Beta, gamma and High-Frequency Oscillation characterization for targeting in Deep Brain Stimulation procedures

Keywords: Deep Brain Stimulation, microelectrode recording, biomedical signal processing, Parkinson’s disease, subthalamic nucleus


Deep Brain Stimulation (DBS) has been successfully used to treat patients with Parkinson’s Disease. DBS employs an electrode that regulates the oscillatory activity of the basal ganglia, such as the subthalamic nucleus (STN). A critical point during the surgical implantation of such electrode is the precise localization of the target. This is done using presurgical images, stereotactic frames, and microelectrode recordings (MER). The latter allows neurophysiologists to visualize the electrical activity of different structures along the surgical track, each of them with well-defined variations in the frequency pattern; however, this is far from an automatic or semi-automatic method to help these specialists make decisions concerning the surgical target. To pave the way to automation, we analyzed three frequency bands in MER signals acquired from 11 patients undergoing DBS: beta (13-40 Hz), gamma (40-200 Hz), and high-frequency oscillations (HFO – 201-400 Hz). In this study, we propose and assess five indexes in order to detect the STN: variations in autoregressive parameters and their derivative along the surgical track, the energy of each band calculated using the Yule-Walker power spectral density, the high-to-low (H/L) ratio, and its derivative. We found that the derivative of one parameter of the beta band and the H/L ratio of the HFO/gamma bands produced errors in STN targeting like those reported in the literature produced by image-based methods (<2 mm). Although the indexes introduced here are simple to compute and could be applied in real time, further studies must be conducted to be able to generalize their results.

Author Biographies

Sarah Valderrama-Hincapié, Universidad de Antioquia, Colombia

Bioeng., Research Group Bioinstrumentation and Clinical Engineering (GIBIC), Universidad de Antioquia, Medellín-Colombia,

Sebastián Roldán-Vasco*, Instituto Tecnológico Metropolitano, Colombia

MSc. Engineer, Research Groups in Advanced Materials and Energy (MATyER), Instituto Tecnológico Metropolitano, Medellín-Colombia,

Sebastián Restrepo-Agudelo, Instituto Tecnológico Metropolitano, Colombia

MSc. Automation and Industrial Control, Research Groups in Advanced Materials and Energy (MATyER), Instituto Tecnológico Metropolitano, Medellín-Colombia,

Frank Sánchez-Restrepo, Universidad de Antioquia, Colombia

Bioeng., Research Group Bioinstrumentation and Clinical Engineering (GIBIC), Universidad de Antioquia, Medellín-Colombia,

William D. Hutchison, University of Toronto, Canadá

PhD. in Neuroscience, Division of Neurosurgery, Department of Surgery and Physiology, University of Toronto, Toronto- Canadá,

Adriana L. López-Ríos, Hospital Universitario San Vicente Fundación , Colombia

Neurosurgeon, Functional and Stereotactic Neurosurgeon, Neurosurgeon Oncologist and Skull Base, Hospital Universitario San Vicente Fundación (Medellín and Rionegro), Medellín – Colombia,

Alher Mauricio Hernández, Universidad de Antioquia, Colombia

PhD. in Biomedical Engineer, Research Group Bioinstrumentation and Clinical Engineering (GIBIC), Universidad de Antioquia, Medellín- Colombia,


F. Fröhlich, “Chapter 23” in Parkinson’s Disease BT - Network Neuroscience, San Diego: Academic Press, 2016, pp. 291–296.

L. M. de Lau; M. M. Breteler, “Epidemiology of Parkinson’s disease,” Lancet Neurol., vol. 5, no. 6, pp. 525–535, Jun. 2006.

P. J. Magill; A. Sharott; M. D. Bevan; P. Brown; J. P. Bolam, “Synchronous Unit Activity and Local Field Potentials Evoked in the Subthalamic Nucleus by Cortical Stimulation,” J. Neurophysiol., vol. 92, no. 2, pp. 700–714, Aug. 2004.

J. Obeso; M. Rodriguez; M. DeLong, “Basal ganglia pathophysiology: A critical review,” Adv Neurol, pp. 3–18, 1997. Available:

J. M. Bronstein et al., “Deep Brain Stimulation for Parkinson Disease An Expert Consensus and Review of Key Issues,” Arch. Neurol., vol. 68, no. 2, pp. 165–171, Feb. 2011.

A. Moran; I. Bar-gad; H. Bergman; Z. Israel, “Real-Time Refinement of Subthalamic Nucleus Targeting Using Bayesian Decision-Making on the Root Mean Square Measure,” Mov. Disord., vol. 21, no. 9, pp. 1425–1431, Jun. 2006.

A. O. Hebb; K. J. Miller, “Semi-automatic stereotactic coordinate identification algorithm for routine localization of Deep Brain Stimulation electrodes,” J. Neurosci. Methods, vol. 187, no. 1, pp. 114–119, Mar. 2010,

J. A. Thompson et al., “Semi-automated application for estimating subthalamic nucleus boundaries and optimal target selection for deep brain stimulation implantation surgery,” pp. 1–10, May. 2018.

