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Brain Sciences
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Neuropeptides, Behavior and Psychiatric Disorders
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Neuropeptides, Behavior and Psychiatric Disorders

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

Deadline for manuscript submissions: 15 March 2026 | Viewed by 2779

Special Issue Editors

Dr. Chanpreet Singh

E-Mail Website
Guest Editor
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
Interests: neuropsychiatric disorders; autism spectrum disorders; sleep disorders; zebrafish; learning and memory; Alzheimer’s disease; therapeutics
Special Issues, Collections and Topics in MDPI journals
Dr. Jin Xu

E-Mail Website
Guest Editor Assistant
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
Interests: developmental biology; neuropsychiatric disorders

Special Issue Information

Dear Colleagues,

Neuropeptides play a pivotal role in modulating brain function, behavior, and the pathophysiology of psychiatric disorders. Since their discovery, neuropeptides come to be understood as the key neuromodulators influencing emotion, stress response, cognition, and social behavior. Dysregulation in neuropeptide signaling pathways has been increasingly implicated in conditions such as depression, anxiety, schizophrenia, and autism spectrum disorders.

This Special Issue aims to present interdisciplinary research that is at the intersection of neuropeptidergic systems, behavioral neuroscience, and psychiatry. We invite original research articles, reviews, and perspectives that explore the molecular, cellular, and circuit-level mechanisms of neuropeptides and how they affect behavior and mental health.

We especially welcome cutting-edge studies that use advanced techniques such as optogenetics, chemogenetics, imaging, omics, and computational modeling to elucidate the function of neuropeptides in animal models and humans. Translational and clinical studies that provide new insights into their diagnostic, therapeutic, or biomarker potential are highly encouraged.

Dr. Chanpreet Singh
Guest Editor

Dr. Jin Xu
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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

  • neuropeptides
  • psychiatric disorders
  • behavioral neuroscience
  • neuromodulation
  • stress and emotion
  • neurocircuitry
  • translational psychiatry
  • biomarkers
  • optogenetics
  • peptidergic signaling

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Published Papers (2 papers)

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18 pages, 2188 KB  
Article
Neuropeptides, Altruism, and Adverse Childhood Experiences: Investigating Biological and Behavioral Correlations in Medical Students
by Jennifer Khong, Lauren Bennett, Johanna Felix Rivera, Nathan Andrews, Veronica Vuong, Demi Zapata, Phillip Khong and Rebecca Ryznar
Brain Sci. 2025, 15(10), 1128; https://doi.org/10.3390/brainsci15101128 - 21 Oct 2025
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Abstract
Background/Objectives: This pilot study aimed to investigate the relationship between salivary neuropeptides levels, adverse childhood experiences (ACEs), and altruism in a sample of medical students. Additionally, the study examined potential sex differences in these relationships. Methods: Sixty medical students (36.6% men, [...] Read more.
Background/Objectives: This pilot study aimed to investigate the relationship between salivary neuropeptides levels, adverse childhood experiences (ACEs), and altruism in a sample of medical students. Additionally, the study examined potential sex differences in these relationships. Methods: Sixty medical students (36.6% men, 63.3% women) provided saliva samples to measure oxytocin, α-MSH, β-endorphin, neurotensin, and substance P using a custom 5-plex human peptide assay. Participants completed the ACE Survey and Compassionate Love Scale for Humanity (CLS-H) Altruism Survey. Descriptive statistics characterized demographics and survey data, with out-of-range values adjusted to the standard curve maximum. Data normality was assessed with the Jarque–Bera test; due to non-normality, values were log-transformed. Differences between male and female salivary, ACE score, and CLS-H altruism score were tested using t-tests and Mann–Whitney U-tests, while correlations were evaluated with Pearson and Spearman coefficients. Results: The five neuropeptides, while highly correlated with each other, did not exhibit significant relationships with altruism, as measured by the CLS-H Altruism Survey. Finally, female participants demonstrated greater altruistic tendencies compared with male participants with marginal significance. Conclusions: While there were no significant relationships between the fives neuropeptides, ACEs, or altruism; women demonstrated higher levels of altruism compared with men. The data reported in this pilot study did not strongly support the conclusion that neuropeptides influence social behavior and trauma response. Furthermore, future studies with larger, more diverse samples and multiple time point measurements of neuropeptides could be beneficial to better understand the relationships between neuropeptides and any potential implications for mental health interventions. Full article
(This article belongs to the Special Issue Neuropeptides, Behavior and Psychiatric Disorders)
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24 pages, 1408 KB  
Systematic Review
Fear Detection Using Electroencephalogram and Artificial Intelligence: A Systematic Review
by Bladimir Serna, Ricardo Salazar, Gustavo A. Alonso-Silverio, Rosario Baltazar, Elías Ventura-Molina and Antonio Alarcón-Paredes
Brain Sci. 2025, 15(8), 815; https://doi.org/10.3390/brainsci15080815 - 29 Jul 2025
Viewed by 1995
Abstract
Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting [...] Read more.
Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string (“fear detection” AND “artificial intelligence” OR “machine learning” AND NOT “fnirs OR mri OR ct OR pet OR image”). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing. Full article
(This article belongs to the Special Issue Neuropeptides, Behavior and Psychiatric Disorders)
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