Latent semantic variables are associated with formal thought disorder and adaptive behavior in older inpatients with schizophrenia

The efficacy of low frequency repetitive transcial magnetic stimulation for treating auditory verbal hallucinations in schizophrenia: Insights from functional gradient analyses

Background

Auditory Verbal Hallucinations (AVH) constitute a prominent feature of schizophrenia. Although low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated therapeutic benefits in ameliorating AVH, the underlying mechanisms of its efficacy necessitate further elucidation.

Objective

This study investigated the cortical gradient characteristics and their associations with clinical responses in schizophrenia patients with AVH, mediated through 1 Hz rTMS targeting the left temporoparietal junction.

Method

Functional gradient metrics were employed to examine the hierarchy patterns of cortical organization, capturing whole-brain functional connectivity profiles in patients and controls.

Results

The 1 Hz rTMS treatment effectively ameliorated the positive symptoms in patients, specifically targeting AVH. Initial evaluations revealed expanded global gradient distribution patterns and specific principal gradient variations in certain brain regions in patients at baseline compared to a control cohort. Following treatment, these divergent global and local patterns showed signs of normalizing. Furthermore, there was observed a closer alignment in between-network dispersion among various networks after treatment, including the somatomotor, attention, and limbic networks, indicating a potential harmonization of brain functionality.

Conclusion

Low-frequency rTMS induces alternations in principal functional gradient patterns, may serve as imaging markers to elucidate the mechanisms underpinning the therapeutic efficacy of rTMS on AVH in schizophrenia.

SpEx: a German-language dataset of speech and executive function performance

This work presents data from 148 German native speakers (20-55 years of age), who completed several speaking tasks, ranging from formal tests such as word production tests to more ecologically valid spontaneous tasks that were designed to mimic natural speech. This speech data is supplemented by performance measures on several standardised, computer-based executive functioning (EF) tests covering domains of working-memory, cognitive flexibility, inhibition, and attention. The speech and EF data are further complemented by a rich collection of demographic data that documents education level, family status, and physical and psychological well-being. Additionally, the dataset includes information of the participants' hormone levels (cortisol, progesterone, oestradiol, and testosterone) at the time of testing. This dataset is thus a carefully curated, expansive collection of data that spans over different EF domains and includes both formal speaking tests as well as spontaneous speaking tasks, supplemented by valuable phenotypical information. This will thus provide the unique opportunity to perform a variety of analyses in the context of speech, EF, and inter-individual differences, and to our knowledge is the first of its kind in the German language. We refer to this dataset as SpEx since it combines speech and executive functioning data. Researchers interested in conducting exploratory or hypothesis-driven analyses in the field of individual differences in language and executive functioning, are encouraged to request access to this resource. Applicants will then be provided with an encrypted version of the data which can be downloaded.

A new schizophrenia screening instrument based on evaluating the patient's writing

Formal Thought Disorder (FTD) is a defining feature of schizophrenia, which is often assessed through patients' speech. Meanwhile, the written language is less studied. The aim of the present study is to establish and validate a comprehensive clinical screening scale, capturing the full variety of empirical characteristics of writing in patients with schizophrenia. The 16-item Screening Instrument for Schizophrenic Features in Writing (SISFiW) is derived from detailed literature review and a "brainstorming" discussion on 30 samples written by patients with schizophrenia. One hundred and fifty-seven participants (114 patients with an ICD-10 diagnoses of schizophrenia; 43 healthy control subjects) were interviewed and symptoms assessed with the Positive and Negative Syndrome Scale (PANSS) and the Scale for the Assessment of Thought, Language, and Communication (TLC). Article samples written by each participant were rated with the SISFiW. Results demonstrated significant difference of the SISFiW-total between the patient group and healthy controls [(3.61 ± 1.72) vs. (0.49 ± 0.63), t = 16.64, p<0.001]. The inter-rater reliability (weighted kappa = 0.72) and the internal consistency (Cronbach's alpha coefficient = 0.613) were acceptable, but correlations with the criterion (PANSS and TLC) were unremarkable. The ROC analysis indicated a cutoff point at 2 with the maximal sensitivity (93.0 %)/specificity (93.0 %). Discriminant analysis of the SISFiW items yielded 8 classifiers that discriminated between the diagnostic groups at a perfect overall performance (with 90.4 % of original and 88.5 % cross-validated grouped cases classified correctly). This instrument appears to be practicable and reliable, with relatively robust discriminatory power, and may serve as a complementary tool to existing FTD rating scales.

