How to reap the benefits of language for psychiatry

Our aim is to find accurate and valid markers for diagnosis, prognosis, and the monitoring of treatment to improve outcome for patients with schizophrenia-spectrum disorders. This search has led us into the disciplines of computational linguistics and artificial intelligence, as automatic analysis of spoken language may provide useful markers for psychiatry. Together with our language team at UMC Groningen and with great colleagues around the globe, we intend to push this field forward and provide tools that can support service users in self-monitoring and help clinicians with diagnosis, treatment monitoring and risk prediction

Towards better care for women with schizophrenia-spectrum disorders

Women with a schizophrenia-spectrum disorder (SSD) have a better clinical profile than do men at the start of their illness but progress to the same state within the first few years of living with SSD. There are benefits to be gained across different areas in the care currently offered to women with psychosis. An important point for improvement is the early detection of female-specific signs of a first episode of psychosis, to shorten the duration of untreated psychosis, with prompt access to early intervention services. Special attention should be paid to sexual health, and to any history of childhood trauma. Antipsychotics require dosing and prescription tailored to the female physiology that consider hormonal life phases such as menopause. Switching to prolactin-sparing medications can benefit both mental and somatic health. Finally, hormone replacement therapy should be considered for postmenopausal women. By providing female-specific care, women with schizophrenia-spectrum disorders will have optimal chances to fare well

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

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

The experience of felt presence in a general population sample.

Felt presence is a widely occurring experience, but remains under-recognised in clinical and research practice. To contribute to a wider recognition of the phenomenon, we aimed to assess the presentation of felt presence in a large population sample ( = 10 447) and explore its relation to key risk factors for psychosis. In our sample 1.6% reported experiencing felt presence in the past month. Felt presence was associated with visual and tactile hallucinations and delusion-like thinking; it was also associated with past occurrence of adverse events, loneliness and poor sleep. The occurrence of felt presence may function as a marker for general hallucination proneness

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

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

Auditory hallucinations, top-down processing and language perception: a general population study

Background: Studies investigating the underlying mechanisms of hallucinations in patients with schizophrenia suggest that an imbalance in top-down expectations v. bottom-up processing underlies these errors in perception. This study evaluates this hypothesis by testing if individuals drawn from the general population who have had auditory hallucinations (AH) have more misperceptions in auditory language perception than those who have never hallucinated. Methods: We used an online survey to determine the presence of hallucinations. Participants filled out the Questionnaire for Psychotic Experiences and participated in an auditory verbal recognition task to assess both correct perceptions (hits) and misperceptions (false alarms). A hearing test was performed to screen for hearing problems. Results: A total of 5115 individuals from the general Dutch population participated in this study. Participants who reported AH in the week preceding the test had a higher false alarm rate in their auditory perception compared with those without such (recent) experiences. The more recent the AH were experienced, the more mistakes participants made. While the presence of verbal AH (AVH) was predictive for false alarm rate in auditory language perception, the presence of non-verbal or visual hallucinations were not. Conclusions: The presence of AVH predicted false alarm rate in auditory language perception, whereas the presence of non-verbal auditory or visual hallucinations was not, suggesting that enhanced top-down processing does not transfer across modalities. More false alarms were observed in participants who reported more recent AVHs. This is in line with models of enhanced influence of top-down expectations in persons who hallucinate.publishedVersio

Raloxifene augmentation in men and women with a schizophrenia spectrum disorder:A study protocol

Although acute psychotic symptoms are often reduced by antipsychotic treatment, many patients with schizophrenia are impaired in daily functioning due to the persistence of negative and cognitive symptoms. Raloxifene, a Selective Estrogen Receptor Modulator (SERM) has been shown to be an effective adjunctive treatment in schizophrenia. Yet, there is a paucity in evidence for raloxifene efficacy in men and premenopausal women. We report the design of a study that aims to replicate earlier findings concerning the efficacy of raloxifene augmentation in reducing persisting symptoms and cognitive impairment in postmenopausal women, and to extend these findings to a male and peri/premenopausal population of patients with schizophrenia. The study is a multisite, placebo-controlled, double-blind, randomised clinical trial in approximately 110 adult men and women with schizophrenia. Participants are randomised 1:1 to adjunctive raloxifene 120 mg or placebo daily during 12 weeks. The treatment phase includes measurements at three time points (week 0, 6 and 12), followed by a follow-up period of two years. The primary outcome measure is change in symptom severity, as measured with the Positive and Negative Syndrome Scale (PANSS), and cognition, as measured with the Brief Assessment of Cognition in Schizophrenia (BACS). Secondary outcome measures include social functioning and quality of life. Genetic, hormonal and inflammatory biomarkers are measured to assess potential associations with treatment effects. If it becomes apparent that raloxifene reduces psychotic symptoms and/or improves cognition, social functioning and/or quality of life as compared to placebo, implementation of raloxifene in clinical psychiatric practice can be considered

How to reap the benefits of language for psychiatry

Our aim is to find accurate and valid markers for diagnosis, prognosis, and the monitoring of treatment to improve outcome for patients with schizophrenia-spectrum disorders. This search has led us into the disciplines of computational linguistics and artificial intelligence, as automatic analysis of spoken language may provide useful markers for psychiatry. Together with our language team at UMC Groningen and with great colleagues around the globe, we intend to push this field forward and provide tools that can support service users in self-monitoring and help clinicians with diagnosis, treatment monitoring and risk prediction