R. Dallapiazza; M. S. McKisic; B. Shah; W. J. Elias, “Neuromodulation for Movement Disorders,” Neurosurg. Clin. N. Am., vol. 25, no. 1, pp. 47–58, Jan. 2014.

A.-S. Moldovan; S. Groiss; S. Elben; M. Südmeyer, A. Schnitzler, and L. Wojtecki, “The treatment of Parkinson′s disease with deep brain stimulation: current issues,” Neural Regen. Res., vol. 10, no. 7, p. 1018-1022, Jul. 2015.

A. L. Benabid, “Deep brain stimulation for Parkinson’s disease,” Curr. Opin. Neurobiol., vol. 13, no. 6, pp. 696–706, Dec. 2003.

C. C. McIntyre; S. Mori; D. L. Sherman; N. V. Thakor; J. L. Vitek, “Electric field and stimulating influence generated by deep brain stimulation of the subthalamic nucleus,” Clin. Neurophysiol., vol. 115, no. 3, pp. 589–595, Mar. 2004.

A. Husch; M. V. Petersen; P. Gemmar; J. Goncalves; F. Hertel, “PaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation,” NeuroImage Clin., vol. 17, pp. 80–89, Oct. 2017.

W. D. Hutchison et al., “Neurophysiological Identification of the Subthalamic nucleus in Surgery for Parkinson ’ s Disease,” Ann. Neurol., vol. 44, no. 4, pp. 622–628, Oct. 2004.

M. S. Okun et al., “Management of Referred Deep Brain Stimulation Failures A Retrospective Analysis From 2 Movement Disorders Centers,” Arch. Neurol., vol. 62, no. 8, pp. 1250-1255, Aug. 2005.

A. Berney et al., “Effect on mood of subthalamic DBS for Parkinson’s disease A consecutive series of 24 patients,” Neurology, vol. 59, no. 9, pp. 1427 LP – 1429, Nov. 2002.

J. L. Houeto et al.,“Subthalamic Stimulation in Parkinson Disease: Behavior and Social Adaptation,” Arch Neurol, vol. 63, no. 8, pp. 1090–1095, Aug. 2006.

K. P. Michmizos; P. Frangou; P. Stathis; D. Sakas; K. S. Nikita, “Beta-band frequency peaks inside the subthalamic nucleus as a biomarker for motor improvement after deep brain stimulation in parkinson’s disease,” IEEE J. Biomed. Heal. Informatics, vol. 19, no. 1, pp. 174–180, Jan. 2015.

H. Bronte-Stewart; C. Barberini; M. M. Koop; B. C. Hill; J. M. Henderson; B. Wingeier, “The STN beta-band profile in Parkinson’s disease is stationary and shows prolonged attenuation after deep brain stimulation,” Exp. Neurol., vol. 215, no. 1, pp. 20–28, Jan. 2009.

M. C. Rodriguez-Oroz et al., “The subthalamic nucleus in Parkinson disease: somatotopic organization and physiological characteristics,” Brain, vol. 124, no. 9, pp. 1777–1790, Sep. 2001.

A. A. Kühn; T. Trottenberg; A. Kivi; A. Kupsch; G. H. Schneider; P. Brown, “The relationship between local field potential and neuronal discharge in the subthalamic nucleus of patients with Parkinson’s disease,” Exp. Neurol., vol. 194, no. 1, pp. 212–220, Jul. 2005.

M. Beudel; P. Brown, “Adaptive deep brain stimulation in Parkinson’s disease,” Parkinsonism Relat. Disord., vol. 22, no. 1, pp. S123–S126, Jan. 2016.

S. Little; P. Brown, “What brain signals are suitable for feedback controzl of deep brain stimulation in Parkinson’s disease?,” Ann. N. Y. Acad. Sci., vol. 1265, no. 1, pp. 9–24, Jul. 2012.

D. Basha; J. O. Dostrovsky; A. L. Lopez Rios; M. Hodaie, A. M. Lozano; W. D. Hutchison, “Beta oscillatory neurons in the motor thalamus of movement disorder and pain patients,” Exp. Neurol., vol. 261, pp. 782–790, Nov. 2014.

D. Valsky et al., “Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery,” Mov. Disord., vol. 32, no. 1, pp. 70–79, Oct. 2016.

M. Weinberger; W. D. Hutchison; A. M. Lozano; M. Hodaie; J. O. Dostrovsky, “Increased gamma oscillatory activity in the subthalamic nucleus during tremor in Parkinson’s disease patients.,” J. Neurophysiol., vol. 101, no. 2, pp. 789–802, Feb. 2009.

G. Foffani; G. Ardolino; M. Egidi; E. Caputo; B. Bossi; A. Priori, “Subthalamic oscillatory activities at beta or higher frequency do not change after high-frequency DBS in Parkinson’s disease.,” Brain Res. Bull., vol. 69, no. 2, pp. 123–130, Mar. 2006.

C. C. Chen et al., “Intra-operative recordings of local field potentials can help localize the subthalamic nucleus in Parkinson’s disease surgery.,” Exp. Neurol., vol. 198, no. 1, pp. 214–221, Mar. 2006.