Evidence for the factor structure of formal thought disorder: A systematic review

Disorganised speech, or, formal thought disorder (FTD), is considered one of the core features of psychosis, yet its factor structure remains debated. This systematic review aimed to identify the core dimensions of FTD. In line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), a systematic review was conducted on the FTD factor analytic literature. Sixteen studies were identified from PsycINFO, PubMed and Web of Science between October 1971 and January 2023. Across the 39 factor analyses investigated, findings demonstrated the prominence of a three-factor structure. Broad agreement was found for two factors within the three-factor model, which were typically referred to as disorganisation and negative, with the exact nature of the third dimension requiring further clarification. The quality assessment revealed some methodological challenges relating to the assessment of FTD and conducted factor analyses. Future research should clarify the exact nature of the third dimension across different patient groups and methodologies to determine whether a consistent transdiagnostic concept of FTD can be elucidated.

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.

The SPEAK study rationale and design: A linguistic corpus-based approach to understanding thought disorder

AIM

Psychotic symptoms are typically measured using clinical ratings, but more objective and sensitive metrics are needed. Hence, we will assess thought disorder using the Research Domain Criteria (RDoC) heuristic for language production, and its recommended paradigm of "linguistic corpus-based analyses of language output". Positive thought disorder (e.g., tangentiality and derailment) can be assessed using word-embedding approaches that assess semantic coherence, whereas negative thought disorder (e.g., concreteness, poverty of speech) can be assessed using part-of-speech (POS) tagging to assess syntactic complexity. We aim to establish convergent validity of automated linguistic metrics with clinical ratings, assess normative demographic variance, determine cognitive and functional correlates, and replicate their predictive power for psychosis transition among at-risk youths.

METHODS

This study will assess language production in 450 English-speaking individuals in Australia and Canada, who have recent onset psychosis, are at clinical high risk (CHR) for psychosis, or who are healthy volunteers, all well-characterized for cognition, function and symptoms. Speech will be elicited using open-ended interviews. Audio files will be transcribed and preprocessed for automated natural language processing (NLP) analyses of coherence and complexity. Data analyses include canonical correlation, multivariate linear regression with regularization, and machine-learning classification of group status and psychosis outcome.

CONCLUSIONS

This prospective study aims to characterize language disturbance across stages of psychosis using computational approaches, including psychometric properties, normative variance and clinical correlates, important for biomarker development. SPEAK will create a large archive of language data available to other investigators, a rich resource for the field.

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.

Computational analysis on verbal fluency reveals heterogeneity in subjective language interests and brain structure

Language is an essential higher cognitive function in humans and is often affected by psychiatric and neurological disorders. Objective measures like the verbal fluency test are often used to determine language dysfunction. Recent applications of computational approaches broaden insights into language-related functions. In addition, individuals diagnosed with a psychiatric or neurological disorder also often report subjective difficulties in language-related functions. Therefore, we investigated the association between objective and subjective measures of language functioning, on the one hand, and inter-individual structural variations in language-related brain areas, on the other hand. We performed a Latent Semantic analysis (LSA) on a semantic verbal fluency task in 101 healthy adult participants. To investigate if these objective measures are associated with a subjective one, we examined assessed subjective natural tendency of interest in language-related activity with a study-specific questionnaire. Lastly, a voxel-based brain morphometry (VBM) was conducted to reveal associations between objective (LSA) measures and structural changes in language-related brain areas. We found a positive correlation between the LSA measure cosine similarity and the subjective interest in language. Furthermore, we found that higher cosine similarity corresponds to higher gray matter volume in the right cerebellum. The results suggest that people with higher interests in language access semantic knowledge in a more organized way exhibited by higher cosine similarity and have larger grey matter volume in the right cerebellum, when compared to people with lower interests. In conclusion, we demonstrate that there is inter-individual diverseness of accessing the semantic knowledge space and that it is associated with subjective language interests as well as structural differences in the right cerebellum.

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.

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.

Dispositional and ideological factor correlate of conspiracy thinking and beliefs

This study explored how the Big Five personality traits, as well as measures of personality disorders, are related to two different measures of conspiracy theories (CTs)The two measures correlated r = .58 and were applied to examine generalisability of findings. We also measured participants (N = 397) general knowledge levels and ideology in the form of religious and political beliefs. Results show that the Big Five and ideology are related to CTs but these relationships are generally wiped out by the stronger effects of the personality disorder scales. Two personality disorder clusters (A and B) were significant correlates of both CT measures, in both cases accounting for similar amounts of variance (20%). The personality disorders most predictive of conspiracy theories were related to the A cluster, characterized by schizotypal symptoms such as oddities of thinking and loose associations. These findings were corroborated by an additional analysis using Latent Semantic Analysis (LSA). LSA demonstrated that the items measuring schizotypal and related symptoms are cognitively related to both our measures of CTs. The implications for the studying of CTs is discussed, and limitations are acknowledged.

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.

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.

