Computational linguistic analysis applied to a semantic fluency task to measure derailment and tangentiality in schizophrenia

Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational language models

Speech in psychosis has long been ascribed as involving 'loosening of associations'. We pursued the aim to elucidate its underlying cognitive mechanisms by analysing picture descriptions from 94 subjects (29 healthy controls, 18 participants at clinical high risk, 29 with first-episode psychosis, and 18 with chronic schizophrenia), using five language models with different computational architectures: FastText, which represents meaning non-contextually/statically; BERT, which represents contextual meaning sensitive to grammar and context; Infersent and SBERT, which provide sentential representations; and CLIP, which evaluates speech relative to a visual stimulus. These models were used to quantify semantic distances crossed between successive tokens/sentences, and semantic perplexity indicating unexpectedness in continuations. Results showed that, among patients, semantic similarity increased when measured with FastText, Infersent, and SBERT, while it decreased with CLIP and BERT. Higher perplexity was observed in first-episode psychosis. Static semantic measures were associated with clinically measured impoverishment of thought and referential semantic measures with disorganization. These patterns indicate a shrinking conceptual semantic space as represented by static language models, which co-occurs with a widening in the referential semantic space as represented by contextual models. This duality underlines the need to separate these two forms of meaning for understanding mechanisms involved in semantic change in psychosis.

Automated analysis of speech as a marker of sub-clinical psychotic experiences

Automated speech analysis techniques, when combined with artificial intelligence and machine learning, show potential in capturing and predicting a wide range of psychosis symptoms, garnering attention from researchers. These techniques hold promise in predicting the transition to clinical psychosis from at-risk states, as well as relapse or treatment response in individuals with clinical-level psychosis. However, challenges in scientific validation hinder the translation of these techniques into practical applications. Although sub-clinical research could aid to tackle most of these challenges, there have been only few studies conducted in speech and psychosis research in non-clinical populations. This work aims to facilitate this work by summarizing automated speech analytical concepts and the intersection of this field with psychosis research. We review psychosis continuum and sub-clinical psychotic experiences, and the benefits of researching them. Then, we discuss the connection between speech and psychotic symptoms. Thirdly, we overview current and state-of-the art approaches to the automated analysis of speech both in terms of language use (text-based analysis) and vocal features (audio-based analysis). Then, we review techniques applied in subclinical population and findings in these samples. Finally, we discuss research challenges in the field, recommend future research endeavors and outline how research in subclinical populations can tackle the listed challenges.

Accumulating evidence for myriad alternatives: Modeling the generation of free association

The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models' huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people's associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts

Human cognition is underpinned by structured internal representations that encode relationships between entities in the world (cognitive maps). Clinical features of schizophrenia-from thought disorder to delusions-are proposed to reflect disorganization in such conceptual representations. Schizophrenia is also linked to abnormalities in neural processes that support cognitive map representations, including hippocampal replay and high-frequency ripple oscillations. Here, we report a computational assay of semantically guided conceptual sampling and exploit this to test a hypothesis that people with schizophrenia (PScz) exhibit abnormalities in semantically guided cognition that relate to hippocampal replay and ripples. Fifty-two participants [26 PScz (13 unmedicated) and 26 age-, gender-, and intelligence quotient (IQ)-matched nonclinical controls] completed a category- and letter-verbal fluency task, followed by a magnetoencephalography (MEG) scan involving a separate sequence-learning task. We used a pretrained word embedding model of semantic similarity, coupled to a computational model of word selection, to quantify the degree to which each participant's verbal behavior was guided by semantic similarity. Using MEG, we indexed neural replay and ripple power in a post-task rest session. Across all participants, word selection was strongly influenced by semantic similarity. The strength of this influence showed sensitivity to task demands (category > letter fluency) and predicted performance. In line with our hypothesis, the influence of semantic similarity on behavior was reduced in schizophrenia relative to controls, predicted negative psychotic symptoms, and correlated with an MEG signature of hippocampal ripple power (but not replay). The findings bridge a gap between phenomenological and neurocomputational accounts of schizophrenia.

Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling

BACKGROUND

Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD.

METHODS

We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and tangentiality (how quickly meaning drifts away from the topic) in detecting and dissociating the 2 underlying impairments.

RESULTS

Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were nonmonotonic in that forgetting the global context resulted in sentences that were semantically closer to their local, intermediate context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance.

CONCLUSIONS

This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial.

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.

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.

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.

Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia

BACKGROUND AND HYPOTHESIS

Disturbances in self-experience are a central feature of schizophrenia and its study can enhance phenomenological understanding and inform mechanisms underlying clinical symptoms. Self-experience involves the sense of self-presence, of being the subject of one's own experiences and agent of one's own actions, and of being distinct from others. Self-experience is traditionally assessed by manual rating of interviews; however, natural language processing (NLP) offers automated approach that can augment manual ratings by rapid and reliable analysis of text.

STUDY DESIGN

We elicited autobiographical narratives from 167 patients with schizophrenia or schizoaffective disorder (SZ) and 90 healthy controls (HC), amounting to 490 000 words and 26 000 sentences. We used NLP techniques to examine transcripts for language related to self-experience, machine learning to validate group differences in language, and canonical correlation analysis to examine the relationship between language and symptoms.

STUDY RESULTS

Topics related to self-experience and agency emerged as significantly more expressed in SZ than HC (P < 10-13) and were decoupled from similarly emerging features such as emotional tone, semantic coherence, and concepts related to burden. Further validation on hold-out data showed that a classifier trained on these features achieved patient-control discrimination with AUC = 0.80 (P < 10-5). Canonical correlation analysis revealed significant relationships between self-experience and agency language features and clinical symptoms.

CONCLUSIONS

Notably, the self-experience and agency topics emerged without any explicit probing by the interviewer and can be algorithmically detected even though they involve higher-order metacognitive processes. These findings illustrate the utility of NLP methods to examine phenomenological aspects of schizophrenia.

Applications of Speech Analysis in Psychiatry

ABSTRACT

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

If you don’t let it in, you don’t have to get it out: Thought preemption as a method to control unwanted thoughts

To attain goals, people must proactively prevent interferences and react to interferences once they occur. Whereas most research focuses on how people deal with external interferences, here we investigate the use of proactive and reactive control in dealing with unwanted thoughts. To examine this question, we asked people to generate an association to each of several repeating cue words, while forbidding the repetition of associations. Reactively rejecting and replacing unwanted repeated associations after they occur entails slower response times. Conversely, proactive control entails constricting the search space and thus faster response times. To gain further insight into different potential proactive thought control mechanisms, we augmented the analysis of raw response times with a novel, hypothesis-based, tractable computational model describing how people serially sample associations. Our results indicate that people primarily react to unwanted thoughts after they occur. Yet, we found evidence for two latent proactive control mechanisms: one that allows people to mitigate the episodic strengthening of repeated thoughts, and another that helps avoid looping in a repetitive thought. Exploratory analysis showed a relationship between model parameters and self-reported individual differences in the control over unwanted thoughts in daily life. The findings indicate the novel task and model can advance our understanding of how people can and cannot control their thoughts and memories, and benefit future research on the mechanisms responsible for unwanted thought in different psychiatric conditions. Finally, we discuss implications concerning the involvement of associative thinking and various control processes in semantic fluency, decision-making and creativity.

Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies

OBJECTIVES

Machine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers often conceptualize such an approach as operating in isolation without much need for human involvement, yet it remains crucial to harness human-in-the-loop practices when developing and implementing such techniques as their absence may be catastrophic. We advocate for building ML-based technologies that collaborate with experts within psychiatry in all stages of implementation and use to increase model performance while simultaneously increasing the practicality, robustness, and reliability of the process.

METHODS

We showcase pitfalls of the traditional ML framework and explain how it can be improved with human-in-the-loop techniques. Specifically, we applied active learning strategies to the automatic scoring of a story recall task and compared the results to a traditional approach.

