Deep brain stimulation (DBS) therapy relies on electrical stimulation of neuronal elements in small brain targets. However, the lack of fine spatial control over field distributions in current systems implies that stimulation easily spreads into adjacent structures that may induce adverse side-effects. This study investigates DBS field steering using a novel DBS lead design carrying a high-resolution electrode array. We apply computational models to simulate voltage distributions and DBS activation volumes in order to theoretically assess the potential of field steering in DBS. Our computational analysis demonstrates that the DBS-array is capable of accurately displacing activation volumes with sub-millimeter precision. Our findings demonstrate that future systems for DBS therapy may provide for more accurate target coverage than currently available systems achieve.
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http://dx.doi.org/10.1109/IEMBS.2010.5626472 | DOI Listing |
J Neurosurg
January 2025
1Department of Neurosurgery, St. Olav's University Hospital, Trondheim, Norway.
Objective: The extent of resection (EOR) and postoperative residual tumor (RT) volume are prognostic factors in glioblastoma. Calculations of EOR and RT rely on accurate tumor segmentations. Raidionics is an open-access software that enables automatic segmentation of preoperative and early postoperative glioblastoma using pretrained deep learning models.
View Article and Find Full Text PDFAlzheimers Dement (N Y)
January 2025
Indiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis Indiana USA.
Introduction: The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.
View Article and Find Full Text PDFAlzheimers Dement
January 2025
Department of Internal Medicine-Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Extracellular vesicles (EVs) have emerged as novel blood-based biomarkers for various pathologies. The development of methods to enrich cell-specific EVs from biofluids has enabled us to monitor difficult-to-access organs, such as the brain, in real time without disrupting their function, thus serving as liquid biopsy. Burgeoning evidence indicates that the contents of neuron-derived EVs (NDEs) in blood reveal dynamic alterations that occur during neurodegenerative pathogenesis, including Alzheimer's disease (AD), reflecting a disease-specific molecular signature.
View Article and Find Full Text PDFNeuroinformatics
January 2025
Department of CSE, Chandigarh Group of Colleges, Landran, Mohali, India.
The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes and generate inaccurate results since they are executed by human beings and cause delays in treatment that can be fatal. Considering these and other shortcomings there exists a need to have more efficient and accurate solutions based on artificial intelligence.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
The field of medical image segmentation powered by deep learning has recently received substantial attention, with a significant focus on developing novel architectures and designing effective loss functions. Traditional loss functions, such as Dice loss and Cross-Entropy loss, predominantly rely on global metrics to compare predictions with labels. However, these global measures often struggle to address challenges such as occlusion and nonuni-form intensity.
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