Processing Argument Structure and Syntactic Complexity in People with Schizophrenia Spectrum Disorders

INTRODUCTION

Deficits in language comprehension and production have been repeatedly observed in Schizophrenia Spectrum Disorders (SSD). However, the characterization of the language profile of this population is far from complete, and the relationship between language deficits, impaired thinking and cognitive functions is widely debated.

OBJECTIVE

The aims of the present study were to assess production and comprehension of verbs with different argument structures, as well as production and comprehension of sentences with canonical and non-canonical word order in people with SSD. In addition, the study investigated the relationship between language deficits and cognitive functions.

METHODS

Thirty-four participants with a diagnosis of SSD and a group of healthy control participants (HC) were recruited and evaluated using the Italian version of the Northwestern Assessment of Verbs and Sentences (NAVS, Cho-Reyes & Thompson, 2012; Barbieri et al., 2019).

RESULTS

Results showed that participants with SSD were impaired - compared to HC - on both verb and sentence production, as well as on comprehension of syntactically complex (but not simple) sentences. While verb production was equally affected by verb-argument structure complexity in both SSD and HC, sentence comprehension was disproportionately more affected by syntactic complexity in SSD than in HC. In addition, in the SSD group, verb production deficits were predicted by performance on a measure of visual attention, while sentence production and comprehension deficits were explained by performance on measures of executive functions and working memory, respectively.

DISCUSSION

Our findings support the hypothesis that language deficits in SSD may be one aspect of a more generalized, multi-domain, cognitive impairment, and are consistent with previous findings pointing to reduced inter- and intra-hemispheric connectivity as a possible substrate for such deficits. The study provides a systematic characterization of lexical and syntactic deficits in SSD and demonstrates that psycholinguistically-based assessment tools may be able to capture language deficits in this population.

High Predictive Accuracy of Negative Schizotypy With Acoustic Measures

Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning. Accuracy was good (> 80%) and was improved by considering speaking task and sex. However, the features identified as most predictive of negative schizotypal traits were generally not considered critical to their conceptual definitions. Implications for validating and implementing digital phenotyping to understand and quantify negative schizotypy are discussed.

Investigating the diagnostic utility of speech patterns in schizophrenia and their symptom associations

BACKGROUND

Speech disturbances are a recognised aspect of schizophrenia that may have potential utility as a diagnostic indicator. Recent advances in quantitative speech assessment methods have led to more reproducible and precise metrics making this possible. The current study sought firstly to characterise the speech profile of schizophrenia patients using quantitative speech measures, then examine the diagnostic utility of these measures and explore their relationship to symptoms.

METHODS

Speech recordings from 43 schizophrenia/schizoaffective disorder (SZ) patients and 46 healthy controls (HC) were obtained and transcribed. Cognitive and symptom measures were also administered.

RESULTS

Compared to HCs, SZ patients had higher incidences of aberrance across five types of quantitative speech variables: utterances, single words, time/speaking rate, turns and formulation errors, but not pauses. Based on two machine learning algorithms, 21 speech variables across the same five speech variable types (again not including pauses) were identified as significant classifiers for a schizophrenia diagnosis with 90-100% specificity and 80-90% sensitivity for both models. Selective relationships were also observed between these speech variables and only positive, disorganisation, excitement and formal thought disorder symptoms.

CONCLUSIONS

The findings support pervasive speech impairments in schizophrenia patients relative to HCs, and the potential diagnostic utility of these speech disturbances. Continued work is needed to build the evidence base for quantitative speech assessment as a future objective diagnostic tool for schizophrenia. It holds the promise of improved diagnostic accuracy leading to increased treatment efficacy and better patient outcomes.

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.

The cognitive aspect of formal thought disorder and its relationship with global social functioning and the quality of life in schizophrenia

PURPOSE

It was expected that using a comprehensive scale like the Thought and Language Disorder Scale (TALD) for measurement of FTD would enable assessing its heterogeneity and its associations with cognitive impairment and functionality. This study has aimed to analyze the relationship between formal thought disorder (FTD) and cognitive functions, functionality, and quality of life in schizophrenia.

METHODS

This cross-sectional exploratory study included 46 clinical participants meeting the DSM-5 diagnostic criteria for schizophrenia and 35 healthy individuals as the control groups. Data were acquired by means of the Turkish language version of the TALD, the Positive and Negative Syndrome Scale, the Clinical Global Impression Scale, the Functioning Assessment Short Test, the Social Functioning Scale, the World Health Organization Quality of Life Instrument-Short Form, and a neuropsychological test battery on executive functions, working memory, verbal fluency, abstract thinking, and response inhibition. Correlation analyses were conducted to detect significant relationships.

