic variables analyzed were indices of connections within causal and goal-driven speech (deep cohesion), unique word usage (lexical diversity), and pronoun usage. Individuals with schizophrenia compared to control participants produced narratives that were lower in deep cohesion, contained more first-person singular pronouns, and contained fewer first-person plural pronouns. Narratives did not significantly differ between groups in lexical diversity, third-person pronoun usage, or total word count. Cognitive-linguistic relationships emerged in the full sample, including significant correlations between greater working memory capacity and greater deep cohesion and lexical diversity. In the schizophrenia group, social problem-solving abilities did not correlate with linguistic variables but were associated with cognition. Findings highlight the relevance of psychotherapies which aim to promote recovery among individuals with psychosis through the construction of coherent life narratives and increasing agency and social connectedness.

Primary states of consciousness: A review of historical and contemporary developments

Primary states of consciousness are conceived as phylogenetically older states of consciousness as compared to secondary states governed by sociocultural inhibition. The historical development of the concept in psychiatry and neurobiology is reviewed, along with its relationship to theories of consciousness. We suggest that primary states of consciousness are characterized by a temporary breakdown of self-control accompanied by a merging of action, communication, and emotion (ACE fusion), ordinarily segregated in human adults. We examine the neurobiologic basis of this model, including its relation to the phenomenon of neural dedifferentiation, the loss of modularity during altered states of consciousness, and increased corticostriatal connectivity. By shedding light on the importance of primary states of consciousness, this article provides a novel perspective on the role of consciousness as a mechanism of differentiation and control. We discuss potential differentiators underlying a gradient from primary to secondary state of consciousness, suggesting changes in thalamocortical interactions and arousal function. We also propose a set of testable, neurobiologically plausible working hypotheses to account for their distinct phenomenological and neural signatures.

Lexical Diversity and Other Linguistic Measures in Schizophrenia: Associations With Negative Symptoms and Neurocognitive Performance

ABSTRACT

Straightforward linguistic measures may be indicators of reduced language production and lexical diversity among individuals with schizophrenia with negative symptoms and neurocognitive impairments. We compared 98 patients with schizophrenia to 101 unaffected controls on six language variables ( e.g. , number of relationships between objects, use of complex transitions in the narrative structure), number of words produced, and lexical diversity computed as the moving average type-token ratio from both speaking and writing tasks. Patients differed from controls on nearly all of the linguistic measures; number of words produced had the strongest effect, with an average Cohen's d of 0.68; values pertaining to lexical diversity were 0.50 and 0.32, respectively, for the speaking tasks and the writing tasks. Most measures were correlated with alogia and other domains of negative symptoms (including avolition-apathy and anhedonia-asociality), as well as with diverse neurocognitive domains, especially those pertaining to working memory, verbal learning, and verbal category fluency. Further work is needed to understand longitudinal changes in these linguistic variables, as well as their utility as measures of alogia.

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.

Mild vs. moderate: How behavioral speech measures predict metacognitive capacity across different levels of formal thought disorder

Disorganized speech is a key component of formal thought disorder (FTD) in schizophrenia. Recent work has tied disorganized speech to deficits in metacognition, or one's ability to integrate experiences to form complex mental representations. The level of FTD at which differences in metacognitive capacity emerge remains unclear. Across two studies, using different cut scores to form FTD groups, we aimed to 1) explore the relationship between disorganized speech and metacognition and 2) compare trained rater and automated analysis methods. Clinical interviews were coded for disorganized speech and metacognition using the Communication Disturbances Index (CDI), Coh-Metrix multidimensional indices, and Metacognition Assessment Scale. In Study 1, we examined CDI and Coh-Metrix's ability to predict metacognition in FTD (n = 16) and non-FTD (n = 29) groups. We hypothesized the FTD group would have lower metacognition and that both CDI and Coh-Metrix would account for significant variance in metacognition. In Study 2, we conducted the same analyses with an independent sample using more stringent FTD cut scores (FTD: n = 23; non-FTD: n = 23). Analyses indicated that at a moderate but not mild cutoff: 1) automated methods differentiated FTD and non-FTD groups, 2) differences in metacognition emerged, and 3) behavioral measures accounted for significant variance (34%) in metacognition. Results emphasize the importance of setting the FTD cutoff at a moderate level and using samples that contain high levels of FTD. Findings extend research linking FTD and metacognition and demonstrate the benefit of pairing trained rater and automated speech measures.

Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology

Introduction

Psychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, are mainly based on each psychiatrist's level of experience and often lack objectivity, potentially leading to disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. Some studies explored biomarkers, but biomarkers have yet to be used in clinical practice.

Aim

The purposes of this study are to create a large dataset of Japanese speech data labeled with detailed information on psychiatric disorders and neurocognitive disorders to quantify the linguistic features of those disorders using natural language processing and, finally, to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of them.

Methods

This study will have a multi-center prospective design. The DSM-5 or ICD-11 criteria for major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder and for major and minor neurocognitive disorders will be regarded as the inclusion criteria for the psychiatric disorder samples. For the healthy subjects, the absence of a history of psychiatric disorders will be confirmed using the Mini-International Neuropsychiatric Interview (M.I.N.I.). The absence of current cognitive decline will be confirmed using the Mini-Mental State Examination (MMSE). A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant; these interviews will include free conversation, picture-description task, and story-telling task, all of which 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 at an interval of at least one month.

Discussion

This study is unique in its large sample size and the novelty of its method, and has potential for applications in many fields. We have some challenges regarding inter-rater reliability and the linguistic peculiarities of Japanese. As of September 2022, we have collected a total of >1000 records from >400 participants. To the best of our knowledge, this data sample is one of the largest in this field.

Clinical Trial Registration

Identifier: UMIN000032141.

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.

Disorganization domain as a putative predictor of Treatment Resistant Schizophrenia (TRS) diagnosis: A machine learning approach

BACKGROUND

Treatment Resistant Schizophrenia (TRS) is the persistence of significant symptoms despite adequate antipsychotic treatment. Although consensus guidelines are available, this condition remains often unrecognized and an average delay of 4-9 years in the initiation of clozapine, the gold standard for the pharmacological treatment of TRS, has been reported. We aimed to determine through a machine learning approach which domain of the Positive and Negative Syndrome Scale (PANSS) 5-factor model was most associated with TRS.

METHODS

In a cross-sectional design, 128 schizophrenia patients were classified as TRS (n = 58) or non-TRS (n = 60) after a structured retrospective-prospective analysis of treatment response. The random forest algorithm (RF) was trained to analyze the relationship between the presence/absence of TRS and PANSS-based psychopathological factor scores (positive, negative, disorganization, excitement, and emotional distress). As a complementary strategy to identify the variables most associated with the diagnosis of TRS, we included the variables selected by the RF algorithm in a multivariate logistic regression model.

RESULTS

according to the RF model, patients with higher disorganization, positive, and excitement symptom scores were more likely to be classified as TRS. The model showed an accuracy of 67.19%, a sensitivity of 62.07%, and a specificity of 71.43%, with an area under the curve (AUC) of 76.56%. The multivariate model including disorganization, positive, and excitement factors showed that disorganization was the only factor significantly associated with TRS. Therefore, the disorganization factor was the variable most consistently associated with the diagnosis of TRS in our sample.

Widespread cortical thinning, excessive glutamate and impaired linguistic functioning in schizophrenia: A cluster analytic approach

Introduction

Symptoms of schizophrenia are closely related to aberrant language comprehension and production. Macroscopic brain changes seen in some patients with schizophrenia are suspected to relate to impaired language production, but this is yet to be reliably characterized. Since heterogeneity in language dysfunctions, as well as brain structure, is suspected in schizophrenia, we aimed to first seek patient subgroups with different neurobiological signatures and then quantify linguistic indices that capture the symptoms of "negative formal thought disorder" (i.e., fluency, cohesion, and complexity of language production).

Methods

Atlas-based cortical thickness values (obtained with a 7T MRI scanner) of 66 patients with first-episode psychosis and 36 healthy controls were analyzed with hierarchical clustering algorithms to produce neuroanatomical subtypes. We then examined the generated subtypes and investigated the quantitative differences in MRS-based glutamate levels [in the dorsal anterior cingulate cortex (dACC)] as well as in three aspects of language production features: fluency, syntactic complexity, and lexical cohesion.

