Abstract
Precise functional localization plays an important role in deep brain stimulation for treatment of Parkinson’s disease (PD) to target the lesion of stimulation and observe spike morphology of the neural circuit for evaluating effect of stimulation. This research described a functional localization method in the brain of a cynomolgus monkey based on spike patterns recognition at different locations through machine learning algorithm. Through K-means algorithm cluster, spikes were sorted into different clusters, and the template of sorting was considered as spike pattern of each cluster. Four different spike patterns in cortex, three different spike patterns in white matter, and two different spike patterns in striatum were found through sorting in normal monkey, which were seen as features of different locations in normal monkey. While spike patterns found in PD monkey were totally different, including four different spike patterns in cortex, four different spike patterns in white matter, and five different spike patterns in striatum, considered as features of different locations in PD monkey. Cubic support vector machine (SVM) was used to train spike pattern recognition model for functional localization with accuracy of 100% in normal monkey, and the evaluation of trained model demonstrated reasonably excellent recognition accuracy of 99.5%. Weighted K-nearest neighbor (KNN) showed a better performance of accuracy (94.5%) of spike pattern recognition for functional localization in PD monkey than cubic SVM. In evaluation testing of the trained weighted KNN model, the accuracy reached to 96.1%. The results revealed that functional localization based on spike patterns recognition using machine learning algorithm would be an important tool in precise targeting and evaluating outcome not only for Parkinson’s disease, but also for other major brain diseases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adeniyi DA, Wei Z, Yongquan Y (2016) Automated web usage data mining and recommendation system using K-nearest neighbor (KNN) classification method. Appl Comput Inform 12:90–108
Alafeef M, Fraiwan M (2018) On the diagnosis of idiopathic Parkinson’s disease using continuous wavelet transform complex plot. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-1014-x
Alshargie F, Tang TB, Badruddin N, Kiguchi M (2017) Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach. Med Biol Eng Comput 56(1):125–136. https://doi.org/10.1007/s11517-017-1733-8
Andrzej M, Waldemar R (1993) Principal components analysis (PCA). Comput Geosci 19(3):303–342
Bosch P, Herrera M, López J, Maldonado S (2018) Mining EEG with SVM for understanding cognitive underpinnings of math problem solving strategies. Behav Neurol. https://doi.org/10.1155/2018/4638903(article ID 4638903)
Brinkmann BH, Patterson EE, Vite C, Vasoli VM, Crepeau D et al (2016) Correction: forecasting seizures using bivariate intracranial EEG measures and SVM in naturally occurring canine epilepsy. PLOS One 11(5):e0156476. https://doi.org/10.1371/journal.pone.0156476
Calabresi P, Centonze D, Gubellini P, Marfia GA et al (2000) Synaptic transmission in the striatum: from plasticity to neurodegeneration. Prog Neurobiol 61(3):231–265
Chen C, Zhang GH, Qian Z, Tarefdera RA, Tian Z (2016) Investigating driver injury severity patterns in rollover crashes using support vector machine models. Accid Anal Prev 90:128–139
Cortes C, Vapnik V (1995) Support-vector vetworks. Mach Learn 20:273–297. https://doi.org/10.1023/A:1022627411411
Ekiz S, Erdogmus P (2017) Comparative study of heart disease classification. 2017 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT). https://doi.org/10.1109/ebbt.2017.7956761
Falkenberg JH, McNames J, Burchiel KJ (2006) Automatic microelectrode recording analysis and visualization of the globus pallidus interna and stereotactic trajectory. Stereotact Funct Neurosurg 84:28–34
Favre J, Taha JM, Thomas Baumann, Burchiel KJ (1999) Computer analysis of the tonic, phasic, and kinesthetic activity of pallidal discharges in Parkinson patients. Surg Neurol 51:665–673
Fu K, Qu J, Chai Y, Dong Y (2014) Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomed Signal Process Control 13:15–22
Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. In: OTM confederated international conferences: on the move to meaningful internet systems, vol 2003, pp 986–996
Guo S, Zhuang P, Hallett M, Zheng Z et al (2013) Subthalamic deep brain stimulation for Parkinson’s disease: correlation between locations of oscillatory activity and optimal site of stimulation. Parkinsonism Relat Disord 19:109–114
