Global trends of machine learning applications in psychiatric research over 30 years: A bibliometric analysis

COVID-19 and Sleep Problems: A Perspective from Bibliometric Analysis

OBJECTIVES

The Coronavirus Disease 2019 (COVID-19) pandemic and the containment measures for COVID-19 have affected sleep quality in the population. This study explored sleep-related research from a bibliometric perspective to provide an overview of the research outputs in this field.

METHODS

Original and review articles were retrieved from the Web of Science Core Collection (WOSCC) database from December 2019 to 7 Aug 2023. R package "bibliometrix" was used to summarize the number of articles of authors, institutions, and countries; count the citations of the articles, and generate a Three-Fields Plot. VOSviewer software was applied to visualize the collaboration network among authors and institutions, and to conduct a co-occurrence analysis of keywords.

RESULTS

A total of 4,499 articles on COVID-19 and sleep, and 25,883 articles on non-COVID-19 and sleep were included. Sleep related articles were mainly published by authors from China, the USA, and Italy. For COVID-19 and sleep research, Huazhong University of Science was the most productive institution. The Psychiatry Research was the most influential journal across the different subject categories of this field. "Mental health", "anxiety", and "depression" were the most common keywords, while "sleep quality" and "quality of life" were the likely topic areas in terms of future research directions.

CONCLUSIONS

Our findings provide a comprehensive perspective for researchers to understand the wider landscape of both COVID-19 and non-COVID-19 sleep-related research area.

Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning

The present study aims to identify suicide risks in major depressive disorders (MDD) patients from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI data of 288 MDD patients, including 110 patients with suicide ideation (SI), 93 patients with suicide attempts (SA), and 85 patients without suicidal ideation or attempts (NS). And we developed interpretable deep neural network models to classify patients in three tasks including SA-versus-SI, SA-versus-NS, and SI-versus-NS, respectively. Furthermore, we interpreted the models by extracting the important features that contributed most to the classification, and further discussed these features or ROI/brain regions.

The State of the Catatonia Literature: Employing Bibliometric Analysis of Articles From 1965-2020 to Identify Current Research Gaps

BACKGROUND

Since Kahlbaum's classic 19th-century description of catatonia, our conceptualization of this syndrome, as well treatment options for it, has advanced considerably. However, little is known about the current state of the catatonia literature since a comprehensive bibliometric analysis of it has not yet been undertaken.

OBJECTIVE

The purpose of this study was to conduct a bibliometric analysis, along with a content analysis of articles reporting new findings, to better understand the catatonia literature and how catatonia research is changing.

METHODS

Using the search term "Title(catatoni∗)" in Web of Science Core Collection for all available years (1965-2020), all available publications (articles, proceeding papers, reviews) pertaining directly to catatonia were identified, and metadata extracted. Semantic and coauthorship network analyses were conducted. A content analysis was also conducted on all available case reports, case series, and research articles written in English.

RESULTS

A total of 1015 articles were identified representing 2861 authors, 346 journals, and 15,639 references. The average number of publications per year over the last 20 years (31.3) more than doubled in comparison to that in the 20 years prior (12.8). The top 3 most common journals were Psychosomatics/Journal of the Academy of Consultation-Liaison Psychiatry, Journal of ECT, and Schizophrenia Research, which represented 12.6% of all publications. Content analysis revealed that catatonia articles are increasingly published in nonpsychiatric journals. There was a notable paucity of clinical trials throughout the study period. Since 2003, articles on catatonia secondary to a general medical condition, as well as articles including child/adolescent patients and patients with autism spectrum disorder or intellectual disability, have made up increasing shares of the literature, with a smaller proportion of articles reporting periodic or recurrent catatonia. We noted a decrease in the proportion of articles detailing animal/in vitro studies, genetic/heredity studies, and clinical trials, along with stagnation in the proportion of neuroimaging studies.

CONCLUSIONS

The catatonia literature is growing through contributions from authors and institutions across multiple countries. However, recent growth has largely been driven by increased case reports, with significant downturns observed in both clinical and basic science research articles. A dearth of clinical trials evaluating potential treatments remain a critical gap in the catatonia literature.