Results

Two neuroanatomical subtypes among patients were observed, one with near-normal cortical thickness patterns while the other with widespread cortical thinning. Compared to the subgroup of patients with relatively normal cortical thickness patterns, the subgroup with widespread cortical thinning was older, with higher glutamate concentration in dACC and produced speech with reduced mean length of T-units (complexity) and lower repeats of content words (lexical cohesion), despite being equally fluent (number of words).

Conclusion

We characterized a patient subgroup with thinner cortex in first-episode psychosis. This subgroup, identifiable through macroscopic changes, is also distinguishable in terms of neurochemistry (frontal glutamate) and language behavior (complexity and cohesion of speech). This study supports the hypothesis that glutamate-mediated cortical thinning may contribute to a phenotype that is detectable using the tools of computational linguistics in schizophrenia.

Automatic language analysis identifies and predicts schizophrenia in first-episode of psychosis

Automated language analysis of speech has been shown to distinguish healthy control (HC) vs chronic schizophrenia (SZ) groups, yet the predictive power on first-episode psychosis patients (FEP) and the generalization to non-English speakers remain unclear. We performed a cross-sectional and longitudinal (18 months) automated language analysis in 133 Spanish-speaking subjects from three groups: healthy control or HC (n = 49), FEP (n = 40), and chronic SZ (n = 44). Interviews were manually transcribed, and the analysis included 30 language features (4 verbal fluency; 20 verbal productivity; 6 semantic coherence). Our cross-sectional analysis showed that using the top ten ranked and decorrelated language features, an automated HC vs SZ classification achieved 85.9% accuracy. In our longitudinal analysis, 28 FEP patients were diagnosed with SZ at the end of the study. Here, combining demographics, PANSS, and language information, the prediction accuracy reached 77.5% mainly driven by semantic coherence information. Overall, we showed that language features from Spanish-speaking clinical interviews can distinguish HC vs chronic SZ, and predict SZ diagnosis in FEP patients.

Progressive changes in descriptive discourse in First Episode Schizophrenia: a longitudinal computational semantics study

Computational semantics, a branch of computational linguistics, involves automated meaning analysis that relies on how words occur together in natural language. This offers a promising tool to study schizophrenia. At present, we do not know if these word-level choices in speech are sensitive to the illness stage (i.e., acute untreated vs. stable established state), track cognitive deficits in major domains (e.g., cognitive control, processing speed) or relate to established dimensions of formal thought disorder. In this study, we collected samples of descriptive discourse in patients experiencing an untreated first episode of schizophrenia and healthy control subjects (246 samples of 1-minute speech; n = 82, FES = 46, HC = 36) and used a co-occurrence based vector embedding of words to quantify semantic similarity in speech. We obtained six-month follow-up data in a subsample (99 speech samples, n = 33, FES = 20, HC = 13). At baseline, semantic similarity was evidently higher in patients compared to healthy individuals, especially when social functioning was impaired; but this was not related to the severity of clinically ascertained thought disorder in patients. Across the study sample, higher semantic similarity at baseline was related to poorer Stroop performance and processing speed. Over time, while semantic similarity was stable in healthy subjects, it increased in patients, especially when they had an increasing burden of negative symptoms. Disruptions in word-level choices made by patients with schizophrenia during short 1-min descriptions are sensitive to interindividual differences in cognitive and social functioning at first presentation and persist over the early course of the illness.