RESULTS

The clinical group scored failures in all cognitive tests. The objective positive FTD was associated with deficits in executive functions and social functioning. The objective negative FTD was associated with poor performance in all cognitive domains, physical quality of life, and social and global functioning. The subjective negative FTD was negatively correlated with psychological quality of life.

CONCLUSION

This study demonstrated that objective FTD factors reflect different underlying cognitive deficits and correlate with different functioning domains. Significant correlation was determined between subjective negative FTD and psychological quality of life. Given the close relationship of FTD with functioning and quality of life, the FTD-related cognitive deficits should be the key treatment goal in schizophrenia.

A Comparison of Connected Speech Tasks for Detecting Early Alzheimer’s Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning

Alzheimer’s disease (AD) has a long pre-clinical period, and so there is a crucial need for early detection, including of Mild Cognitive Impairment (MCI). Computational analysis of connected speech using Natural Language Processing and machine learning has been found to indicate disease and could be utilized as a rapid, scalable test for early diagnosis. However, there has been a focus on the Cookie Theft picture description task, which has been criticized. Fifty participants were recruited – 25 healthy controls (HC), 25 mild AD or MCI (AD+MCI) – and these completed five connected speech tasks: picture description, a conversational map reading task, recall of an overlearned narrative, procedural recall and narration of a wordless picture book. A high-dimensional set of linguistic features were automatically extracted from each transcript and used to train Support Vector Machines to classify groups. Performance varied, with accuracy for HC vs. AD+MCI classification ranging from 62% using picture book narration to 78% using overlearned narrative features. This study shows that, importantly, the conditions of the speech task have an impact on the discourse produced, which influences accuracy in detection of AD beyond the length of the sample. Further, we report the features important for classification using different tasks, showing that a focus on the Cookie Theft picture description task may narrow the understanding of how early AD pathology impacts speech.

Cognitive-perceptual and disorganized schizotypal traits are nonlinearly related to atypical semantic content on tasks of semantic fluency

Language deficits emerge early in the course of schizophrenia, yet research findings in those at-risk for schizophrenia, such as those with schizotypy, are mixed. The purpose of the present study was to elucidate the relationship of language ability, measured via semantic fluency, to schizotypy, examining both linear and non-linear relations. Semantic fluency data from 295 individuals with varying amounts of schizotypal traits were analyzed utilizing traditional methods (i.e., counting words generated that fit a specific semantic category). The content of semantic fluency responses was also analyzed via a semantic infrequency score (i.e., how infrequent participant responses were relative to all responses generated for the category in the study sample) and a total semantic productivity score (i.e., how many unique words generated overall, including those that did not fit the semantic category). Using traditional methods of scoring, schizotypy was not related to semantic fluency. However, schizotypy was non-linearly related to semantic infrequency and productivity, reflecting atypical semantic activation and processing. In particular, cognitive-perceptual and disorganized, but not interpersonal, traits were related to semantic infrequency and productivity. Valuable content-based information is missed when only analyzing semantic fluency data via the traditional method in the schizophrenia spectrum population. Cognitive-perceptual and disorganized traits, attenuated thought disorder symptoms, evidence the strongest relationship to semantic fluency, further illustrating the link between language and schizophrenia symptoms along the schizophrenia spectrum.

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.

What gets in the way of social engagement in schizophrenia?

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

Language as a biomarker for psychosis: A natural language processing approach

Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.

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.

How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease

Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability.

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.

Gray matter abnormalities in language processing areas and their associations with verbal ability and positive symptoms in first-episode patients with schizophrenia spectrum psychosis

BACKGROUND

Impaired verbal communication is a prominent feature in patients with schizophrenia. Verbal communication difficulties adversely affect psychosocial outcomes and worsen schizophrenia's clinical manifestation. In the present study, we aimed to investigate associations among gray matter (GM) volumes in language processing areas (LPAs), verbal ability, and positive symptoms in first-episode patients (FEPs) with schizophrenia spectrum psychosis.

METHODS

We enrolled 94 FEPs and 52 healthy controls (HCs) and subjected them to structural magnetic resonance imaging. The GM volumes of the bilateral pars opercularis (POp), pars triangularis (PTr), planum temporale (PT), Heschl's gyrus (HG), insula, and fusiform gyrus (FG), were estimated and compared between the FEPs and HCs. Verbal intelligence levels and positive symptom severity were examined for correlations with the left LPA volumes.

RESULTS

The GM volumes of the left POp, HG, and FG were significantly smaller in the FEPs than in the HCs, while the right regions showed no significant between-group difference. A multiple linear regression model revealed that larger left PT volume was associated with better verbal intelligence in FEPs. In exploratory correlation analysis, several LPAs showed significant correlations with the severity of positive symptoms in FEPs. The left FG volume had a strong inverse correlation with the severity of auditory verbal hallucinations, while the left PT volume was inversely associated with the severity of positive formal thought disorder and delusions. Moreover, the volume of the left insula was positively associated with the severity of bizarre behavior.

