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Overview: Post traumatic stress disorder (PTSD) has emerged as a severely debilitating psychiatric disorder associated with critical illness. Little progress has been made in the treatment of post-intensive care unit (ICU) PTSD.
Aim: To synthesize neurobiological evidence on the pathophysiology of PTSD and the brain areas involved, and to highlight the potential of music to treat post-ICU PTSD.
Methods: Critical narrative review to elucidate an evidence-based neurobiological framework to inform the study of music interventions for PTSD post-ICU. Literature searches were performed in PubMed and CINAHL. The Scale for the Assessment of Narrative Review Articles (SANRA) guided reporting.
Results: A dysfunctional HPA axis feedback loop, an increased amygdalic response, hippocampal atrophy, and a hypoactive prefrontal cortex contribute to PTSD symptoms. Playing or listening to music can stimulate neurogenesis and neuroplasticity, enhance brain recovery, and normalize stress response. Additionally, evidence supports effectiveness of music to improve coping and emotional regulation, decrease dissociation symptoms, reduce depression and anxiety levels, and overall reduce severity of PTSD symptoms.
Conclusions: Despite the lack of music interventions for ICU survivors, music has the potential to help people suffering from PTSD by decreasing amygdala activity, improving hippocampal and prefrontal brain function, and balancing the HPA-axis.
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http://dx.doi.org/10.3390/ijerph19053113 | DOI Listing |
Hum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFPrev Med Rep
October 2025
Guangxi Orthopedic Hospital, Nanning 530012, China.
Objective: Negative emotions during adolescence constitute a significant public health challenge requiring theoretically-grounded intervention approaches. This investigation examined sequential mediation mechanisms whereby physical exercise influences adolescent negative emotions through psychological benefits and social self-efficacy pathways, integrating neurobiological and social-cognitive theoretical frameworks.
Methods: Cross-sectional analysis of 1471 Chinese adolescents (Mean age = 13.
Front Neurol
August 2025
Unit of Child Neurology and Psychiatry, Department of Systems Medicine, Tor Vergata University of Rome, Rome, Italy.
Introduction: Restless Legs Syndrome (RLS), known as Willis-Ekbom disease, is a common neurological condition that often goes undiagnosed, especially in children. Characterized by an irresistible urge to move the legs, it is typically more pronounced in the evening and at rest. Growing Pains (GP), common in childhood and associated with migraine, present apparently overlapping symptoms with RLS, making it sometimes difficult to distinguish between the two.
View Article and Find Full Text PDFPsychiatry Res Neuroimaging
August 2025
Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey. Electronic address:
Schizophrenia is a heterogeneous disorder with significant variability in neurobiological and clinical presentations. In this study, we aimed to investigate neuroanatomical subtypes of schizophrenia using a data-driven machine-learning algorithm. Structural MRI data from 222 participants (136 schizophrenia patients and 86 healthy controls) were analyzed.
View Article and Find Full Text PDFFamily history is one the most powerful risk factor for attention-deficit/hyperactivity disorder (ADHD), yet no study has tested whether multimodal Magnetic Resonance Imaging (MRI) combined with deep learning can separate familial ADHD (ADHD-F) and non-familial ADHD (ADHD-NF). T1-weighted and diffusion-weighted MRI data from 438 children (129 ADHD-F, 159 ADHD-NF, and 150 controls) were parcellated into 425 cortical and white-matter metrics. Our pipeline combined three feature-selection steps (t-test filtering, mutual-information ranking, and Lasso) with an auto-encoder and applied the binary-hypothesis strategy throughout; each held-out subject was assigned both possible labels in turn and evaluated under leave-one-out testing nested within five-fold cross-validation.
View Article and Find Full Text PDF