Automated Atlas Fitting for Deep Brain Stimulation Surgery Based on Microelectrode Neuronal Recordings

被引:3
|
作者
Bakstein, Eduard [1 ,2 ]
Sieger, Tomas [1 ,3 ,4 ]
Novak, Daniel [1 ]
Ruzicka, Filip [3 ,4 ]
Jech, Robert [3 ,4 ]
机构
[1] Czech Tech Univ, Dept Cybernet, Fac Elect Engn, Prague, Czech Republic
[2] Natl Inst Mental Hlth, Klecany, Czech Republic
[3] Charles Univ Prague, Fac Med 1, Ctr Clin Neurosci, Dept Neurol, Prague, Czech Republic
[4] Gen Univ Hosp, Prague, Czech Republic
关键词
Deep brain stimulation; Anatomical atlas fitting; Microelectrode recordings; SUBTHALAMIC NUCLEUS;
D O I
10.1007/978-981-10-9023-3_19
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Introduction: The deep brain stimulation (DBS) is a treatment technique for late-stage Parkinson's disease (PD), based on chronic electrical stimulation of neural tissue through implanted electrodes. To achieve high level of symptom suppression with low side effects, precise electrode placement is necessary, although difficult due to small size of the target nucleus and various sources of inaccuracy, especially brain shift and electrode bending. To increase accuracy of electrode placement, electrophysiological recording using several parallel microelectrodes (MER) is used intraoperatively in most centers. Location of the target nucleus is identified from manual expert evaluation of characteristic neuronal activity. Existing studies have presented several models to classify individual recordings or trajectories automatically. In this study, we extend this approach by fitting a 3D anatomical atlas to the recorded electrophysiological activity, thus adding topological information. Methods: We developed a probabilistic model of neuronal activity in the vicinity the subthalamic nucleus (STN), based on normalized signal energy. The model is used to find a maximum-likelihood transformation of an anatomical surface-based atlas to the recorded activity. The resulting atlas fit is compared to atlas position estimated from pre-operative MRI scans. Accuracy of STN classification is then evaluated in a leave-one-subject-out scenario using expert MER annotation. Results: In an evaluation on a set of 27 multi-electrode trajectories from 15 PD patients, the proposed method showed higher accuracy in STN-nonSTN classification (88.1%) compared to the reference methods (78.7%) with an even more pronounced advantage in sensitivity (69.0% vs 44.6%). Conclusion: The proposed method allows electrophysiology-based refinement of atlas position of the STN and represents a promising direction in refining accuracy of MER localization in clinical DBS setting, as well as in research of DBS mechanisms.
引用
收藏
页码:105 / 111
页数:7
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