Semantic and phonetic similarity of verbal fluency responses in early-stage psychosis

Linguistic abnormalities can emerge early in the course of psychotic illness. Computational tools that quantify similarity of responses in standardized language-based tasks such as the verbal fluency test could efficiently characterize the nature and functional correlates of these disturbances. Participants with early-stage psychosis (n=20) and demographically matched controls without a psychiatric diagnosis (n=20) performed category and letter verbal fluency. Semantic similarity was measured via predicted context co-occurrence in a large text corpus using Word2Vec. Phonetic similarity was measured via edit distance using the VFClust tool. Responses were designated as clusters (related items) or switches (transitions to less related items) using similarity-based thresholds. Results revealed that participants with early-stage psychosis compared to controls had lower fluency scores, lower cluster-related semantic similarity, and fewer switches; mean cluster size and phonetic similarity did not differ by group. Lower fluency semantic similarity was correlated with greater speech disorganization (Communication Disturbances Index), although more strongly in controls, and correlated with poorer social functioning (Global Functioning: Social), primarily in the psychosis group. Findings suggest that search for semantically related words may be impaired soon after psychosis onset. Future work is warranted to investigate the impact of language disturbances on social functioning over the course of psychotic illness.

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.

More than a biomarker: could language be a biosocial marker of psychosis?

Automated extraction of quantitative linguistic features has the potential to predict objectively the onset and progression of psychosis. These linguistic variables are often considered to be biomarkers, with a large emphasis placed on the pathological aberrations in the biological processes that underwrite the faculty of language in psychosis. This perspective offers a reminder that human language is primarily a social device that is biologically implemented. As such, linguistic aberrations in patients with psychosis reflect both social and biological processes affecting an individual. Failure to consider the sociolinguistic aspects of NLP measures will limit their usefulness as digital tools in clinical settings. In the context of psychosis, considering language as a biosocial marker could lead to less biased and more accessible tools for patient-specific predictions in the clinic.

Metacognition, social cognition, and mentalizing in psychosis: are these distinct constructs when it comes to subjective experience or are we just splitting hairs?

Research using the integrated model of metacognition has suggested that the construct of metacognition could quantify the spectrum of activities that, if impaired, might cause many of the subjective disturbances found in psychosis. Research on social cognition and mentalizing in psychosis, however, has also pointed to underlying deficits in how persons make sense of their experience of themselves and others. To explore the question of whether metacognitive research in psychosis offers unique insight in the midst of these other two emerging fields, we have offered a review of the constructs and research from each field. Following that summary, we discuss ways in which research on metacognition may be distinguished from research on social cognition and mentalizing in three broad categories: (1) experimental procedures, (2) theoretical advances, and (3) clinical applications or indicated interventions. In terms of its research methods, we will describe how metacognition makes a unique contribution to understanding disturbances in how persons make sense of and interpret their own experiences within the flow of life. We will next discuss how metacognitive research in psychosis uniquely describes an architecture which when compromised - as often occurs in psychosis - results in the loss of persons' sense of purpose, possibilities, place in the world and cohesiveness of self. Turning to clinical issues, we explore how metacognitive research offers an operational model of the architecture which if repaired or restored should promote the recovery of a coherent sense of self and others in psychosis. Finally, we discuss the concrete implications of this for recovery-oriented treatment for psychosis as well as the need for further research on the commonalities of these approaches.

Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders

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.

[Metacognition as a pathway to the study and treatment of fragmentation in schizophrenia]

This paper explores the potential of recent research on metacognition to offer new avenues to assess and address the phenomenon of fragmentation in schizophrenia, which was described by E.Bleuler as «splitting». The concepts of metacognition characterize and quantify alterations or decrements in the processes by which fragments or pieces of information are integrated into a coherent sense of self and others. A method for assessing metacognition is presented along with research examining the presence and importance of metacognitive deficits in schizophrenia. Greater levels of metacognitive deficits have been detected in different phases of schizophrenia and linked to poorer psychosocial outcomes. These data were obtained both in foreign and preliminary Russian studies. The authors suggest that treatments, which successfully target metacognitive capacity, may uniquely promote wellness and recovery in schizophrenia.

Linguistic determinants of formal thought disorder in first episode psychosis

AIM

Thought disorder is a core feature of schizophrenia but assessment of disordered thinking is challenging, which may contribute to the paucity of mechanistic understanding of disorganization in early psychosis. We studied the use of linguistic connectives in relation to clinically quantified dimensions of thought disorder using automated speech analysis in untreated, first episode psychosis (FEPs) and healthy controls (HCs).

