Objective: Although a few studies have quantified errors in the implantation of deep brain stimulation electrodes into the subthalamic nucleus (STN), a significant trend in error direction has not been reported. We have previously found that an error in axial plane, which is of most concern because it cannot be compensated for during deep brain stimulation programming, had a posteromedial trend. We hypothesized that this trend results from a predominance of a directionally oriented error factor of brain origin. Accordingly, elimination of nonbrain (technical) error factors could augment this trend. Thus, implantation accuracy could be improved by anterolateral compensation during target planning.
Methods: Surgical technique was revised to minimize technical error factors. During 22 implantations, targets were selected on axial magnetic resonance imaging scans up to 1.5 mm anterolateral from the STN center. Using fusion of postoperative computed tomographic and preoperative magnetic resonance imaging scans, implantation errors in the axial plane were obtained and compared with distances from the lead to the STN to evaluate the benefit of anterolateral compensation.
Results: Twenty errors and the mean error had a posteromedial direction. The average distances from the lead to the target and to the STN were 1.7 mm (range, 0.8-3.1 mm) and 1.1 mm (range, 0.1-1.9 mm), respectively. The difference between the 2 distances was significant (paired t test, P < 0.0001). The lower parts of the lead were consistently bent in the posteromedial direction on postoperative scout computed tomographic scans, suggesting that a brain-related factor is responsible for the reported error.
Conclusion: Elimination of the technical factors of error during STN deep brain stimulation implantation can result in a consistent posteromedial error. Implantation accuracy may be improved by compensation for this error in advance.
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http://dx.doi.org/10.1227/01.NEU.0000335171.38334.39 | DOI Listing |
Comput Struct Biotechnol J
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Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.
Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power.
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Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea. Electronic address:
A recently introduced quantitative susceptibility mapping (QSM) technique, χ-separation, offers the capability to separate paramagnetic (χ) and diamagnetic (χ) susceptibility distribution within the brain. In-vivo high-resolution mapping of iron and myelin distribution, estimated by χ-separation, could provide a deeper understanding of brain substructures, assisting the investigation of their functions and alterations. This can be achieved using 7T MRI, which benefits from a high signal-to-noise ratio and susceptibility effects.
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