Molecular Neurobiology and Behavioral Mechanisms in 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 September 2026 | Viewed by 4290

Special Issue Editors


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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

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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,

Psychiatric disorders arise from complex interactions among molecular, cellular, circuit-level, and environmental processes that shape brain function and behavior. Despite the fact that significant advances have been made in neuroscience, the biological mechanisms underlying mental illness remain incompletely understood, limiting progress in diagnosis and targeted treatment. Integrating molecular neurobiology with systems-level and behavioral approaches is therefore critical for advancing both mechanistic insight and clinical translation.

Recent work has revealed the key roles that neurochemical signaling, neuromodulatory systems, and neuroimmune interactions play in regulating emotion, cognition, stress, motivation, and social behavior. Dysregulation of these processes is increasingly being linked to a broad range of psychiatric conditions, including mood and anxiety disorders, schizophrenia, addiction, and neurodevelopmental disorders. Converging evidence further suggests that alterations in neural circuit function and plasticity represent shared features across diagnostic categories.

Technological advances—including multi-omics, high-resolution neuroimaging, large-scale electrophysiology, and computational modeling—now enable the interrogation of molecular and circuit dynamics across multiple scales. These approaches, combined with refined animal models and translational human studies, are strengthening links between molecular mechanisms, circuit dysfunction, and behavioral outcomes. At the same time, growing interest in therapeutic targeting of molecular pathways, neuromodulatory interventions, and biomarker development highlights the need for a deeper mechanistic framework.

This Special Issue aims to collate interdisciplinary research addressing molecular neurobiology and the behavioral mechanisms underlying psychiatric disorders. We welcome the submission of original research articles, reviews, and perspectives spanning basic, translational, and clinical neuroscience, including studies of neurochemical and neuroimmune mechanisms, emotion and cognition, stress and addiction, neural circuit alterations and translational models, multi-omics and neuroimaging approaches, and emerging therapeutic and neuromodulatory strategies.

Dr. Chanpreet Singh
Guest Editor

Dr. Jin Xu
Guest Editor Assistant

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Keywords

  • molecular neurobiology
  • psychiatric disorders
  • neuromodulation
  • neural circuits
  • behavior and cognition
  • neurochemical signaling
  • neuroimmune interactions
  • stress and addiction
  • translational neuroscience
  • multi-omics and neuroimaging
  • biomarkers and therapeutics

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Published Papers (2 papers)

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Research

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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
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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
Cited by 1 | Viewed by 2830
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
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