CONCLUSIONS

The present study suggests that GM abnormalities in the LPAs, which can be detected during the early stage of illness, may underlie impaired verbal communication and positive symptoms in patients with schizophrenia spectrum psychosis.

A machine learning approach to predicting psychosis using semantic density and latent content analysis

Subtle features in people’s everyday language may harbor the signs of future mental illness. Machine learning offers an approach for the rapid and accurate extraction of these signs. Here we investigate two potential linguistic indicators of psychosis in 40 participants of the North American Prodrome Longitudinal Study. We demonstrate how the linguistic marker of semantic density can be obtained using the mathematical method of vector unpacking, a technique that decomposes the meaning of a sentence into its core ideas. We also demonstrate how the latent semantic content of an individual’s speech can be extracted by contrasting it with the contents of conversations generated on social media, here 30,000 contributors to Reddit. The results revealed that conversion to psychosis is signaled by low semantic density and talk about voices and sounds. When combined, these two variables were able to predict the conversion with 93% accuracy in the training and 90% accuracy in the holdout datasets. The results point to a larger project in which automated analyses of language are used to forecast a broad range of mental disorders well in advance of their emergence.

Spared bottom-up but impaired top-down interactive effects during naturalistic language processing in schizophrenia: evidence from the visual-world paradigm

BACKGROUND

People with schizophrenia process language in unusual ways, but the causes of these abnormalities are unclear. In particular, it has proven difficult to empirically disentangle explanations based on impairments in the top-down processing of higher level information from those based on the bottom-up processing of lower level information.

METHODS

To distinguish these accounts, we used visual-world eye tracking, a paradigm that measures spoken language processing during real-world interactions. Participants listened to and then acted out syntactically ambiguous spoken instructions (e.g. 'tickle the frog with the feather', which could either specify how to tickle a frog, or which frog to tickle). We contrasted how 24 people with schizophrenia and 24 demographically matched controls used two types of lower level information (prosody and lexical representations) and two types of higher level information (pragmatic and discourse-level representations) to resolve the ambiguous meanings of these instructions. Eye tracking allowed us to assess how participants arrived at their interpretation in real time, while recordings of participants' actions measured how they ultimately interpreted the instructions.

RESULTS

We found a striking dissociation in participants' eye movements: the two groups were similarly adept at using lower level information to immediately constrain their interpretations of the instructions, but only controls showed evidence of fast top-down use of higher level information. People with schizophrenia, nonetheless, did eventually reach the same interpretations as controls.

CONCLUSIONS

These data suggest that language abnormalities in schizophrenia partially result from a failure to use higher level information in a top-down fashion, to constrain the interpretation of language as it unfolds in real time.

Comprehension of Embedded Clauses in Schizophrenia With and Without Formal Thought Disorder

Formal thought disorder (FTD) in schizophrenia (SZ) is clinically manifested primarily through language production, where linguistic studies have reported numerous anomalies including lesser use of embedded clauses. Here, we explored whether problems of language may extend to comprehension and clause embedding in particular. A sentence-picture matching task was designed with two conditions in which embedded clauses were presupposed as either true (factive) or not. Performance across these two conditions was compared in people with SZ and moderate-to-severe FTD (SZ + FTD), SZ with minimal FTD (SZ-FTD), first-degree relatives of people with SZ, and neurotypical controls. The SZ + FTD group performed significantly worse than all others in both conditions, and worse in the nonfactive than in the factive one. These results demonstrate language dysfunction in comprehension specific to FTD is a critical aspect of grammatical complexity and its associated meaning, which has been independently known to be cognitively significant as well.

Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia

Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.

Evidence for gesture-speech mismatch detection impairments in schizophrenia

Patients with schizophrenia suffer from impairments in the perception and production of gestures. The extent to which patients can access the semantic association between speech and co-verbal gestures in concrete or abstract/metaphorical meaning contexts is unknown. We investigated 1) how patients differ from controls in gesture matching performance, 2) how performance differs in the context of abstract versus concrete meaning, and 3) whether formal thought disorder (FTD) symptom severity predicts task impairment. Forty-five patients with schizophrenia spectrum disorders (two subgroups, "mild" and "severe") took part in this study. Participants were presented with video clips, each showing an actor saying a sentence while producing a gesture. Sentences contained either concrete or abstract/metaphorical information, and the accompanying gesture was either semantically related or unrelated to the sentence. Participants indicated via button press whether the gesture matched the content of the verbal utterance. Both patient subgroups demonstrated reduced performance in all comparisons. A significant interaction was found between patient subgroup and sentence abstractness. Task performance was worst for patients with severe positive FTD symptomatology in the abstract condition, while there were no patient subgroup differences in the concrete condition. These data shed new light on gesture-speech mismatch detection impairments in schizophrenia.