RESULTS

Human-in-the-loop methodologies supplied a greater understanding of where the model was least confident or had knowledge gaps during training. As compared to the traditional framework, less than half of the training data were needed to reach a given accuracy.

CONCLUSIONS

Human-in-the-loop ML is an approach to data collection and model creation that harnesses active learning to select the most critical data needed to increase a model's accuracy and generalizability more efficiently than classic random sampling would otherwise allow. Such techniques may additionally operate as safeguards from spurious predictions and can aid in decreasing disparities that artificial intelligence systems otherwise propagate.

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.

Computational linguistic analysis applied to a semantic fluency task: A replication among first-episode psychosis patients with and without derailment and tangentiality

Automated tools do not yet exist to measure formal thought disorder, including derailment and tangentiality, both of which can be subjectively rated using the Scale for the Assessment of Positive Symptoms after a clinical research interview. CoVec, a new automated tool, measures the semantic similarity among words averaged in a five- and ten-word window (Coherence-5 and Coherence-10, respectively). One prior report demonstrated that this tool was able to differentiate between patients with those types of thought disorder and patients without them (and controls). Here, we attempted a replication of the initial findings using data from a different sample of patients hospitalized for initial evaluation of first-episode psychosis. Participants were administered a semantic fluency task and the animal lists were analyzed with CoVec. In this study, we partially replicated the prior findings, showing that first-episode patients with derailment had significantly lower Coherence-5 and Coherence-10 compared with patients without derailment. Further research is warranted on this and other highly reliable and objective methods of detecting formal thought disorder through simple assessments such as semantic fluency tasks.

Quantified language connectedness in schizophrenia-spectrum disorders

Language abnormalities are a core symptom of schizophrenia-spectrum disorders and could serve as a potential diagnostic marker. Natural language processing enables quantification of language connectedness, which may be lower in schizophrenia-spectrum disorders. Here, we investigated connectedness of spontaneous speech in schizophrenia-spectrum patients and controls and determine its accuracy in classification. Using a semi-structured interview, speech of 50 patients with a schizophrenia-spectrum disorder and 50 controls was recorded. Language connectedness in a semantic word2vec model was calculated using consecutive word similarity in moving windows of increasing sizes (2-20 words). Mean, minimal and variance of similarity were calculated per window size and used in a random forest classifier to distinguish patients and healthy controls. Classification based on connectedness reached 85% cross-validated accuracy, with 84% specificity and 86% sensitivity. Features that best discriminated patients from controls were variance of similarity at window sizes between 5 and 10. We show impaired connectedness in spontaneous speech of patients with schizophrenia-spectrum disorders even in patients with low ratings of positive symptoms. Effects were most prominent at the level of sentence connectedness. The high sensitivity, specificity and tolerability of this method show that language analysis is an accurate and feasible digital assistant in diagnosing schizophrenia-spectrum disorders.

Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach

An impairment in pragmatic communication is a core feature of schizophrenia, often associated with difficulties in social interactions. The pragmatic deficits regard various pragmatic phenomena, e.g., direct and indirect communicative acts, deceit, irony, and include not only the use of language but also other expressive means such as non-verbal/extralinguistic modalities, e.g., gestures and body movements, and paralinguistic cues, e.g., prosody and tone of voice. The present paper focuses on the identification of those pragmatic features, i.e., communicative phenomena and expressive modalities, that more reliably discriminate between individuals with schizophrenia and healthy controls. We performed a multimodal assessment of communicative-pragmatic ability, and applied a machine learning approach, specifically a Decision Tree model, with the aim of identifying the pragmatic features that best separate the data into the two groups, i.e., individuals with schizophrenia and healthy controls, and represent their configuration. The results indicated good overall performance of the Decision Tree model, with mean Accuracy of 82%, Sensitivity of 76%, and Precision of 91%. Linguistic irony emerged as the most relevant pragmatic phenomenon in distinguishing between the two groups, followed by violation of the Gricean maxims, and then extralinguistic deceitful and sincere communicative acts. The results are discussed in light of the pragmatic theoretical literature, and their clinical relevance in terms of content and design of both assessment and rehabilitative training.

