Quantified speech as a marker:computational linguistics in schizophrenia-spectrum disorders

Speech contains a wealth of information about the speaker's state of mind, not only in the words used, but also in the way these words are pronounced. Recent developments in Natural Language Processing (NLP) have paved the way for rapid, systematic recording and analysis of quantifiable properties of spoken language. Schizophrenia spectrum disorders, a collection of serious psychiatric disorders, involve different aspects of language. Abnormalities in language are used by practitioners to make a diagnosis or to estimate the severity of complaints. By using NLP techniques it has become possible to make these deviations objective and quantifiable, so that subtle changes can also be detected. This thesis contains a number of studies on the application of NLP techniques to the spoken language of people with a schizophrenia spectrum disorder. The focus of the research presented here is mainly on semantics (what is told) and phonetics of speech (how is told). These aspects are used to distinguish individuals with schizophrenia spectrum disorders from healthy controls, to investigate the relationship between speech features and specific symptoms, the relationship with clinical subtypes, as well as the relationship with antipsychotic medication

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

BackgroundIncoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD.MethodsConnectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups.ResultsSSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p < .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p < .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy.DiscussionOur results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD

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

BackgroundIncoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD.MethodsConnectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups.ResultsSSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p < .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p < .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy.DiscussionOur results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD

Semantic and Acoustic Markers in Schizophrenia-Spectrum Disorders:A Combinatory Machine Learning Approach

BACKGROUND AND HYPOTHESIS: Speech is a promising marker to aid diagnosis of schizophrenia-spectrum disorders, as it reflects symptoms like thought disorder and negative symptoms. Previous approaches made use of different domains of speech for diagnostic classification, including features like coherence (semantic) and form (acoustic). However, an examination of the added value of each domain when combined is lacking as of yet. Here, we investigate the acoustic and semantic domains separately and combined. STUDY DESIGN: Using semi-structured interviews, speech of 94 subjects with schizophrenia-spectrum disorders (SSD) and 73 healthy controls (HC) was recorded. Acoustic features were extracted using a standardized feature-set, and transcribed interviews were used to calculate semantic word similarity using word2vec. Random forest classifiers were trained for each domain. A third classifier was used to combine features from both domains; 10-fold cross-validation was used for each model. RESULTS: The acoustic random forest classifier achieved 81% accuracy classifying SSD and HC, while the semantic domain classifier reached an accuracy of 80%. Joining features from the two domains, the combined classifier reached 85% accuracy, significantly improving on separate domain classifiers. For the combined classifier, top features were fragmented speech from the acoustic domain and variance of similarity from the semantic domain. CONCLUSIONS: Both semantic and acoustic analyses of speech achieved ~80% accuracy in classifying SSD from HC. We replicate earlier findings per domain, additionally showing that combining these features significantly improves classification performance. Feature importance and accuracy in combined classification indicate that the domains measure different, complementing aspects of speech.</p

Anomalies in language as a biomarker for schizophrenia

PURPOSE OF REVIEW: After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established. This article reviews current advances in evaluating the use of language as a diagnostic or prognostic tool in schizophrenia. RECENT FINDINGS: The development of computational linguistic tools to quantify language disturbances is rapidly gaining ground in the field of schizophrenia research. Current applications are the use of semantic space models and acoustic analyses focused on phonetic markers. These features are used in machine learning models to distinguish patients with schizophrenia from healthy controls or to predict conversion to psychosis in high-risk groups, reaching accuracy scores (generally ranging from 80 to 90%) that exceed clinical raters. Other potential applications for a language biomarker in schizophrenia are monitoring of side effects, differential diagnostics and relapse prevention. SUMMARY: Language disturbances are a key feature of schizophrenia. Although in its early stages, the emerging field of research focused on computational linguistics suggests an important role for language analyses in the diagnosis and prognosis of schizophrenia. Spoken language as a biomarker for schizophrenia has important advantages because it can be objectively and reproducibly quantified. Furthermore, language analyses are low-cost, time efficient and noninvasive in nature

Syntactic Network Analysis in Schizophrenia-Spectrum Disorders

BACKGROUND: Language anomalies are a hallmark feature of schizophrenia-spectrum disorders (SSD). Here, we used network analysis to examine possible differences in syntactic relations between patients with SSD and healthy controls. Moreover, we assessed their relationship with sociodemographic factors, psychotic symptoms, and cognitive functioning, and we evaluated whether the quantification of syntactic network measures has diagnostic value. STUDY DESIGN: Using a semi-structured interview, we collected speech samples from 63 patients with SSD and 63 controls. Per sentence, a syntactic representation (ie, parse tree) was obtained and used as input for network analysis. The resulting syntactic networks were analyzed for 11 local and global network measures, which were compared between groups using multivariate analysis of covariance, considering the effects of age, sex, and education. RESULTS: Patients with SSD and controls significantly differed on most syntactic network measures. Sex had a significant effect on syntactic measures, and there was a significant interaction between sex and group, as the anomalies in syntactic relations were most pronounced in women with SSD. Syntactic measures were correlated with negative symptoms (Positive and Negative Syndrome Scale) and cognition (Brief Assessment of Cognition in Schizophrenia). A random forest classifier based on the best set of network features distinguished patients from controls with 74% cross-validated accuracy. CONCLUSIONS: Examining syntactic relations from a network perspective revealed robust differences between patients with SSD and healthy controls, especially in women. Our results support the validity of linguistic network analysis in SSD and have the potential to be used in combination with other automated language measures as a marker for SSD.</p

