Deep brain stimulation is used to alleviate symptoms of neurological and psychiatric disorders including Parkinson's disease, epilepsy, and obsessive-compulsive-disorder. Electrically stimulating limbic structures has been of great interest, and in particular, the region of the fornix. We conducted a systematic search for studies that reported clinical and preclinical outcomes of deep brain stimulation within the fornix up to July 2019. We identified 13 studies (7 clinical, 6 preclinical) that examined the effects of fornix stimulation in Alzheimer's disease (n = 9), traumatic brain injury (n = 2), Rett syndrome (n = 1), and temporal lobe epilepsy (n = 1). Overall, fornix stimulation can lead to decreased rates of cognitive decline (in humans), enhanced memory (in humans and animals), visuo-spatial memorization (in humans and animals), and improving verbal recollection (in humans). While the exact mechanisms of action are not completely understood, studies suggest fornix DBS to be involved with increased functional connectivity and neurotransmitter levels, as well as enhanced neuroplasticity.
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http://dx.doi.org/10.1007/s00018-020-03456-4 | 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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