Controversies in psychiatry

Relative importance of speech and voice features in the classification of schizophrenia and depression

Speech is a promising biomarker for schizophrenia spectrum disorder (SSD) and major depressive disorder (MDD). This proof of principle study investigates previously studied speech acoustics in combination with a novel application of voice pathology features as objective and reproducible classifiers for depression, schizophrenia, and healthy controls (HC). Speech and voice features for classification were calculated from recordings of picture descriptions from 240 speech samples (20 participants with SSD, 20 with MDD, and 20 HC each with 4 samples). Binary classification support vector machine (SVM) models classified the disorder groups and HC. For each feature, the permutation feature importance was calculated, and the top 25% most important features were used to compare differences between the disorder groups and HC including correlations between the important features and symptom severity scores. Multiple kernels for SVM were tested and the pairwise models with the best performing kernel (3-degree polynomial) were highly accurate for each classification: 0.947 for HC vs. SSD, 0.920 for HC vs. MDD, and 0.932 for SSD vs. MDD. The relatively most important features were measures of articulation coordination, number of pauses per minute, and speech variability. There were moderate correlations between important features and positive symptoms for SSD. The important features suggest that speech characteristics relating to psychomotor slowing, alogia, and flat affect differ between HC, SSD, and MDD.

Theories of consciousness and psychiatric disorders - A comparative analysis

Disorders of consciousness represent an efficient way to test theories of consciousness' (ToCs) predictions. So far, ToCs have mostly focused on disorders of quantitative awareness such as coma, vegetative state, spatial neglect and hemianopia. Psychiatric disorders, by contrast, have received little attention, leaving their contribution to consciousness research almost unexplored. Therefore, this paper aims to assess the relation between ToCs and psychiatric disorders - that is, the extent to which current ToCs can account for psychiatric symptomatology. First, I review direct and indirect evidence linking each ToC to psychiatry disorders. Next, I differentiate ToCs based on their theoretical and methodological ground, highlighting how they distinctively address neural, cognitive, and phenomenological aspects of conscious experience and, in turn, psychiatric symptoms. Finally, I refer to one specific symptom to directly compare ToCs' explanatory power. Overall, Temporospatial Theory of Consciousness (TTC) appears to provide a more comprehensive account of psychiatric disorders, suggesting that a novel dimension of consciousness (i.e., form of consciousness) may be needed to address more qualitative alterations in conscious experience.

Syntactic complexity and diversity of spontaneous speech production in schizophrenia spectrum and major depressive disorders

Syntax, the grammatical structure of sentences, is a fundamental aspect of language. It remains debated whether reduced syntactic complexity is unique to schizophrenia spectrum disorder (SSD) or whether it is also present in major depressive disorder (MDD). Furthermore, the association of syntax (including syntactic complexity and diversity) with language-related neuropsychology and psychopathological symptoms across disorders remains unclear. Thirty-four SSD patients and thirty-eight MDD patients diagnosed according to DSM-IV-TR as well as forty healthy controls (HC) were included and tasked with describing four pictures from the Thematic Apperception Test. We analyzed the produced speech regarding its syntax delineating measures for syntactic complexity (the total number of main clauses embedding subordinate clauses) and diversity (number of different types of complex sentences). We performed cluster analysis to identify clusters based on syntax and investigated associations of syntactic, to language-related neuropsychological (verbal fluency and verbal episodic memory), and psychopathological measures (positive and negative formal thought disorder) using network analyses. Syntax in SSD was significantly reduced in comparison to MDD and HC, whereas the comparison of HC and MDD revealed no significant differences. No associations were present between speech measures and current medication, duration and severity of illness, age or sex; the single association accounted for was education. A cluster analysis resulted in four clusters with different degrees of syntax across diagnoses. Subjects with less syntax exhibited pronounced positive and negative symptoms and displayed poorer performance in executive functioning, global functioning, and verbal episodic memory. All cluster-based networks indicated varying degrees of domain-specific and cross-domain connections. Measures of syntactic complexity were closely related while syntactic diversity appeared to be a separate node outside of the syntactic network. Cross-domain associations were more salient in more complex syntactic production.

Speech as a Biomarker for Depression

BACKGROUND

Depression is a debilitating disorder that at present lacks a reliable biomarker to aid in diagnosis and early detection. Recent advances in computational analytic approaches have opened up new avenues in developing such a biomarker by taking advantage of the wealth of information that can be extracted from a person's speech.

OBJECTIVE

The current review provides an overview of the latest findings in the rapidly evolving field of computational language analysis for the detection of depression. We cover a wide range of both acoustic and content-related linguistic features, data types (i.e., spoken and written language), and data sources (i.e., lab settings, social media, and smartphone-based). We put special focus on the current methodological advances with regard to feature extraction and computational modeling techniques. Furthermore, we pay attention to potential hurdles in the implementation of automatic speech analysis.

CONCLUSION

Depressive speech is characterized by several anomalies, such as lower speech rate, less pitch variability and more self-referential speech. With current computational modeling techniques, such features can be used to detect depression with an accuracy of up to 91%. The performance of the models is optimized when machine learning techniques are implemented that suit the type and amount of data. Recent studies now work towards further optimization and generalizability of the computational language models to detect depression. Finally, privacy and ethical issues are of paramount importance to be addressed when automatic speech analysis techniques are further implemented in, for example, smartphones. Altogether, computational speech analysis is well underway towards becoming an effective diagnostic aid for depression.

The Systemic Assessment Clinic, a Novel Method for Assessing Patients in General Adult Psychiatry: Presentation and Preliminary Service Evaluation

The traditional model of psychiatric assessment and diagnosis can be criticised as reductive. We developed an innovative model for psychiatric assessment of adult patients referred to our adult mental health team, the Systemic Assessment Clinic, incorporating the principles and techniques of systemic family therapy and dialogical practice into standard psychiatric assessment. We conducted a service evaluation, comparing prospective use of mental health services for patients assessed either in the Systemic Assessment Clinic or in standard assessment. Patients assessed in the Systemic Assessment Clinic had more favourable outcomes than those in standard assessment: they were significantly less likely to need multiple follow-up treatment appointments with a psychiatrist and to be re-referred to mental health services once discharged, indicating reduced healthcare costs. Satisfaction rates for participants attending the systemic assessment clinic were high. Our service evaluation gives preliminary evidence that the Systemic Assessment Clinic could be a potential new model for psychiatric assessment; further evaluation is warranted in a randomised controlled trial.