Thalamocortical dysrhythmia in patients with schizophrenia spectrum disorder and individuals at clinical high risk for psychosis

Author(s):  
Minah Kim ◽  
Tak Hyung Lee ◽  
Hyungyou Park ◽  
Sun-Young Moon ◽  
Silvia Kyungjin Lho ◽  
...  
2018 ◽  
Vol 26 (1-2) ◽  
pp. 14-20 ◽  
Author(s):  
Olga Santesteban-Echarri ◽  
Danijela Piskulic ◽  
Rowen K Nyman ◽  
Jean Addington

Background Despite its increased use in mental health, both health care provision by telehealth and research are in the early stages. Videoconferencing, a telehealth subfield, has been mainly used for the medication management and delivery of psychological treatments for mood, adjustment and anxiety disorders, and to a lesser extent for psychotic disorders. Objectives The focus of this scoping review is on studies using videoconferencing for intervention for individuals with a diagnosis of schizophrenia-spectrum disorder and those who may be considered to be in the very early stages of psychosis (clinical high risk). The aim of this review is to assess the feasibility, acceptability and clinical benefits of videoconferencing interventions and compare them with face-to-face interventions for this population. Methods A scoping review of peer-reviewed original research on the use of videoconferencing for intervention purposes in individuals with a schizophrenia-spectrum disorder or at clinical high risk. Results Out of 13,750 citations, 60 articles were retrieved for detailed evaluation, resulting in 14 eligible studies ( N = 439 individuals). There was no study reporting on videoconferencing interventions for individuals at clinical high risk. All the studies reported that videoconferencing implementation was feasible, and most of them described high acceptance by individuals with a schizophrenia-spectrum disorder. However, selection bias of studies was high, and overall methodological quality was poor. Conclusion Videoconferencing interventions seem feasible for participants with schizophrenia-spectrum disorder who showed high acceptance of this intervention modality.


2012 ◽  
Vol 139 (1-3) ◽  
pp. 129-135 ◽  
Author(s):  
Shana Golembo-Smith ◽  
Jason Schiffman ◽  
Emily Kline ◽  
Holger J. Sørensen ◽  
Erik L. Mortensen ◽  
...  

2013 ◽  
Vol 151 (1-3) ◽  
pp. 270-273 ◽  
Author(s):  
Thomas Tsuji ◽  
Emily Kline ◽  
Holger J. Sorensen ◽  
Erik L. Mortensen ◽  
Niels M. Michelsen ◽  
...  

2021 ◽  
pp. 1-11
Author(s):  
J. N. de Boer ◽  
A. E. Voppel ◽  
S. G. Brederoo ◽  
H. G. Schnack ◽  
K. P. Truong ◽  
...  

Abstract 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.


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