Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS)

Parent Psychopathology and Behavioral Effects on Child Brain-Symptom Networks in the ABCD Study

OBJECTIVE

Parents play a notable role in the development of child psychopathology. In this study, we investigated the role of parent psychopathology and behaviors on child brain-symptom networks to understand the role of intergenerational transmission of psychopathology. Few studies have documented the interaction of child psychopathology, parent psychopathology, and child neuroimaging.

METHOD

We used the baseline cohort of the Adolescent Brain Cognitive Development Study (N = 7,151, female-at-birth = 3,619, aged 9-11 years) to derive brain-symptom networks using sparse canonical correlation analysis with the Child Behavior Checklist and resting-state functional magnetic resonance imaging. We then correlated parent psychopathology symptoms and parental behaviors with child brain-symptom networks. Finally, we used the significant correlations to understand, using the mediation R package, whether parent behaviors mediated the effect of parent psychopathology on child brain connectivity.

RESULTS

We observed 3 brain-symptom networks correlated with externalizing (r = 0.19, internalizing (r = 0.17), and neurodevelopmental symptoms (r = 0.18). These corresponded to differences in connectivity between the default mode-default mode, default mode-control, and visual-visual canonical networks. We further detected aspects of parental psychopathology, including personal strength, thought problems, and rule-breaking symptoms to be associated with child brain connectivity. Finally, we found that parental behaviors and symptoms mediate each other's relationship to child brain connectivity.

CONCLUSION

The current study suggests that positive parental behaviors can relieve potentially detrimental effects of parental psychopathology, and vice versa, on symptom-correlated child brain connectivity. Altogether, these results provide a framework for future research and potential targets for parents who experience mental health symptoms to help mitigate potential intergenerational transmission of mental illness.

PLAIN LANGUAGE SUMMARY

Utilizing data from 7,151 participants in the ABCD Study, the authors found that positive parental behaviors, like acceptance and appropriate parental monitoring may reduce the potentially detrimental effects of parental psychopathology on child brain connectivity. These results provide potential targets for parents that experience mental health symptoms, or clinicians who treat families, to help mitigate potential intergenerational transmission of mental illness.

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech

Background

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Language Processing (NLP) methodologies enable the automated extraction of informative speech features, which has been leveraged for early psychosis detection and assessment of symptomology. However, critical gaps persist, including the absence of standardized sample collection protocols, small sample sizes, and a lack of multi-illness classification, limiting clinical applicability. Our study aimed to (1) identify an optimal assessment approach for the online and remote collection of speech, in the context of assessing the psychosis spectrum and evaluate whether a fully automated, speech-based machine learning (ML) pipeline can discriminate among different conditions on the schizophrenia-bipolar spectrum (SSD-BD-SPE), help-seeking comparison subjects (MDD), and healthy controls (HC) at varying layers of analysis and diagnostic complexity.

Methods

We adopted online data collection methods to collect 20 minutes of speech and demographic information from individuals. Participants were categorized as "healthy" help-seekers (HC), having a schizophrenia-spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), or being on the psychosis spectrum with sub-clinical psychotic experiences (SPE). SPE status was determined based on self-reported clinical diagnosis and responses to the PHQ-8 and PQ-16 screening questionnaires, while other diagnoses were determined based on self-report from participants. Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. A 70%-30% train-test split and 30-fold cross-validation was used to validate the model performance.

Results

The final analysis sample included 1140 individuals and 22,650 minutes of speech. Using 5-minutes of speech, our model could discriminate between HC and those with a serious mental illness (SSD or BD) with 86% accuracy (AUC = 0.91, Recall = 0.7, Precision = 0.98). Furthermore, our model could discern among HC, SPE, BD and SSD groups with 86% accuracy (F1 macro = 0.855, Recall Macro = 0.86, Precision Macro = 0.86). Finally, in a 5-class discrimination task including individuals with MDD, our model had 76% accuracy (F1 macro = 0.757, Recall Macro = 0.758, Precision Macro = 0.766).

Conclusion

Our ML pipeline demonstrated disorder-specific learning, achieving excellent or good accuracy across several classification tasks. We demonstrated that the screening of mental disorders is possible via a fully automated, remote speech assessment pipeline. We tested our model on relatively high number conditions (5 classes) in the literature and in a stratified sample of psychosis spectrum, including HC, SPE, SSD and BD (4 classes). We tested our model on a large sample (N = 1150) and demonstrated best-in-class accuracy with remotely collected speech data in the psychosis spectrum, however, further clinical validation is needed to test the reliability of model performance.

Exploring the conceptualisation, measurement, clinical utility and treatment of formal thought disorder in psychosis: A Delphi study

BACKGROUND

Formal Thought Disorder (FTD) is a recognised psychiatric symptom, yet its characterisation remains debated. This is problematic because it contributes to poor efficiency and heterogeneity in psychiatric research, with salient clinical impact.

OBJECTIVE

This study aimed to investigate expert opinion on the concept, measurement and clinical utility of FTD using the Delphi technique.

METHOD

Across three rounds, experts were queried on their definitions of FTD, methods for the assessment and measurement of FTD, associated clinical outcomes and treatment options.

RESULTS

Responses were obtained from 56 experts, demonstrating varying levels of consensus across different aspects of FTD. While consensus (>80 %) was reached for some aspects on the concept of FTD, including its definition and associated symptomology and mechanisms, others remained less clear. Overall, the universal importance attributed to the clinical understanding, measurement and treatment of FTD was clear, although consensus was infrequent as to the reasons behind and methods for doing so.

CONCLUSIONS

Our results contribute to the still elusive formal definition of FTD. The multitude of interpretations regarding these topics highlights the need for further clarity with this phenomenon. Our findings emphasised that the measurement and clinical utility of FTD are closely tied to the concept; hence, until there is agreement on the concept of FTD, difficulties with measuring and understanding its clinical usefulness to inform treatment interventions will persist. Future FTD research should focus on clarifying the factor structure and dimensionality to determine the latent structure and elucidate the core clinical phenotype.

The association between discourse production and schizotypal personality traits

Discourse abnormalities are a prominent feature in schizophrenia spectrum disorders, including poor lexical diversity, and have been found to differentiate patients from healthy subjects. However, discourse processing in individuals with high schizotypal personality traits is less understood and is often overshadowed by research on clinical psychotic symptoms. In the present study, we examined schizotypal traits at a non-clinical threshold and their association with lexical diversity and discourse coherence using two automated Natural Language Processing (NLP) tools - Type-Token-Ratio (TTR) measures from the Tool for the Automatic Analysis of Lexical Diversity (TAALED) and discourse coherence using sentence-level average cosign similarity with FastText to assess sentence similarity. 276 college students completed the full assessment including the Schizotypal Personality Questionnaire- Brief Revised (SPQ-BR) and recorded a speech sample describing a detailed painting. Results revealed that high schizotypal traits, specifically positive traits, were associated with lower lexical diversity and higher sentence similarity. Our findings suggest that even when clinically significant symptoms are not present, discourse abnormalities are present in healthy populations with high ST. The stronger association with positive traits suggests that theories of perseveration and top-down processing may warrant further investigation in schizophrenia-spectrum disorders.

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.

Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures

Automatically extracted measures of speech constitute a promising marker of psychosis as disorganized speech is associated with psychotic symptoms and predictive of psychosis-onset. The potential of speech markers is, however, hampered by (i) lengthy assessments in laboratory settings and (ii) manual transcriptions. We investigated whether a short, scalable data collection (online) and processing (automated transcription) procedure would provide data of sufficient quality to extract previously validated speech measures. To evaluate the fit of our approach for purpose, we assessed speech in relation to psychotic-like experiences in the general population. Participants completed an 8-minute-long speech task online. Sample 1 included measures of psychometric schizotypy and delusional ideation (N = 446). Sample 2 included a low and high psychometric schizotypy group (N = 144). Recordings were transcribed both automatically and manually, and connectivity, semantic, and syntactic speech measures were extracted for both types of transcripts. 73%/86% participants in sample 1/2 completed the experiment. Nineteen out of 25 speech measures were strongly (r > 0.7) and significantly correlated between automated and manual transcripts in both samples. Amongst the 14 connectivity measures, 11 showed a significant relationship with delusional ideation. For the semantic and syntactic measures, On Topic score and the Frequency of personal pronouns were negatively correlated with both schizotypy and delusional ideation. Combined with demographic information, the speech markers could explain 11-14% of the variation of delusional ideation and schizotypy in Sample 1 and could discriminate between high-low schizotypy with high accuracy (0.72-0.70, AUC = 0.78-0.79) in Sample 2. The moderate to high retention rate, strong correlation of speech measures across manual and automated transcripts and sensitivity to psychotic-like experiences provides initial evidence that online collected speech in combination with automatic transcription is a feasible approach to increase accessibility and scalability of speech-based assessment of psychosis.

Useful blunders: Can automated speech recognition errors improve downstream dementia classification?

OBJECTIVES

We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the "Cookie Theft" picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguishing between language samples from cognitively healthy individuals and those with Alzheimer's disease (AD).

METHODS

We conducted experiments using various ASR models, refining their transcripts with post-editing techniques. Both these imperfect ASR transcripts and manually transcribed ones were used as inputs for the downstream dementia classification. We conducted comprehensive error analysis to compare model performance and assess ASR-generated transcript effectiveness in dementia classification.

RESULTS

Imperfect ASR-generated transcripts surprisingly outperformed manual transcription for distinguishing between individuals with AD and those without in the "Cookie Theft" task. These ASR-based models surpassed the previous state-of-the-art approach, indicating that ASR errors may contain valuable cues related to dementia. The synergy between ASR and classification models improved overall accuracy in dementia classification.

CONCLUSION

Imperfect ASR transcripts effectively capture linguistic anomalies linked to dementia, improving accuracy in classification tasks. This synergy between ASR and classification models underscores ASR's potential as a valuable tool in assessing cognitive impairment and related clinical applications.

Assessing coherence through linguistic connectives: Analysis of speech in patients with schizophrenia-spectrum disorders

BACKGROUND

Incoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD.

METHODS

Connectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups.

RESULTS

SSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p < .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p < .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy.

DISCUSSION

Our results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD.

Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors

Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.

Combining automatic speech recognition with semantic natural language processing in schizophrenia

Natural language processing (NLP) tools are increasingly used to quantify semantic anomalies in schizophrenia. Automatic speech recognition (ASR) technology, if robust enough, could significantly speed up the NLP research process. In this study, we assessed the performance of a state-of-the-art ASR tool and its impact on diagnostic classification accuracy based on a NLP model. We compared ASR to human transcripts quantitatively (Word Error Rate (WER)) and qualitatively by analyzing error type and position. Subsequently, we evaluated the impact of ASR on classification accuracy using semantic similarity measures. Two random forest classifiers were trained with similarity measures derived from automatic and manual transcriptions, and their performance was compared. The ASR tool had a mean WER of 30.4%. Pronouns and words in sentence-final position had the highest WERs. The classification accuracy was 76.7% (sensitivity 70%; specificity 86%) using automated transcriptions and 79.8% (sensitivity 75%; specificity 86%) for manual transcriptions. The difference in performance between the models was not significant. These findings demonstrate that using ASR for semantic analysis is associated with only a small decrease in accuracy in classifying schizophrenia, compared to manual transcripts. Thus, combining ASR technology with semantic NLP models qualifies as a robust and efficient method for diagnosing schizophrenia.