METHODS

39 treatment-naïve, actively psychotic FEPs and 23 group matched HCs were recruited. Three one-minute speech samples were induced in response to photographs from the Thematic Apperception Test and speech was analysed using COH-METRIX software. Five connectives variables from the Coh-Metrix software were reduced using principle component analysis, resulting in two linguistic connectives factors. Thought disorder was assessed using the Thought Language Index (TLI) and the PANSS-8.

RESULTS

Connective factors predicted disorganization, but not impoverishment suggesting aberrant use of connectives is specific to positive thought disorder. An independent t test comparing low and high disorganization FEPs showed higher load of acausal temporal connectives in high disorganization FEPs compared to low disorganization FEPs (mean [SD] in high vs low disorganization FEPs = 0.64 (1.1) vs -0.37 (1.02); t = 2.91, P = .006). Acausal-temporal connectives were not correlated with severity of symptoms or cognition suggesting connective use is a specific index of disorganized thinking rather than overall illness status.

CONCLUSIONS

Clinical assessment of disorganization in psychosis is likely linked to the aberrant use of connectives resulting in an intuitive sense of incoherence. In early psychosis, thought disorder may be reliably quantifiable using automated syntax analysis.

A cognitive model of diminished expression in schizophrenia: The interface of metacognition, cognitive symptoms and language disturbances

The resistance of negative symptoms to pharmacologic treatment has spurred interest in understanding the psychological factors that contribute to their formation and persistence. However, little is understood about the psychological processes that reinforce and sustain the negative symptoms domain of diminished expression. Prior research has shown that higher levels of diminished expression relate to deficits in metacognitive capacity. We propose a more complex model in which diminished expression occurs when impairments in metacognitive self-reflectivity, alterations in higher-order language structure, and cognitive symptoms interact and thus interfere with persons' ability to understand and express emotions in ways others can recognize. Individuals with schizophrenia-spectrum disorders (N = 201) provided personal narratives detailing their life story and reflections about their mental illness. Self-reflectivity was measured with the Metacognition Assessment Scale-Abbreviated, and situation models were extracted from participants' personal narratives via Coh-Metrix 3.0, an automated program that calculates language indices. Diminished expression and cognitive symptoms were measured with the Positive and Negative Syndrome Scale. Structural equation models (SEM) examined whether self-reflectivity mediated the impact of cognitive symptoms and situation models on diminished expression. Results of the SEM revealed that self-reflectivity partially mediated the impact of situation models on diminished expression (β = -.073, p = .008, ±95% CI [-0.126, -0.019]). and fully mediated the influence of cognitive symptoms in diminished expression (β = 0.099, p = .001, ±95% CI [0.038, 0.160]). In conclusion, results suggest that self-reflectivity, linguistic cohesion, and cognitive symptoms may be useful targets for intervention in efforts to treat diminished expression in psychosis.

Lowering costs for large-scale screening in psychosis: a systematic review and meta-analysis of performance and value of information for speech-based psychiatric evaluation

OBJECTIVE

Obstacles for computational tools in psychiatry include gathering robust evidence and keeping implementation costs reasonable. We report a systematic review of automated speech evaluation for the psychosis spectrum and analyze the value of information for a screening program in a healthcare system with a limited number of psychiatrists (Maputo, Mozambique).

METHODS

Original studies on speech analysis for forecasting of conversion in individuals at clinical high risk (CHR) for psychosis, diagnosis of manifested psychotic disorder, and first-episode psychosis (FEP) were included in this review. Studies addressing non-verbal components of speech (e.g., pitch, tone) were excluded.

RESULTS

Of 168 works identified, 28 original studies were included. Valuable speech features included direct measures (e.g., relative word counting) and mathematical embeddings (e.g.: word-to-vector, graphs). Accuracy estimates reported for schizophrenia diagnosis and CHR conversion ranged from 71 to 100% across studies. Studies used structured interviews, directed tasks, or prompted free speech. Directed-task protocols were faster while seemingly maintaining performance. The expected value of perfect information is USD 9.34 million. Imperfect tests would nevertheless yield high value.

