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Brain Sciences
Special Issues
Clinical and Biological Characterization of Psychiatric Disorders
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Clinical and Biological Characterization of Psychiatric Disorders

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Psychiatric Diseases".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1369

Special Issue Editors

Dr. Kang Sim

E-Mail Website
Guest Editor
Institute of Mental Health, Singapore, Singapore
Interests: clinical characterization of major psychiatric disorders; neural basis of major psychiatric disorders; medical education
Dr. Takahiro Kato

E-Mail Website
Guest Editor
Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
Interests: depression; suicide; biological psychiatry; neuroinflammation; microglia; oxidative stress; metabolomics; biomarker; psychoimmunology; neuropsychoanalysis; social and cultural psychiatry
Special Issues, Collections and Topics in MDPI journals
Dr. Seon-Cheol Park

E-Mail Website
Guest Editor
Department of Psychiatry, Hanyang University College of Medicine, Seoul, Republic of Korea
Interests: depression; psychopathology; network analysis; machine learning; biological psychiatry; psychopharmacology; philosophy of psychiatry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Psychiatric disorders such as psychotic, affective, and anxiety disorders are complex neuro-behavioural conditions that can have a myriad of clinical manifestations and are influenced by multifaceted biological and psychosocial factors in terms of aetiology. We need better clinical and biological characterisation of these conditions in order to improve our understanding of the nature of these conditions and underlying causative mechanisms. Some ways to profile the condition can be through illness subtyping such as symptom clusters (e.g., positive, negative, and disorganised syndromes in schizophrenia, marked fears and avoidance in anxiety conditions), symptom course (e.g., predominant polarity in bipolar disorder), illness course (e.g., remission, relapse status), treatment patterns (e.g., psychotropic agent prescriptions), and response (e.g., inadequate response, treatment resistance), patterns of comorbidities and crossover diagnostic categories. These illness subtypes can then be further examined with other clinical correlates, cognitive functioning, and biological substrates such as neuroimaging measures of brain cortical and subcortical volumes, white matter architecture, cerebral function, putative genetic factors, physiological measures, inflammatory markers, etc. Such characterisations of psychiatric disorders and associated factors can potentially allow for the clarification of neural basis of these conditions and also highlight the clinical and biological predictors of illness course and outcome.

We greatly encourage the submission of relevant original articles based on empirical studies as well as review articles related to this theme to this Special Issue.

Dr. Kang Sim
Dr. Takahiro Kato
Dr. Seon-Cheol Park
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clinical
  • biological
  • subtyping
  • psychiatric disorders
  • neural basis

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Published Papers (2 papers)

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Review

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20 pages, 805 KiB  
Review
Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review
by Jing Ling Tay, Kyawt Kyawt Htun and Kang Sim
Brain Sci. 2024, 14(9), 878; https://doi.org/10.3390/brainsci14090878 - 29 Aug 2024
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Abstract
Background: Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment [...] Read more.
Background: Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment strategies in a timely manner. Objective: In this scoping review, we aimed to examine the accuracy of the use of artificial intelligence (AI) methods in predicting the clinical outcomes of patients with psychotic disorders as well as determine the relevant predictors of these outcomes. Methods: This review was guided by the PRISMA Guidelines for Scoping Reviews. Seven electronic databases were searched for relevant published articles in English until 1 February 2024. Results: Thirty articles were included in this review. These studies were mainly conducted in the West (63%) and Asia (37%) and published within the last 5 years (83.3%). The clinical outcomes included symptomatic improvements, illness course, and social functioning. The machine learning models utilized data from various sources including clinical, cognitive, and biological variables such as genetic, neuroimaging measures. In terms of main machine learning models used, the most common approaches were support vector machine, random forest, logistic regression, and linear regression models. No specific machine learning approach outperformed the other approaches consistently across the studies, and an overall range of predictive accuracy was observed with an AUC from 0.58 to 0.95. Specific predictors of clinical outcomes included demographic characteristics (gender, socioeconomic status, accommodation, education, and employment); social factors (activity level and interpersonal relationships); illness features (number of relapses, duration of relapses, hospitalization rates, cognitive impairments, and negative and disorganization symptoms); treatment (prescription of first-generation antipsychotics, high antipsychotic doses, clozapine, use of electroconvulsive therapy, and presence of metabolic syndrome); and structural and functional neuroimaging abnormalities, especially involving the temporal and frontal brain regions. Conclusions: The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders. Full article
(This article belongs to the Special Issue Clinical and Biological Characterization of Psychiatric Disorders)
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7 pages, 262 KiB  
Brief Report
Neurological Damage Measured by S-100b and Neuron-Specific Enolase in Patients Treated with Electroconvulsive Therapy
by Ángel A. Ruiz-Chow, Carlos J. López-Cruz, Daniel Crail-Meléndez, Jesús Ramírez-Bermúdez, José Santos-Zambrano and Laura A. Luz-Escamilla
Brain Sci. 2024, 14(8), 822; https://doi.org/10.3390/brainsci14080822 - 16 Aug 2024
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Abstract
Electroconvulsive therapy (ECT) is considered one of the most effective treatments for psychiatric disorders. ECT has proven effective in the treatment of depression, mania, catatonia and psychosis. It is presumed that seizures induced during ECT administration cause toxicity and potentially neuronal and glial [...] Read more.
Electroconvulsive therapy (ECT) is considered one of the most effective treatments for psychiatric disorders. ECT has proven effective in the treatment of depression, mania, catatonia and psychosis. It is presumed that seizures induced during ECT administration cause toxicity and potentially neuronal and glial cell death. A broad range of neurological disorders increase cerebrospinal fluid and serum levels of neuron-specific enolase (NSE) and S-100b protein. This study aims to investigate the effect of ECT on NSE and S-100b levels, which, together, serve as a proxy for neuronal cell damage. Serum concentrations of S-100b and NSE of adult patients who received ECT were measured by immunoluminometric analysis before and after treatment. A two-way ANOVA test was used to estimate the statistical differences in marker concentrations between the subgroups of the study population. Results: A total of 55 patients were included in the analysis: 52.73% (n = 29) were diagnosed with depression, 21.82% (n = 12) with schizophrenia or other psychosis, 16.36% (n = 9) with mania and 9.09% (n = 5) with catatonia. There were no statistically significant changes in NSE (p = 0.288) and S-100b (p = 0.243) levels. We found no evidence that ECT induced neuronal damage based on NSE and S-100b protein levels measured in the serum of patients before and after treatment. Full article
(This article belongs to the Special Issue Clinical and Biological Characterization of Psychiatric Disorders)
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