Racial and Ethnic Biases in Computational Approaches to Psychopathology

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.

Self-concept and Narrative Identity in Youth at Clinical High Risk for Psychosis

BACKGROUND AND HYPOTHESIS

Disturbances of the narrative self and personal identity accompany the onset of psychotic disorders in late adolescence and early adulthood (a formative developmental stage for self-concept and personal narratives). However, these issues have primarily been studied retrospectively after illness onset, limiting any inferences about their developmental course.

STUDY DESIGN

Youth at clinical high risk for psychosis (CHR) (n = 49) and matched healthy comparison youth (n = 52) completed a life story interview (including self-defining memory, turning point, life challenge, and psychotic-like experience) and questionnaires assessing self-esteem, self-beliefs, self-concept clarity, and ruminative/reflective self-focus. Trained raters coded interviews for narrative identity themes of emotional tone, agency, temporal coherence, context coherence, self-event connections, and meaning-making (intraclass correlations >0.75). Statistical analyses tested group differences and relationships between self-concept, narrative identity, symptoms, and functioning.

STUDY RESULTS

CHR participants reported more negative self-esteem and self-beliefs, poorer self-concept clarity, and more ruminative self-focus, all of which related to negative symptoms. CHR participants narrated their life stories with themes of negative emotion and passivity (ie, lack of personal agency), which related to positive and negative symptoms. Reflective self-focus and autobiographical reasoning were unaffected and correlated. Autobiographical reasoning was uniquely associated with preserved role functioning.

CONCLUSIONS

This group of youth at CHR exhibited some, but not all, changes to self-concept and narrative identity seen in psychotic disorders. A core theme of negativity, uncertainty, and passivity ran through their semantic and narrative self-representations. Preserved self-reflection and autobiographical reasoning suggest sources of resilience and potential footholds for cognitive-behavioral and metacognitive interventions.

Speech characteristics yield important clues about motor function: Speech variability in individuals at clinical high-risk for psychosis

BACKGROUND AND HYPOTHESIS

Motor abnormalities are predictive of psychosis onset in individuals at clinical high risk (CHR) for psychosis and are tied to its progression. We hypothesize that these motor abnormalities also disrupt their speech production (a highly complex motor behavior) and predict CHR individuals will produce more variable speech than healthy controls, and that this variability will relate to symptom severity, motor measures, and psychosis-risk calculator risk scores.

STUDY DESIGN

We measure variability in speech production (variability in consonants, vowels, speech rate, and pausing/timing) in N = 58 CHR participants and N = 67 healthy controls. Three different tasks are used to elicit speech: diadochokinetic speech (rapidly-repeated syllables e.g., papapa…, pataka…), read speech, and spontaneously-generated speech.

STUDY RESULTS

Individuals in the CHR group produced more variable consonants and exhibited greater speech rate variability than healthy controls in two of the three speech tasks (diadochokinetic and read speech). While there were no significant correlations between speech measures and remotely-obtained motor measures, symptom severity, or conversion risk scores, these comparisons may be under-powered (in part due to challenges of remote data collection during the COVID-19 pandemic).

CONCLUSION

This study provides a thorough and theory-driven first look at how speech production is affected in this at-risk population and speaks to the promise and challenges facing this approach moving forward.

Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function

Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders.

Speech disturbances in schizophrenia: Assessing cross-linguistic generalizability of NLP automated measures of coherence

INTRODUCTION

Language disorders - disorganized and incoherent speech in particular - are distinctive features of schizophrenia. Natural language processing (NLP) offers automated measures of incoherent speech as promising markers for schizophrenia. However, the scientific and clinical impact of NLP markers depends on their generalizability across contexts, samples, and languages, which we systematically assessed in the present study relying on a large, novel, cross-linguistic corpus.

METHODS

We collected a Danish (DK), German (GE), and Chinese (CH) cross-linguistic dataset involving transcripts from 187 participants with schizophrenia (111DK, 25GE, 51CH) and 200 matched controls (129DK, 29GE, 42CH) performing the Animated Triangles Task. Fourteen previously published NLP coherence measures were calculated, and between-groups differences and association with symptoms were tested for cross-linguistic generalizability.

RESULTS

One coherence measure, i.e. second-order coherence, robustly generalized across samples and languages. We found several language-specific effects, some of which partially replicated previous findings (lower coherence in German and Chinese patients), while others did not (higher coherence in Danish patients). We found several associations between symptoms and measures of coherence, but the effects were generally inconsistent across languages and rating scales.

CONCLUSIONS

Using a cumulative approach, we have shown that NLP findings of reduced semantic coherence in schizophrenia have limited generalizability across different languages, samples, and measures. We argue that several factors such as sociodemographic and clinical heterogeneity, cross-linguistic variation, and the different NLP measures reflecting different clinical aspects may be responsible for this variability. Future studies should take this variability into account in order to develop effective clinical applications targeting different patient populations.

Voice Patterns as Markers of Schizophrenia: Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation

BACKGROUND AND HYPOTHESIS

Voice atypicalities are potential markers of clinical features of schizophrenia (eg, negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages.

STUDY DESIGN

We provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences.

STUDY RESULTS

We found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages.

CONCLUSIONS

The findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.

The computational psychiatry of antisocial behaviour and psychopathy

Antisocial behaviours such as disobedience, lying, stealing, destruction of property, and aggression towards others are common to multiple disorders of childhood and adulthood, including conduct disorder, oppositional defiant disorder, psychopathy, and antisocial personality disorder. These disorders have a significant negative impact for individuals and for society, but whether they represent clinically different phenomena, or simply different approaches to diagnosing the same underlying psychopathology is highly debated. Computational psychiatry, with its dual focus on identifying different classes of disorder and health (data-driven) and latent cognitive and neurobiological mechanisms (theory-driven), is well placed to address these questions. The elucidation of mechanisms that might characterise latent processes across different disorders of antisocial behaviour can also provide important advances. In this review, we critically discuss the contribution of computational research to our understanding of various antisocial behaviour disorders, and highlight suggestions for how computational psychiatry can address important clinical and scientific questions about these disorders in the future.

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.

Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation

BACKGROUND AND HYPOTHESIS

Despite decades of "proof of concept" findings supporting the use of Natural Language Processing (NLP) in psychosis research, clinical implementation has been slow. One obstacle reflects the lack of comprehensive psychometric evaluation of these measures. There is overwhelming evidence that criterion and content validity can be achieved for many purposes, particularly using machine learning procedures. However, there has been very little evaluation of test-retest reliability, divergent validity (sufficient to address concerns of a "generalized deficit"), and potential biases from demographics and other individual differences.

STUDY DESIGN

This article highlights these concerns in development of an NLP measure for tracking clinically rated paranoia from video "selfies" recorded from smartphone devices. Patients with schizophrenia or bipolar disorder were recruited and tracked over a week-long epoch. A small NLP-based feature set from 499 language samples were modeled on clinically rated paranoia using regularized regression.

STUDY RESULTS

While test-retest reliability was high, criterion, and convergent/divergent validity were only achieved when considering moderating variables, notably whether a patient was away from home, around strangers, or alone at the time of the recording. Moreover, there were systematic racial and sex biases in the model, in part, reflecting whether patients submitted videos when they were away from home, around strangers, or alone.

CONCLUSIONS

Advancing NLP measures for psychosis will require deliberate consideration of test-retest reliability, divergent validity, systematic biases and the potential role of moderators. In our example, a comprehensive psychometric evaluation revealed clear strengths and weaknesses that can be systematically addressed in future research.

A Brazilian bottom-up strategy to address mental health in a diverse population over a large territorial area - an inspiration for the use of digital mental health

Brazil is a continental country with a history of massive immigration waves from around the world. Consequently, the Brazilian population is rich in ethnic, cultural, and religious diversity, but suffers from tremendous socioeconomic inequality. Brazil has a documented history of categorizing individuals with culturally specific behaviors as mentally ill, which has led to psychiatric institutionalization for reasons that were more social than clinical. To address this, a "network for psychosocial care" was created in Brazil, that included mental health clinics and community services distributed throughout the country. This generates local support for mental health rehabilitation, integrating psychiatric care, family support and education/work opportunities. These clinics and community services are tailored to provide care for each specific area, and are more attuned to regional culture, values and neighborhood infrastructure. Here we review existing reports about the Brazilian experience, including advances in public policy on mental health, and challenges posed by the large diversity to the psychosocial rehabilitation.  In addition, we show how new digital technologies in general, and computational speech analysis in particular, can contribute to unbiased assessments, resulting in decreased stigma and more effective diagnosis of the mental diseases, with methods that are free of gender, ethnic, or socioeconomic biases.

Computerized analysis of facial expressions in serious mental illness

Blunted facial affect is a transdiagnostic component of Serious Mental Illness (SMI) and is associated with a host of negative outcomes. However, blunted facial affect is a poorly understood phenomenon, with no known cures or treatments. A critical step in better understanding its phenotypic expression involves clarifying which facial expressions are altered in specific ways and under what contexts. The current literature suggests that individuals with SMI show decreased positive facial expressions, but typical, or even increased negative facial expressions during laboratory tasks. While this literature has coalesced around general trends, significantly more nuance is available regarding what components facial expressions are atypical and how those components are associated with increased severity of clinical ratings. The present project leveraged computerized facial analysis to test whether clinician-rated blunted affect is driven by decreases in duration, intensity, or frequency of positive versus other facial expressions during a structured clinical interview. Stable outpatients meeting criteria for SMI (N = 59) were examined. Facial expression did not generally vary as a function of clinical diagnosis. Overall, clinically-rated blunted affect was not associated with positive expressions, but was associated with decreased surprise and increased anger, sadness, and fear expressions. Blunted affect is not a monolithic lack of expressivity, and increased precision in operationally defining it is critical for uncovering its causes and maintaining factors. Our discussion focuses on this effort, and on advancing digital phenotyping of blunted facial affect more generally.

Expectations for Artificial Intelligence (AI) in Psychiatry

PURPOSE OF REVIEW

Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.

RECENT FINDINGS

For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.