Comprehensive verbal fluency features predict executive function performance

Semantic verbal fluency (sVF) tasks are commonly used in clinical diagnostic batteries as well as in a research context. When performing sVF tasks to assess executive functions (EFs) the sum of correctly produced words is the main measure. Although previous research indicates potentially better insights into EF performance by the use of finer grained sVF information, this has not yet been objectively evaluated. To investigate the potential of employing a finer grained sVF feature set to predict EF performance, healthy monolingual German speaking participants (n = 230) were tested with a comprehensive EF test battery and sVF tasks, from which features including sum scores, error types, speech breaks and semantic relatedness were extracted. A machine learning method was applied to predict EF scores from sVF features in previously unseen subjects. To investigate the predictive power of the advanced sVF feature set, we compared it to the commonly used sum score analysis. Results revealed that 8 / 14 EF tests were predicted significantly using the comprehensive sVF feature set, which outperformed sum scores particularly in predicting cognitive flexibility and inhibitory processes. These findings highlight the predictive potential of a comprehensive evaluation of sVF tasks which might be used as diagnostic screening of EFs.

Differences in fractal patterns and characteristic periodicities between word salads and normal sentences: Interference of meaning and sound

Fractal dimensions and characteristic periodicities were evaluated in normal sentences, computer-generated word salads, and word salads from schizophrenia patients, in both Japanese and English, using the random walk patterns of vowels.　In normal sentences, the walking curves were smooth with gentle undulations, whereas computer-generated word salads were rugged with mechanical repetitions, and word salads from patients with schizophrenia were unreasonably winding with meaningless repetitive patterns or even artistic cohesion. These tendencies were similar in both languages. Fractal dimensions between normal sentences and word salads of schizophrenia were significantly different in Japanese [1.19 ± 0.09 (n = 90) and 1.15 ± 0.08 (n = 45), respectively] and English [1.20 ± 0.08 (n = 91), and 1.16 ± 0.08 (n = 42)] (p < 0.05 for both). Differences in long-range (>10) periodicities between normal sentences and word salads from schizophrenia patients were predominantly observed at 25.6 (p < 0.01) in Japanese and 10.7 (p < 0.01) in English. The differences in fractal dimension and characteristic periodicities of relatively long-range (>10) presented here are sensitive to discriminate between schizophrenia and healthy mental state, and could be implemented in social robots to assess the mental state of people in care.

Functional magnetic resonance imaging based on Chinese tasks to protect language function in epileptics

OBJECTIVE

To evaluate the efficacy of functional magnetic resonance imaging (fMRI) based on Chinese tasks to protect the language function in epileptics.

MATERIALS AND METHODS

A total of 34 native Chinese patients with epilepsy were enrolled and examined with BOLD-fMRI scan based on six Chinese tasks. The epileptics were randomly divided into the control group (n = 15) and the experimental group (n = 19). The control group underwent the hollowing and multiple subpial transection operation only based on intraoperative EEG, while the experimental group was under notification of task-state fMRI results in addition. Whereafter, the language ability of patients was evaluated by ABC assessment.

RESULTS

The brain regions related to Chinese function activated by different tasks were remarkably distinct and mainly concentrated in the temporal lobe and frontal lobe. In ontoanalysis, the activation signals of the fusiform gyrus, parahippocampal gyrus, hippocampus, and precentral gyrus were generally low or even could not be detected. Unlike ontoanalysis, group analysis showed that the main effect regions of AN and PN task were in right superior temporal gyrus. The main effect regions of FF and VFC task were in right middle temporal gyrus. The main effect region of SF task was in left superior temporal gyrus. The main effect region of VFL task was in right middle frontal gyrus. The ABC assessment score of the control group 6 months after surgery was significantly lower than that 1 week before surgery (p < .05), while there was no significant difference in the experimental group, and the score of the experimental group was higher than that of the control group.