CONCLUSION

Accuracy for screening and diagnosis was high. Larger studies are needed to enhance precision of classificatory estimates. Automated analysis presents itself as a feasible, low-cost method which should be especially useful for regions in which the physician pool is insufficient to meet demand.

The association of metacognition with emotion recognition and perspective taking in a Russian sample with psychosis

OBJECTIVE

Schizophrenia may reflect an interactive network of disturbances in cognition. In this study we have examined the relationship between two forms of cognition: metacognition and social cognition among a sample with schizophrenia (n = 41), early episode psychosis (n = 37), and major depression (n = 30) gathered in Moscow, Russia.

METHODS

Metacognition was assessed with the Metacognition Assessment Scale-Abbreviated. Social cognition was assessed with the Ekman 60 Faces Test and the Interpersonal Reactivity Index. Verbal memory and global psychopathology were included as potential covariates.

RESULTS

Partial correlations controlling for demographics, neurocognition, and psychopathology revealed greater metacognitive capacity was linked to better facial emotion recognition and perspective taking in the prolonged schizophrenia group. Greater metacognitive capacity in the early psychosis group was linked with greater facial emotion recognition. Metacognition and social cognition were not related to one another in the depression group.

CONCLUSIONS

Social cognition and metacognition may be uniquely related in psychosis.

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Speech represents a promising novel biomarker by providing a window into brain health, as shown by its disruption in various neurological and psychiatric diseases. As with many novel digital biomarkers, however, rigorous evaluation is currently lacking and is required for these measures to be used effectively and safely. This paper outlines and provides examples from the literature of evaluation steps for speech-based digital biomarkers, based on the recent V3 framework (Goldsack et al., 2020). The V3 framework describes 3 components of evaluation for digital biomarkers: verification, analytical validation, and clinical validation. Verification includes assessing the quality of speech recordings and comparing the effects of hardware and recording conditions on the integrity of the recordings. Analytical validation includes checking the accuracy and reliability of data processing and computed measures, including understanding test-retest reliability, demographic variability, and comparing measures to reference standards. Clinical validity involves verifying the correspondence of a measure to clinical outcomes which can include diagnosis, disease progression, or response to treatment. For each of these sections, we provide recommendations for the types of evaluation necessary for speech-based biomarkers and review published examples. The examples in this paper focus on speech-based biomarkers, but they can be used as a template for digital biomarker development more generally.

Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders

Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents "big data" at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.

The link between formal thought disorder and social functioning in schizophrenia: A meta-analysis

Background.

Formal thought disorder (FTD) and social functioning impairments are core symptoms of schizophrenia. Although both have been observed for over a century, the strength of the relationship between FTD and social functioning remains unclear. Furthermore, a variety of methodological approaches have been used to assess these constructs—which may contribute to inconsistency in reported associations. This meta-analysis aimed to: (a) systematically test the relationship between FTD and social functioning and (b) determine if the methodology used to assess FTD and/or social functioning moderates this relationship.

Methods.

Following Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) guidelines, a targeted literature search was conducted on studies examining the relationship between FTD and social functioning. Correlations were extracted and used to calculate weighted mean effect sizes using a random effects model.

Results.

A total of 1,478 participants across 13 unique studies were included in this meta-analysis. A small-medium inverse association (r = −0.23, p < 0.001) was observed between FTD and social functioning. Although heterogeneity analyses produced a significant Q-statistic (Q = 52.77, p = <0.001), the relationship between FTD and social functioning was not moderated by methodology, study quality, demographic variables, or clinical factors.

Conclusions.

Findings illustrate a negative association between FTD and social functioning. Despite differences in the methodological approach used and type of information assessed, measurement type and clinical factors did not moderate the relationship between FTD and social functioning. Future studies should explore whether other variables, such as cognitive processes (e.g., social cognition), may account for variability in associations between these constructs.

