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

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Background Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. Methods Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. Results The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. Conclusions Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples

Nucleobindin Co-Localizes and Associates with Cyclooxygenase (COX)-2 in Human Neutrophils

The inducible cyclooxygenase isoform (COX-2) is associated with inflammation, tumorigenesis, as well as with physiological events. Despite efforts deployed in order to understand the biology of this multi-faceted enzyme, much remains to be understood. Nucleobindin (Nuc), a ubiquitous Ca2+-binding protein, possesses a putative COX-binding domain. In this study, we investigated its expression and subcellular localization in human neutrophils, its affinity for COX-2 as well as its possible impact on PGE2 biosynthesis. Complementary subcellular localization approaches including nitrogen cavitation coupled to Percoll fractionation, immunofluorescence, confocal and electron microscopy collectively placed Nuc, COX-2, and all of the main enzymes involved in prostanoid synthesis, in the Golgi apparatus and endoplasmic reticulum of human neutrophils. Immunoprecipitation experiments indicated a high affinity between Nuc and COX-2. Addition of human recombinant (hr) Nuc to purified hrCOX-2 dose-dependently caused an increase in PGE2 biosynthesis in response to arachidonic acid. Co-incubation of Nuc with COX-2-expressing neutrophil lysates also increased their capacity to produce PGE2. Moreover, neutrophil transfection with hrNuc specifically enhanced PGE2 biosynthesis. Together, these results identify a COX-2-associated protein which may have an impact in prostanoid biosynthesis

Depletion of mitochondrial DNA in the liver of a patient with lactic acidemia and hypoketotic hypoglycemia

An infant with feeding difficulties, hypotonia, lactic acidemia, and severe hypoketotic hypoglycemia died at the age of 7 months of liver disease. Electron microscopy revealed abnormal mitochondria. Biochemical studies of mitochondrial enzymes in liver showed a decreased activity of complexes I, III, and IV. Mitochondrial DNA (mtDNA) content was reduced in liver 7% of the mean value in control subjects) and in muscle (50%). In kidney, brain, and heart, the mtDNA content was normal. The liver-specific mtDNA depletion syndrome in this patient manifested itself with features of both a respiratory chain defect and a mitochondrial fatty acid oxidation defect. Syndromes involving depletion of mtDNA can be diagnosed only when the activity of the respiratory chain enzymes and the content of mtDNA are investigated in the most affected tissue

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

BACKGROUND: Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. METHODS: Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. RESULTS: The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. CONCLUSIONS: Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples