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Objective: Deep brain stimulation (DBS) alleviates symptoms associated with some neurological disorders by stimulating specific deep brain targets. However, incomplete stimulation of the target region can provide suboptimal therapy, and spread of stimulation to tissue outside the target can generate side-effects. Existing DBS electrodes generate stimulation profiles that are roughly spherical, neither matching nor enabling the mapping of therapeutic targets. We present a novel electrode design and will perform computational modeling of the new design to investigate its use as a next generation DBS electrode.
Materials And Methods: Computational simulations of a finite element model are performed for both the novel electrode and for a commercially available DBS electrode.
Results: Computational modeling results show that this new electrode design is able to steer stimulation radially around the device, creating voltage distributions that may more closely match deep brain targets.
Conclusion: The ability to better match the anatomy and compensate for targeting errors during implantation will enable strict localization of the generated stimulation fields to within target tissues, potentially providing more complete symptom alleviation while reducing the occurrence of side-effects.
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http://dx.doi.org/10.1111/ner.12330 | DOI Listing |
Phys Rev Lett
August 2025
Northeastern University, Department of Physics, Center for Theoretical Biological Physics, Boston, Massachusetts 02115, USA.
Sparse connectivity is a hallmark of the brain and a desired property of artificial neural networks. It promotes energy efficiency, simplifies training, and enhances the robustness of network function. Thus, a detailed understanding of how to achieve sparsity without jeopardizing network performance is beneficial for neuroscience, deep learning, and neuromorphic computing applications.
View Article and Find Full Text PDFSci Adv
September 2025
Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Subthalamic deep brain stimulation (STN-DBS) provides unprecedented spatiotemporal precision for the treatment of Parkinson's disease (PD), allowing for direct real-time state-specific adjustments. Inspired by findings from optogenetic stimulation in mice, we hypothesized that STN-DBS can mimic dopaminergic reinforcement of ongoing movement kinematics during stimulation. To investigate this hypothesis, we delivered DBS bursts during particularly fast and slow movements in 24 patients with PD.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Epilepsy, a highly individualized neurological disorder, affects millions globally. Electroencephalography (EEG) remains the cornerstone for seizure diagnosis, yet manual interpretation is labor-intensive and often unreliable due to the complexity of multi-channel, high-dimensional data. Traditional machine learning models often struggle with overfitting and fail in fully capturing the highdimensional, temporal dynamics of EEG signals, restricting their clinical utility.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
View Article and Find Full Text PDFExp Brain Res
September 2025
School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color differences, defined in the CIELAB color space with constant luminance and chroma, are investigated in this study. Analysis of Event-Related Potentials (ERPs) revealed a significant decrease in the amplitude of the P300 component as binocular color differences increased, suggesting a measurable brain response to these differences.
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