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Amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes

  • Research Paper
  • Special Focus on Brain Machine Interfaces and Applications
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Abstract

Parkinson’s disease (PD) is treated effectively by deep brain stimulation (DBS) of the subthalamic nucleus (STN), using an electrode inserted into the head of a PD patient. The electrode has multiple electrical contacts along its length, so the best may be chosen for selectively stimulating the STN. Neurosurgeons usually determine the optimal stimulated contact via the clinical experience of the neurosurgeon and the motor improvement of PD patients. This is a time-consuming and labor-intensive trial-and-error process. The selection of optimal stimulated contact highly depends on the locations of sweet spots, which are manually identified by the characteristic features of microelectrode recordings (MERs). This paper presents an amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes. The method first obtains the amplitude-frequency fusion features by combining the MERs time sequence features and the amplitude sequence features, and then uses the convolutional neural network (CNN) with convolutional block attention module (CBAM) to identify both the border of the STN and the sweet spots to implant the electrode. The optimal stimulated contact can be selected according to the distribution of the sweet spots. Experimental results indicate that, for successful surgeries, neurosurgeons and the proposed AI solution selected the same optimal contacts. Furthermore, the proposed method outperforms the state-of-the-art methods for STN and sweet spot identification. The proposed method shows great potential for optimal contact selection to improve the efficiency of STN-DBS surgery and reduce the dependence on clinicians’ experience.

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References

  1. Dorsey E R, Elbaz A, Nichols E, et al. Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol, 2018, 17: 939–953

    Article  Google Scholar 

  2. Mann J M, Foote K D, Garvan C W, et al. Brain penetration effects of microelectrodes and DBS leads in STN or GPi. J Neurol Neurosurg Psychiatry, 2009, 80: 794–798

    Article  Google Scholar 

  3. Schuepbach W M M, Rau J, Knudsen K, et al. Neurostimulation for Parkinson’s disease with early motor complications. N Engl J Med, 2013, 368: 610–622

    Article  Google Scholar 

  4. Boutet A, Madhavan R, Elias G J B, et al. Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning. Nat Commun, 2021, 12: 1–13

    Article  Google Scholar 

  5. Zhang F, Wang F, Li W G, et al. Relationship between electrode position of deep brain stimulation and motor symptoms of Parkinson’s disease. BMC Neurol, 2021, 21: 122

    Article  Google Scholar 

  6. Krauss J K, Lipsman N, Aziz T, et al. Technology of deep brain stimulation: current status and future directions. Nat Rev Neurol, 2021, 17: 75–87

    Article  Google Scholar 

  7. Steigerwald F, Matthies C, Volkmann J. Directional deep brain stimulation. Neurotherapeutics, 2019, 16: 100–104

    Article  Google Scholar 

  8. Lambert C, Zrinzo L, Nagy Z, et al. Confirmation of functional zones within the human subthalamic nucleus: patterns of connectivity and sub-parcellation using diffusion weighted imaging. NeuroImage, 2012, 60: 83–94

    Article  Google Scholar 

  9. Plantinga B R, Temel Y, Duchin Y, et al. Individualized parcellation of the subthalamic nucleus in patients with Parkinson’s disease with 7T MRI. NeuroImage, 2018, 168: 403–411

    Article  Google Scholar 

  10. Pozzi N G, Arnulfo G, Canessa A, et al. Distinctive neuronal firing patterns in subterritories of the subthalamic nucleus. Clin NeuroPhysiol, 2016, 127: 3387–3393

    Article  Google Scholar 

  11. Dembek T A, Roediger J, Horn A, et al. Probabilistic sweet spots predict motor outcome for deep brain stimulation in Parkinson disease. Ann Neurol, 2019, 86: 527–538

    Article  Google Scholar 

  12. Novak P, Przybyszewski A W, Barborica A, et al. Localization of the subthalamic nucleus in Parkinson disease using multiunit activity. J Neurol Sci, 2011, 310: 44–49

    Article  Google Scholar 

  13. Wan K R, Maszczyk T, See A A Q, et al. A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease. Clin Neurophys, 2019, 130: 145–154

    Article  Google Scholar 

  14. Karthick P A, Wan K R, Qi A S A, et al. Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson’s disease using microelectrode recordings and wavelet packet features. J Neurosci Method, 2020, 343: 108826