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

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

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

Although rating scales to assess formal thought disorder exist, there are no objective, high-reliability instruments that can quantify and track it. This proof-of-concept study shows that CoVec, a new automated tool, is able to differentiate between controls and patients with schizophrenia with derailment and tangentiality. According to ratings from the derailment and tangentiality items of the Scale for the Assessment of Positive Symptoms, we divided the sample into three groups: controls, patients without formal thought disorder, and patients with derailment/tangentiality. Their lists of animals produced during a one-minute semantic fluency task were processed using CoVec, a newly developed software that measures the semantic similarity of words based on vector semantic analysis. CoVec outputs were Mean Similarity, Coherence, Coherence-5, and Coherence-10. Patients with schizophrenia produced fewer words than controls. Patients with derailment had a significantly lower mean number of words and lower Coherence-5 than controls and patients without derailment. Patients with tangentiality had significantly lower Coherence-5 and Coherence-10 than controls and patients without tangentiality. Despite the small samples of patients with clinically apparent thought disorder, CoVec was able to detect subtle differences between controls and patients with either or both of the two forms of disorganization.

Semantic coherence in psychometric schizotypy: An investigation using Latent Semantic Analysis

Technological advancements have led to the development of automated methods for assessing semantic coherence in psychiatric populations. Latent Semantic Analysis (LSA) is an automated method that has been used to quantify semantic coherence in schizophrenia-spectrum disorders. The current study examined whether: 1) Semantic coherence reductions extended to psychometrically-defined schizotypy and 2) Greater cognitive load further reduces semantic coherence. LSA was applied to responses generated during category fluency tasks in baseline and cognitive load conditions. Significant differences between schizotypy and non-schizotypy groups were not observed. Findings suggest that semantic coherence may be relatively preserved at this point on the schizophrenia-spectrum.

Action (verb) fluency deficits in schizophrenia spectrum disorders: linking language, cognition and interpersonal functioning

Deficits in action (verb) fluency have previously been reported in schizophrenia spectrum disorders. The degree to which this reflects difficulties generating verbs in different semantic categories is unknown. Here, action fluency responses of 46 patients with schizophrenia spectrum disorders and 76 healthy controls were classified as action or mental state verbs, using well-established taxonomies. The word length, frequency, age of acquisition, valence and concreteness of the verbs produced were also examined. Participants also completed measures of cognitive function, and clinical symptoms. Independent inter-rater agreement of semantic categorization was high. The percentage of action verbs produced was significantly lower in patients than controls, whilst the percentage of mental state verbs produced did not differ. Patients' action verbs were: significantly less concrete; positively correlated with memory and intelligence; and negatively correlated with interpersonal symptoms. Impaired action verb, but intact mental state verb generation is consistent with the neural separability of these processes.

Can RDoC Help Find Order in Thought Disorder?

Thought disorder is a pernicious and nonspecific aspect of numerous serious mental illnesses (SMIs) and related conditions. Despite decades of empirical research on thought disorder, our present understanding of it is poor, our clinical assessments focus on a limited set of extreme behaviors, and treatments are palliative at best. Applying a Research Domain Criteria (RDoC) framework to thought disorder research offers advantages to explicate its phenotype; isolate its mechanisms; and develop more effective assessments, treatments, and potential cures. In this commentary, we discuss ways in which thought disorder can be understood within the RDoC framework. We propose operationalizing thought disorder within the RDoC construct of language using psycholinguistic sciences, to help objectify and quantify language within individuals; technologically sophisticated paradigms, to allow naturalistic behavioral sampling techniques with unprecedented ecological validity; and computational modeling, to account for a network of interconnected and dynamic linguistic, cognitive, affective, and social functions. We also highlight challenges for understanding thought disorder within an RDoC framework. Thought disorder likely does not occur as an isomorphic dysfunction in a single RDoC construct, but rather, as multiple potential dysfunctions in a network of RDoC constructs. Moreover, thought disorder is dynamic over time and context within individuals. In sum, RDoC is a useful framework to integrate multidisciplinary research efforts aimed at operationalizing, understanding, and ameliorating thought disorder.

Psychosis in behavioral variant frontotemporal dementia

BACKGROUND

Dementia is generally characterized by cognitive impairment that can be accompanied by psychotic symptoms; for example, visual hallucinations are a core feature of dementia with Lewy bodies, and delusions are often seen in Alzheimer's disease. However, for behavioral variant of frontotemporal dementia (bvFTD), studies on the broad spectrum of psychotic symptoms are still lacking. The aim of this study was to systematically and prospectively subtype the wide spectrum of psychotic symptoms in probable and definite bvFTD.