Piecing together fragments: Linguistic cohesion mediates the relationship between executive function and metacognition in schizophrenia

Speech disturbances are prevalent in psychosis. These may arise in part from executive function impairment, as research suggests that inhibition and monitoring are associated with production of cohesive discourse. However, it is not yet understood how linguistic and executive function impairments in psychosis interact with disrupted metacognition, or deficits in the ability to integrate information to form a complex sense of oneself and others and use that synthesis to respond to psychosocial challenges. Whereas discourse studies have historically employed manual hand-coding techniques, automated computational tools can characterize deep semantic structures that may be closely linked with metacognition. In the present study, we examined whether higher executive functioning promotes metacognition by way of altering linguistic cohesion. Ninety-four individuals with schizophrenia-spectrum disorders provided illness narratives and completed an executive function task battery (Delis-Kaplan Executive Function System). We assessed the narratives for linguistic cohesion (Coh-Metrix 3.0) and metacognitive capacity (Metacognition Assessment Scale - Abbreviated). Selected linguistic indices measured the frequency of connections between causal and intentional content (deep cohesion), word and theme overlap (referential cohesion), and unique word usage (lexical diversity). In path analyses using bootstrapped confidence intervals, we found that deep cohesion and lexical diversity independently mediated the relationship between executive functioning and metacognitive capacity. Findings suggest that executive control abilities support integration of mental experiences by way of increasing causal, goal-driven speech and word expression in individuals with schizophrenia. Metacognitive-based therapeutic interventions for psychosis may promote insight and recovery in part by scaffolding use of language that links ideas together.

Modeling Incoherent Discourse in Non-Affective Psychosis

BACKGROUND

Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis.

METHODS

Speech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects.

RESULTS

Coherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder.

CONCLUSIONS

Automated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.

Automatic Evaluation of Cancer Treatment Texts for Gist Inferences and Comprehension

Background. It is difficult to write about cancer for laypeople such that everyone understands. One common approach to readability is the Flesch-Kincaid Grade Level (FKGL). However, FKGL has been shown to be less effective than emerging discourse technologies in predicting readability. Objective. Guided by fuzzy-trace theory, we used the discourse technology Coh-Metrix to create a Gist Inference Score (GIS) and applied it to texts from the National Cancer Institute website written for patients and health care providers. We tested the prediction that patient cancer texts with higher GIS scores are likely to be better understood than others. Design. In study 1, all 244 cancer texts were systematically subjected to an automated Coh-Metrix analysis. In study 2, 9 of those patient texts (3 each at high, medium, and low GIS) were systematically converted to fill-the-blanks (Cloze) tests in which readers had to supply the missing words. Participants (162) received 3 texts, 1 at each GIS level. Measures. GIS was measured as the mean of 7 Coh-Metrix variables, and comprehension was measured through a Cloze procedure. Results. Although texts for patients scored lower on FKGL than those for providers, they also scored lower on GIS, suggesting difficulties for readers. In study 2, participants scored higher on the Cloze task for high GIS texts than for low- or medium-GIS texts. High-GIS texts seemed to better lend themselves to correct responses using different words. Limitations. GIS is limited to text and cannot assess inferences made from images. The systematic Cloze procedure worked well in aggregate but does not make fine-grained distinctions. Conclusions. GIS appears to be a useful, theoretically motivated supplement to FKGL for use in research and clinical practice.

Metacognitive function and fragmentation in schizophrenia: Relationship to cognition, self-experience and developing treatments

Bleuler suggested that fragmentation of thought, emotion and volition were the unifying feature of the disorders he termed schizophrenia. In this paper we review research seeking to measure some of the aspects of fragmentation related to the experience of the self and others described by Bleuler. We focus on work which uses the concept of metacognition to characterize and quantify alterations or decrements in the processes by which fragments or pieces of information are integrated into a coherent sense of self and others. We describe the rationale and support for one method for quantifying metacognition and its potential to study the fragmentation of a person's sense of themselves, others and the relative place of themselves and others in the larger human community. We summarize research using that method which suggests that deficits in metacognition commonly occur in schizophrenia and are related to basic neurobiological indices of brain functioning. We also present findings indicating that the capacity for metacognition in schizophrenia is positively related to a broad range of aspects of psychological and social functioning when measured concurrently and prospectively. Finally, we discuss the evolution and study of one therapy that targets metacognitive capacity, Metacognitive Reflection and Insight Therapy (MERIT) and its potential to treat fragmentation and promote recovery.