Characterizing speech heterogeneity in schizophrenia-spectrum disorders

Schizophrenia-spectrum disorders (SSD) are highly heterogeneous in risk factors, symptom characteristics, and disease course outcome. Although speech anomalies have long been recognized as a core symptom of SSD, speech markers are an unexplored source of symptom heterogeneity that may be informative in recognizing relevant subtypes. This study investigated speech heterogeneity and its relation to clinical characteristics in a large sample of patients with SSD and healthy controls. Speech samples were obtained from 142 patients with SSD and 147 healthy controls by means of open-ended interviews. Speech was analyzed using standardized open-source acoustic speech software. Hierarchical clustering was conducted using acoustic speech markers. Symptom severity was rated with the Positive and Negative Syndrome Scale, and cognition was assessed with the Brief Assessment of Cognition for Schizophrenia. Three speech clusters could be distinguished in the patient group that differed regarding speech properties, independent of medication use. One cluster was characterized by mild speech disturbances, while two severely impaired clusters were recognized (fragmented speakers and prolonged pausers). Both clusters with severely impaired speech had more severe cognitive dysfunction than the mildly impaired speakers. Prolonged pausers specifically had difficulties with memory-related tasks. Prolonged pausing, as opposed to fragmented speaking, related to chronic active psychosis and refractory psychotic symptoms. Based on speech clustering, subtypes of patients emerged with distinct disease trajectories, symptomatology, and cognitive functioning. The identification of clinically relevant subgroups within SSD may help to characterize distinct profiles and benefit the tailoring of early intervention and improvement of long-term functional outcome. (PsycInfo Database Record (c) 2022 APA, all rights reserved).</p

Modular-Level Functional Connectome Alterations in Individuals With Hallucinations Across the Psychosis Continuum

Functional connectome alterations, including modular network organization, have been related to the experience of hallucinations. It remains to be determined whether individuals with hallucinations across the psychosis continuum exhibit similar alterations in modular brain network organization. This study assessed functional connectivity matrices of 465 individuals with and without hallucinations, including patients with schizophrenia and bipolar disorder, nonclinical individuals with hallucinations, and healthy controls. Modular brain network organization was examined at different scales of network resolution, including (1) global modularity measured as Qmax and Normalised Mutual Information (NMI) scores, and (2) within- and between-module connectivity. Global modular organization was not significantly altered across groups. However, alterations in within- and between-module connectivity were observed for higher-order cognitive (e.g., central-executive salience, memory, default mode), and sensory modules in patients with schizophrenia and nonclinical individuals with hallucinations relative to controls. Dissimilar patterns of altered within- and between-module connectivity were found bipolar disorder patients with hallucinations relative to controls, including the visual, default mode, and memory network, while connectivity patterns between visual, salience, and cognitive control modules were unaltered. Bipolar disorder patients without hallucinations did not show significant alterations relative to controls. This study provides evidence for alterations in the modular organization of the functional connectome in individuals prone to hallucinations, with schizophrenia patients and nonclinical individuals showing similar alterations in sensory and higher-order cognitive modules. Other higher-order cognitive modules were found to relate to hallucinations in bipolar disorder patients, suggesting differential neural mechanisms may underlie hallucinations across the psychosis continuum.publishedVersio

To continue or not to continue? Antipsychotic medication maintenance versus dose-reduction/discontinuation in first episode psychosis: HAMLETT, a pragmatic multicenter single-blind randomized controlled trial

BACKGROUND: Antipsychotic medication is effective for symptomatic treatment in schizophrenia-spectrum disorders. After symptom remission, continuation of antipsychotic treatment is associated with lower relapse rates and lower symptom severity compared to dose reduction/discontinuation. Therefore, most guidelines recommend continuation of treatment with antipsychotic medication for at least 1 year. Recently, however, these guidelines have been questioned as one study has shown that more patients achieved long-term functional remission in an early discontinuation condition-a finding that was not replicated in another recently published long-term study. METHODS/DESIGN: The HAMLETT (Handling Antipsychotic Medication Long-term Evaluation of Targeted Treatment) study is a multicenter pragmatic single-blind randomized controlled trial in two parallel conditions (1:1) investigating the effects of continuation versus dose-reduction/discontinuation of antipsychotic medication after remission of a first episode of psychosis (FEP) on personal and social functioning, psychotic symptom severity, and health-related quality of life. In total 512 participants will be included, aged between 16 and 60 years, in symptomatic remission from a FEP for 3-6 months, and for whom psychosis was not associated with severe or life-threatening self-harm or violence. Recruitment will take place at 24 Dutch sites. Patients are randomized (1:1) to: continuation of antipsychotic medication until at least 1 year after remission (original dose allowing a maximum reduction of 25%, or another antipsychotic drug in similar dose range); or gradual dose reduction till eventual discontinuation of antipsychotics according to a tapering schedule. If signs of relapse occur in this arm, medication dose can be increased again. Measurements are conducted at baseline, at 3, and 6 months post-baseline, and yearly during a follow-up period of 4 years. DISCUSSION: The HAMLETT study will offer evidence to guide patients and clinicians regarding questions concerning optimal treatment duration and when to taper off medication after remission of a FEP. Moreover, it may provide patient characteristics associated with safe dose reduction with a minimal risk of relapse. TRIAL STATUS: Protocol version 1.3, October 2018. The study is active and currently recruiting patients (since September 2017), with the first 200 participants by the end of 2019. We anticipate completing recruitment in 2022 and final assessments (including follow-up 3.5 years after phase one) in 2026. TRIAL REGISTRATION: European Clinical Trials Database, EudraCT number 2017-002406-12. Registered 7 J