METHODS

In this study, a commonly used and validated clinical scale that quantifies the broad spectrum of psychotic symptoms (Positive and Negative Symptom Scale) was used in patients with probable and definite bvFTD (n=22) and with a primary psychiatric disorder (n=35) in a late-onset frontal lobe cohort. Median symptom duration was 2.8 years, and the patients were prospectively followed for 2 years.

RESULTS

In total, 22.7% of bvFTD patients suffered from delusions, hallucinatory behavior, and suspiciousness, although the majority of the patients exhibited negative psychotic symptoms such as social and emotional withdrawal and blunted affect (95.5%) and formal thought disorders (81.8%). "Difficulty in abstract thinking" and "stereotypical thinking" (formal thought disorders) differentiated bvFTD from psychiatric disorders. The combined predictors difficulty in abstract thinking, stereotypical thinking, "anxiety", "guilt feelings," and "tension" explained 75.4% of variance in the diagnosis of bvFTD versus psychiatric diagnoses (P<0.001).

CONCLUSION

Delusions, hallucinatory behavior, and suspiciousness were present in one-fifth of bvFTD patients, whereas negative psychotic symptoms such as social and emotional withdrawal, blunted affect, and formal thought disorders were more frequently present. This suggests that negative psychotic symptoms and formal thought disorders have an important role in the psychiatric misdiagnosis in bvFTD; misdiagnosis in bvFTD might be reduced by systematically exploring the broad spectrum of psychiatric symptoms.

Relation of formal thought disorder to symptomatic remission and social functioning in schizophrenia

OBJECTIVE

The aim of this cross-sectional study is to examine the relation of formal thought disorder (FTD) with symptomatic remission (SR) and social functioning in patients with schizophrenia.

METHOD

The study was carried out with a sample consisting of 117 patients diagnosed with schizophrenia according to DSM-IV. The patients were assessed with the Positive and Negative Syndrome Scale (PANSS), the Thought and Language Index (TLI), and the Personal and Social Performance Scale (PSP). We used logistic regression in order to determine the relation between FTD and SR and linear regression to identify the strength of association between FTD and social functioning.

RESULTS

Logistic regression analysis revealed that poverty of speech (odds ratio: 1.47, p<0.01) and peculiar logic (odds ratio: 1.66, p=0.01) differentiated the remitted patients from the non-remitted ones. Linear regression analysis showed that the PSP total score was associated with poverty of speech and peculiar logic items of the TLI (B=-0.23, p<0.01, B=-0.24, p=0.01, respectively).

CONCLUSION

Our findings suggest that poverty of speech and peculiar logic are the specific domains of FTD which are related to both SR status and social functioning in patients with schizophrenia.

The Hyper-Modular Associative Mind: A Computational Analysis of Associative Responses of Persons with Asperger Syndrome

Rigidity of thought is considered a main characteristic of persons with Asperger syndrome (AS). This rigidity may explain the poor comprehension of unusual semantic relations, frequently exhibited by persons with AS. Research indicates that such deficiency is related to altered mental lexicon organization, but has never been directly examined. The present study used computational network science tools to compare the mental lexicon structure of persons with AS and matched controls. Persons with AS and matched controls generated free associations, and network tools were used to extract and compare the mental lexicon structure of the two groups. The analysis revealed that persons with AS exhibit a hyper-modular semantic organization: their mental lexicon is more compartmentalized compared to matched controls. We argue that this hyper-modularity may be related to the rigidity of thought which characterizes persons with AS and discuss the clinical and more general cognitive implications of our findings.

Characterizing cognitive performance in a large longitudinal study of aging with computerized semantic indices of verbal fluency

A computational approach for estimating several indices of performance on the animal category verbal fluency task was validated, and examined in a large longitudinal study of aging. The performance indices included the traditional verbal fluency score, size of semantic clusters, density of repeated words, as well as measures of semantic and lexical diversity. Change over time in these measures was modeled using mixed effects regression in several groups of participants, including those that remained cognitively normal throughout the study (CN) and those that were diagnosed with mild cognitive impairment (MCI) or Alzheimer's disease (AD) dementia at some point subsequent to the baseline visit. The results of the study show that, with the exception of mean cluster size, the indices showed significantly greater declines in the MCI and AD dementia groups as compared to CN participants. Examination of associations between the indices and cognitive domains of memory, attention and visuospatial functioning showed that the traditional verbal fluency scores were associated with declines in all three domains, whereas semantic and lexical diversity measures were associated with declines only in the visuospatial domain. Baseline repetition density was associated with declines in memory and visuospatial domains. Examination of lexical and semantic diversity measures in subgroups with high vs. low attention scores (but normal functioning in other domains) showed that the performance of individuals with low attention was influenced more by word frequency rather than strength of semantic relatedness between words. These findings suggest that various automatically semantic indices may be used to examine various aspects of cognitive performance affected by dementia.

