Deep brain stimulation is a method that involves using an electric stimulus on a specific target in the brain with stereotaxis. It is a minimally invasive, safe, adjustable and reversible nerve involvement technology. At present, this technique is widely applied to treat movement disorders and has produced promising effects on mental symptoms, including combined anxiety and depression. Deep brain stimulation has therefore been employed as a novel treatment for depression, obsessive-compulsive disorder, habituation, Tourette's syndrome, presenile dementia, anorexia nervosa and other refractory mental illnesses. Many encouraging results have been reported. The aim of the present review was to briefly describe the mechanisms, target selection, side effects, ethical arguments and risks associated with deep brain stimulation. Although deep brain stimulation is a developing and promising treatment, a large amount of research is still required to determine its curative effect, and the selection of patients and targets must be subjected to strict ethical standards.
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http://dx.doi.org/10.3892/etm.2017.5366 | DOI Listing |
Heliyon
January 2025
BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks.
View Article and Find Full Text PDFHeliyon
July 2024
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu India.
Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment.
View Article and Find Full Text PDFClin Neuropsychol
January 2025
Department of Internal Medicine (Pulmonary, Critical Care, and Sleep Medicine Division), University of South Florida, Tampa, FL, USA.
Obstructive sleep apnea (OSA) has been associated with structural and functional brain changes and cognitive impairment in sleep clinic samples. Persons with traumatic brain injury (TBI) are at increased risk of OSA compared to community samples, and many experience chronic cognitive disability. However, the impact of OSA on cognitive outcome after TBI is unknown.
View Article and Find Full Text PDFJ Neuroimaging
January 2025
Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA.
Background And Purpose: MRI is crucial for multiple sclerosis (MS), but the relative value of portable ultra-low field MRI (pULF-MRI), a technology that holds promise for extending access to MRI, is unknown. We assessed white matter lesion (WML) detection on pULF-MRI compared to high-field MRI (HF-MRI), focusing on blinded assessments, assessor self-training, and multiplanar acquisitions.
Methods: Fifty-five adults with MS underwent pULF-MRI following their HF-MRI.
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