    Article  Google Scholar 

  15. Khosravi M, Atashzar S F, Gilmore G, et al. Intraoperative localization of STN during DBS surgery using a data-driven model. IEEE J Transl Eng Health Med, 2020, 8: 1–9

    Article  Google Scholar 

  16. Thompson J A, Oukal S, Bergman H, et al. Semi-automated application for estimating subthalamic nucleus boundaries and optimal target selection for deep brain stimulation implantation surgery. J Neurosurg, 2019, 130: 1224–1233

    Article  Google Scholar 

  17. Akram H, Sotiropoulos S N, Jbabdi S, et al. Subthalamic deep brain stimulation sweet spots and hyperdirect cortical connectivity in Parkinson’s disease. NeuroImage, 2017, 158: 332–345

    Article  Google Scholar 

  18. Xiao L X, Li C Z, Wang Y J, et al. Automatic identification of sweet spots from MERs for electrodes implantation in STN-DBS. Int J CARS, 2021, 16: 809–818

    Article  Google Scholar 

  19. Wang Z, Oates T. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence, 2015. 40–46

  20. Nowacki A, Nguyen T A K, Tinkhauser G, et al. Accuracy of different three-dimensional subcortical human brain atlases for DBS-lead localisation. NeuroImage-Clin, 2018, 20: 868–874

    Article  Google Scholar 

  21. Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module. In: Proceedings of European Conference on Computer Vision (ECCV), 2018. 3–19

  22. Cardona H D V, Álvarez M A, Orozco A. Multi-task learning for subthalamic nucleus identification in deep brain stimulation. Int J Mach Learn Cyber, 2018, 9: 1181–1192

    Article  Google Scholar 

  23. Martin T, Peralta M, Gilmore G, et al. Extending convolutional neural networks for localizing the subthalamic nucleus from micro-electrode recordings in Parkinson’s disease. BioMed Signal Process Control, 2021, 67: 102529

    Article  Google Scholar 

  24. Rajpurohit V, Danish S F, Hargreaves E L, et al. Optimizing computational feature sets for subthalamic nucleus localization in DBS surgery with feature selection. Clin Neurophysiol, 2015, 126: 975–982

    Article  Google Scholar 

  25. Valsky D, Marmor-Levin O, Deffains M, et al. Stop! border ahead: automatic detection of subthalamic exit during deep brain stimulation surgery. Mov Disord, 2017, 32: 70–79

    Article  Google Scholar 

  26. Bruce L M, Koger C H, Li J. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Trans Geosci Remote Sens, 2002, 40: 2331–2338

    Article  Google Scholar 

  27. Cheng Y W, Lin M X, Wu J, et al. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network. Knowl-Based Syst, 2021, 216: 106796

    Article  Google Scholar 

  28. Salyers J B, Dong Y, Gai Y. Continuous wavelet transform for decoding finger movements from single-channel EEG. IEEE Trans Biomed Eng, 2019, 66: 1588–1597

    Article  Google Scholar 

  29. Kingma D P, Ba J. Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations, 2015

Download references

Acknowledgements

This work was supported in part by Shenzhen Fundamental Research Program (Grant Nos. JCYJ202001091-10208764, JCYJ20200109110420626), in part by National Natural Science Foundation of China (Grant Nos. U1813204, 61802385, 62072468), Natural Science Foundation of Guangdong (Grant No. 2021A1515012604), and in part by Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019A1515111106). We gratefully thank the reviewers for their constructive comments.

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Author notes

  1. Xiao L X and Li C Z have the same contribution to this work.

Authors and Affiliations

  1. College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266580, China

    Linxia Xiao & Yanjiang Wang

  2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Caizi Li, Weixin Si & Pheng-Ann Heng

  3. Department of Neurosurgery, Shenzhen Second People’s Hospital, Shenzhen, 518035, China

    Hai Lin, Doudou Zhang & Xiaodong Cai

  4. School of Medicine, Shenzhen University, Shenzhen, 518000, China

    Hai Lin, Doudou Zhang & Xiaodong Cai

  5. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China

    Pheng-Ann Heng

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  1. Linxia Xiao
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  2. Caizi Li
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  3. Yanjiang Wang
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  4. Weixin Si
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Correspondence to Weixin Si.

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Xiao, L., Li, C., Wang, Y. et al. Amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes. Sci. China Inf. Sci. 65, 140404 (2022). https://doi.org/10.1007/s11432-021-3392-1

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  • DOI: https://doi.org/10.1007/s11432-021-3392-1

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