Psychopathological Symptoms Assessed by a System-Specific Approach Are Related to Global Functioning in Schizophrenic Disorders

BACKGROUND

By mostly using a positive-negative approach, several studies have identified factors that influence day-to-day functioning. We applied a different, system-specific approach to expand the knowledge of this issue.

SAMPLING AND METHODS

We recruited a sample of 100 inpatients with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder. Psychopathological characteristics were assessed with the Bern Psychopathology Scale (BPS) and functional characteristics with the Global Assessment of Functioning (GAF) scale. Linear regression analyses were performed with the GAF score as the dependent variable and the global values of the BPS subscores as independent variables. The model was controlled for confounding variables. Spearman rank correlation analyses were used to identify associations between the relevant BPS subdomains and global functioning.

RESULTS

Higher absolute global values of the BPS domains language (px2009; = x2009;0.038) and motor behavior (px2009; = x2009;0.049) were significantly associated with lower GAF scores. These findings remained stable after adjusting for potential confounding variables. A statistically significant negative correlation was found between both qualitative symptoms (rx2009; = x2009;-0.273, px2009; = x2009;0.006) and indirect signs (rx2009; = x2009;-0.269, px2009; = x2009;0.007) of the language domain and GAF scores. Also, quantitative (rx2009; = x2009;-0.211, px2009; = x2009;0.035) and qualitative symptoms (rx2009; = x2009;-0.214, px2009; = x2009;0.033) in the motor behavior domain were associated with poorer functioning.

CONCLUSIONS

A system-specific approach can describe subgroups of patients with poor functioning. Identifying such subgroups could help to utilize targeted treatment opinions in a timely manner. Another goal of future research is to clarify the underlying neurobiological deficits.

A Hierarchical Generative Framework of Language Processing: Linking Language Perception, Interpretation, and Production Abnormalities in Schizophrenia

Language and thought dysfunction are central to the schizophrenia syndrome. They are evident in the major symptoms of psychosis itself, particularly as disorganized language output (positive thought disorder) and auditory verbal hallucinations (AVHs), and they also manifest as abnormalities in both high-level semantic and contextual processing and low-level perception. However, the literatures characterizing these abnormalities have largely been separate and have sometimes provided mutually exclusive accounts of aberrant language in schizophrenia. In this review, we propose that recent generative probabilistic frameworks of language processing can provide crucial insights that link these four lines of research. We first outline neural and cognitive evidence that real-time language comprehension and production normally involve internal generative circuits that propagate probabilistic predictions to perceptual cortices - predictions that are incrementally updated based on prediction error signals as new inputs are encountered. We then explain how disruptions to these circuits may compromise communicative abilities in schizophrenia by reducing the efficiency and robustness of both high-level language processing and low-level speech perception. We also argue that such disruptions may contribute to the phenomenology of thought-disordered speech and false perceptual inferences in the language system (i.e., AVHs). This perspective suggests a number of productive avenues for future research that may elucidate not only the mechanisms of language abnormalities in schizophrenia, but also promising directions for cognitive rehabilitation.

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

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

Automated analysis of free speech predicts psychosis onset in high-risk youths

BACKGROUND/OBJECTIVES

Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals.

AIMS

In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis.

METHODS

Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed.

RESULTS

Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms.

CONCLUSIONS

Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry.

Automated computerized analysis of speech in psychiatric disorders

PURPOSE OF REVIEW

Disturbances in communication are a hallmark feature of severe mental illnesses. Recent technological advances have paved the way for objectifying communication using automated computerized semantic, linguistic and acoustic analyses. We review recent studies applying various computer-based assessments to the natural language produced by adult patients with severe mental illness.

RECENT FINDINGS

Automated computerized methods afford tools with which it is possible to objectively evaluate patients in a reliable, valid and efficient manner that complements human ratings. Crucially, these measures correlate with important clinical measures. The clinical relevance of these novel metrics has been demonstrated by showing their relationship to functional outcome measures, their in-vivo link to classic 'language' regions in the brain, and, in the case of linguistic analysis, their relationship to candidate genes for severe mental illness.

SUMMARY

Computer-based assessments of natural language afford a framework with which to measure communication disturbances in adults with severe mental illnesses. Emerging evidence suggests that they can be reliable and valid, and overcome many practical limitations of more traditional assessment methods. The advancement of these technologies offers unprecedented potential for measuring and understanding some of the most crippling symptoms of some of the most debilitating illnesses known to humankind.