Guridi J, Gorospe A, Ramos E et al (1999) Stereotactic targeting of the globus pallidus internus in Parkinson’s disease: imaging versus electrophysiological mapping. Neurosurgery 45:278–289
Hammond M, Bergman H, Brown P (2007) Pathological synchronization in Parkinson’s disease: networks, models and treatments. Trends Neurosci 30(7):357–364
Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31:651–666
Khedher L, Ramírez J, Górriz JM, Brahim A, Segovia F (2015) Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing 151:139–150. https://doi.org/10.1016/j.neucom.2014.09.072
Kinfe T, Vesper J (2013) The impact of multichannel microelectrode recording (MER) in deep brain stimulation of the basal ganglia. Stereotact Funct Neurosurg 117:27–33
Krack P, Pollak P, Limousin P et al (1998) Subthalamic nucleus or internal pallidal stimulation in young onset Parkinson’s disease. Brain 121:451–457
Li M, Xu H, Li X, Lu S (2018) Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technol Health Care 26:S509–S519. https://doi.org/10.3233/THC-174836
McCarthy MM, Moore-Kochlacsa C, Gu X, Boydenc ES, Han X, Kopella N (2011) Striatal origin of the pathologic beta oscillations in Parkinson’s disease. PNAS 108(28):11620–11625
Nakano T, Nukala BT, Zupancic S et al (2016) Gaits classification of normal vs. patients by wireless gait sensor and support vector machine (SVM) classifier. In: ICIS 2016, Okayama, Japan
Padraig C, Sarah JD (2007) K-nearest neighbor classifier. Technical report UCD-CSI-2007-4, University College Dublin
Piliourasa N, Kalatzisa I, Dimitropoulosb N, Cavouras D (2004) Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. Comput Med Imaging Graph 28:247–255
Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304
Senatus PB, Teeple D, McClelland S et al (2006) A technique for minimally altering anatomically based subthalamic electrode targeting by microelectrode recording. Neurosurg Focus 20(5):E8
Singh A, Mewes K, Gross RE et al (2016) Human striatal recordings reveal abnormal discharge of projection neurons in Parkinson’s disease. PNAS 113(34):9629–9634
Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37:8659–8666
Taha Z, Musa RM, Majeed AA, Alim MM, Abdullah MR (2018) The identification of high potential archers based on fitness and motor ability variables: a support vector machine approach. Hum Mov Sci 57:184–193
The Parkinson Association (2018). https://www.parkinsonassociation.org/facts-about-parkinsons-disease/. Accessed 10 May 2018
Wagstaff K, Cardie C, Rogers S, Schroedl S (2001) Constrained K-means clustering with background knowledge. In: Proceedings of the eighteenth international conference on machine learning, pp 577–584
Yong DD, Bhowmik S, Magnago F (2015) An effective power quality classifier using wavelet transform and support vector machines. Expert Syst Appl 42(15–16):6075–6081. https://doi.org/10.1016/j.eswa.2015.04.002
Zaidel A, Spivak A, Grieb B, Bergman H, Israel Z (2010) Subthalamic span of beta oscillations predicts deep brain stimulation efficacy for patients with Parkinson’s disease. Brain 133(7):2007–2021
Zebin T, Scully PJ, Ozanyan KB (2017) Inertial sensor based modelling of human activity classes: feature extraction and multi-sensor data fusion using machine learning algorithms. eHealth LNICST 181:306–314. https://doi.org/10.1007/978-3-319-49655-9_38
Zhang S, Song YL, Wang MX, Cai XX et al (2016) A silicon based implantable microelectrode array for electrophysiological and dopamine recording from cortex to striatum in the non-human primate brain. Biosens Bioelectron 85:53–61
Zhang SC, Li XL, Zong M et al (2017) Learning k for kNN classification. ACM Trans Intell Syst Technol 8(30):1–19
Zhang S, Song YL, Wang MX, Cai XX et al (2018) Real-time simultaneous recording of electrophysiological activities and dopamine overflow in the deep brain nuclei of a non-human primate with Parkinson’s disease using nano-based microelectrode arrays. Microsyst Nanoeng 4:17070. https://doi.org/10.1038/micronano.2017.70
Acknowledgements
This work was sponsored by the National Key Research and Development Program of Nano Science and Technology of China (2017YFA0205902), the National Natural Science Foundation of China (no. 61527815, no. 31500800, no. 61501426, no. 61601435), the Beijing Science and Technology Plan (Z161100004916001), and the Key Research Programs (QYZDJ-SSW-SYS015, XDA16020902) of Frontier Sciences, Chinese Academy of Sciences.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, M., Song, Y., Zhang, S. et al. Functional localization in the brain of a cynomolgus monkey based on spike pattern recognition with machine learning. J Ambient Intell Human Comput 14, 15469–15476 (2023). https://doi.org/10.1007/s12652-019-01576-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01576-9