CONCLUSION

In the surgical treatment of epilepsy, a personalized surgical plan, based on task-state fMRI and intraoperative EEG, can be developed according to the difference of activation areas to protect the language function and improve the quality of life in postoperative patients.

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

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

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.

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

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

Semantic Search in Psychosis: Modeling Local Exploitation and Global Exploration

Impairments in category verbal fluency task (VFT) performance have been widely documented in psychosis. These deficits may be due to disturbed “cognitive foraging” in semantic space, in terms of altered salience of cues that influence individuals to search locally within a subcategory of semantically related responses (“clustering”) or globally between subcategories (“switching”). To test this, we conducted a study in which individuals with schizophrenia (n = 21), schizotypal personality traits (n = 25), and healthy controls (n = 40) performed VFT with “animals” as the category. Distributional semantic model Word2Vec computed cosine-based similarities between words according to their statistical usage in a large text corpus. We then applied a validated foraging-based search model to these similarity values to obtain salience indices of frequency-based global search cues and similarity-based local cues. Analyses examined whether diagnosis predicted VFT performance, search strategies, cue salience, and the time taken to switch between vs search within clusters. Compared to control and schizotypal groups, individuals with schizophrenia produced fewer words, switched less, and exhibited higher global cue salience, indicating a selection of more common words when switching to new clusters. Global cue salience negatively associated with vocabulary ability in controls and processing speed in schizophrenia. Lastly, individuals with schizophrenia took a similar amount of time to switch to new clusters compared to control and schizotypal groups but took longer to transition between words within clusters. Findings of altered local exploitation and global exploration through semantic memory provide preliminary evidence of aberrant cognitive foraging in schizophrenia.

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.

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

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

The Ability of Patients With Schizophrenia to Comprehend and Produce Sincere, Deceitful, and Ironic Communicative Intentions: The Role of Theory of Mind and Executive Functions

Patients with schizophrenia are often described as impaired in several cognitive domains. Specifically, patients with schizophrenia often exhibit problems in solving tasks requiring theory of mind (ToM), i.e., the ability to ascribe mental states to oneself and others, communicative-pragmatic ability, i.e., the ability to use language and non-verbal expressive means to convey meaning in a given context, and executive functions (EF). This study aims to investigate the role of cognitive functions, such as general intelligence, selective attention, processing speed, and especially EF (working memory, cognitive flexibility, inhibition, and planning), and ToM in explaining the performance of individual with schizophrenia in comprehending and producing communicative acts expressed with different communicative intentions (i.e., sincere, deceitful, and ironic), and realized through linguistic and extralinguistic/non-verbal expressive means. Thirty-two patients with schizophrenia and an equal number of healthy controls performed tasks aiming to investigate their capacity to comprehend and produce sincere, deceitful, and ironic communicative acts in addition to a series of cognitive tasks evaluating EF and ToM. The results indicated that individuals with schizophrenia performed worse than the controls in the comprehension and production of all pragmatic phenomena investigated, as well as in all the cognitive functions examined. The patients with schizophrenia also exhibited an increasing trend of difficulty in comprehending and producing sincere, deceitful, and ironic communicative acts expressed through either linguistic or extralinguistic means. Furthermore, a multiple regression analysis of the patients' performance on the pragmatic tasks revealed that overall, the role of attention, general intelligence, and processing speed did not appear to significantly explain the patients' communicative-pragmatic performance. The inclusion of EF into the analysis did not contribute to increase the explained variance of the patients' ability to comprehend and produce the various pragmatic phenomena investigated. Only the addition of ToM could significantly increase the explained variance, but only in the comprehension and production of deceit expressed by language and the production of sincere communicative acts, also limited to linguistic production. We conclude that neither EF nor ToM are able to explain the decreasing trend detected in the patients' pragmatic performance.