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Brain Sciences
Special Issues
Relationships Between Disordered Sleep and Mental Health
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Relationships Between Disordered Sleep and Mental Health

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 916

Special Issue Editor

Dr. Anne M. Fink

E-Mail Website
Guest Editor
Department of Biobehavioral Nursing Science, University of Illinois Chicago, Chicago, IL 60612, USA
Interests: sleep neurobiology; insomnia; sleep apnea; occupational health

Special Issue Information

Dear Colleagues,

Sleep-related problems can significantly impair mental health, which can also affect the quality and characteristics of sleep. For example, adults and children who are diagnosed with psychiatric conditions commonly demonstrate sleep–wake disorders. In addition, circadian rhythm disruptions have also been shown to affect mental health, such as causing depressive symptoms in people with shift–work sleep disorder. There is therefore a need for scientific research regarding the biological and psychological mechanisms connecting disordered sleep and mental health outcomes. Novel discoveries in this field are thus critical in order to determine novel targets for interventions that could enhance the mental health of individuals and populations.

I invite you to submit your data-based articles, meta-analyses, or systematic reviews to this Special Issue: Relationships between Disordered Sleep and Mental Health. This Special Issue aims to present recent findings concerning sleep and mental health to provide new insights into the mechanisms, causes, outcomes, and possible treatments. The presented evidence may include a variety of study designs, such as preclinical work, clinical trials, and epidemiologic studies.

Dr. Anne M. Fink
Guest Editor

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

  • circadian rhythms
  • depression
  • genetics and genomics
  • rapid eye movement sleep behavior disorder
  • schizophrenia
  • sleep deprivation
  • shift–work sleep disorder
  • substance use disorder
  • suicide

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Published Papers (1 paper)

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Research

20 pages, 2333 KiB  
Article
Feature Contributions and Predictive Accuracy in Modeling Adolescent Daytime Sleepiness Using Machine Learning: The MeLiSA Study
by Mohammed A. Mamun, Jannatul Mawa Misti, Md Emran Hasan, Firoj Al-Mamun, Moneerah Mohammad ALmerab, Johurul Islam, Mohammad Muhit and David Gozal
Brain Sci. 2024, 14(10), 1015; https://doi.org/10.3390/brainsci14101015 - 12 Oct 2024
Viewed by 778
Abstract
Background: Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches to predict the risk of EDS. [...] Read more.
Background: Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches to predict the risk of EDS. Methods: A cross-sectional study was conducted among 1496 adolescents using a structured questionnaire. Data were collected through a two-stage stratified cluster sampling method. Chi-square tests and logistic regression analyses were performed using SPSS. Machine learning models, including Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM), were employed to identify and predict EDS risk factors using Python and Google Colab. Results: The prevalence of EDS in the cohort was 11.6%. SHAP values from the CatBoost model identified self-rated health status, gender, and depression as the most significant predictors of EDS. Among the models, GBM achieved the highest accuracy (90.15%) and precision (88.81%), while CatBoost had comparable accuracy (89.48%) and the lowest log loss (0.25). ROC-AUC analysis showed that CatBoost and GBM performed robustly in distinguishing between EDS and non-EDS cases, with AUC scores of 0.86. Both models demonstrated the superior predictive performance for EDS compared to others. Conclusions: The study emphasizes the role of health and demographic factors in predicting EDS among adolescents in Bangladesh. Machine learning techniques offer valuable insights into the relative contribution of these factors, and can guide targeted interventions. Future research should include longitudinal and interventional studies in diverse settings to improve generalizability and develop effective strategies for managing EDS among adolescents. Full article
(This article belongs to the Special Issue Relationships Between Disordered Sleep and Mental Health)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Circadian rhythm sleep disorders in young adult athletes: A review about risk factors, consequences, and interventions
Authors: Anne M. Fink, PhD FAHA,; Michele Kerulis, EdD LCPC CMPC,
Affiliation: University of Illinois Chicago Northwestern University

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