F. Alonso-Frech et al., “Slow oscillatory activity and levodopa-induced dyskinesias in Parkinson’s disease,” Brain, vol. 129, no. 7, pp. 1748–1757, Jul. 2006.

A. I. Yang; X. N. Vanegas; C. Lungu; K. A. Zaghloul, “Beta-Coupled High-Frequency Activity and Beta-Locked Neuronal Spiking in the Subthalamic Nucleus of Parkinson ’ s Disease,” Journal of Neuroscience, vol. 34, no. 38, pp. 12816–12827, Sep. 2014.

K. P. Burnham; D. R. Anderson, “Multimodel inference: Understanding AIC and BIC in model selection,” Sociol. Methods Res., vol. 33, no. 2, pp. 261–304, Nov. 2004.

S. E. Valderrama-Hincapié; A. M. Hernández; F. Sánchez; S. Roldán-Vasco; A. L. López-Ríos; W. D. Hutchison, “Optimization of spectral analysis of electrophysiological recordings of the subthalamic nucleus in Parkinson’s disease: A retrospective study,” in VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga. 2016, pp. 300–303.

S. Kara; S. Kemaloǧlu; N. Erdoǧan, “Comparison of fast Fourier transformation and autoregressive modelling as a diagnostic tool in analysis of lower extremity venous signals,” Comput. Biol. Med., vol. 36, no. 5, pp. 484–494, May. 2006.

J. G. Proakis; D. G. Manolakis, Digital signal processing: principles algorithms and applications. Prentice Hall, 3th edition, 2007. Available:

P. A. Starr; A. J. Martin; J. L. Ostrem; P. Talke; N. Levesque; P. S. Larson, “Subthalamic nucleus deep brain stimulator placement using high-field interventional magnetic resonance imaging and a skull-mounted aiming device: technique and application accuracy,” J. Neurosurg., vol. 112, no. 3, pp. 479–490, Mar. 2010.

K. Shahlaie; P. S. Larson; P. A. Starr, “Intraoperative Computed Tomography for Deep Brain Stimulation Surgery: Technique and Accuracy Assessment,” Oper. Neurosurg., vol. 68, no. suppl_1, pp. ons114–ons124, ons114–ons124, pp. Mar. 2011.

K. Burchiel; S. McCartney; A. Lee; A. Raslan, “Accuracy of deep brain stimulation electrode placement using intraoperative computed tomography without microelectrode recording,” Neurosurgery, vol. 119, no. 2, pp. 301-306, Aug. 2013.

R. B. Kochanski; S. Bus; G. Pal; L. V Metman; S. Sani, “Optimization of Microelectrode Recording in DBS Surgery Using Intraoperative Computed Tomography,” World Neurosurg. Vol. 103, pp. 168-173, Jul. 2017.

B. Brahimaj; R. B. Kochanski; S. Sani, “Microelectrode accuracy in deep brain stimulation surgery,” J. Clin. Neurosci., vol. 50, pp. 58–61, Apr. 2018.

K. Kostoglou; K. P. Michmizos; P. Stathis; D. Sakas; K. S. Nikita; G. D. Mitsis, “Classification and Prediction of Clinical Improvement in Deep Brain Stimulation from Intraoperative Microelectrode Recordings,” IEEE Trans. Biomed. Eng., vol. 64, no. 5, pp. 1123–1130, May. 2017.

K. R. Wan; T. Maszczyk; A. A. Q. See; J. Dauwels; N. K. K. King, “A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease,” Clin. Neurophysiol., vol. 130, no. 1, pp. 145–154, Jan. 2019.

S. Wong; G. H. Baltuch; J. L. Jaggi, S. F. Danish, “Functional localization and visualization of the subthalamic nucleus from microelectrode recordings acquired during DBS surgery with unsupervised machine learning,” J. Neural Eng., vol. 6, no. 2, pp. 1–11, Apr. 2009. Available:

A. Zaidel; A. Spivak; L. Shpigelman; H. Bergman; Z. Israel, “Delimiting subterritories of the human subthalamic nucleus by means of microelectrode recordings and a Hidden Markov Model,” Mov. Disord., vol. 24, no. 12, pp. 1785–1793, Sep. 2009.

W. Chaovalitwongse; Y. Jeong; M. K. Jeong; S. Danish; S. Wong, “Pattern recognition approaches for identifying subcortical targets during deep brain stimulation surgery,” IEEE Intell. Syst., vol. 26, no. 5, pp. 54–63, Sep. 2011.

H. D. Vargas Cardona et al., “NEUROZONE : On-line Recognition of Brain Structures in Stereotactic Surgery - Application to Parkinson ’ s Disease,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society., San Diego. 2012.

L. Schiaffino et al., “STN area detection using K-NN classifiers for MER recordings in Parkinson patients during neurostimulator implant surgery,” in J. Phys. Conf. Ser., vol. 705, 2016.

How to Cite
Valderrama-Hincapié, S., Roldán-Vasco, S., Restrepo-Agudelo, S., Sánchez-Restrepo, F., Hutchison, W. D., López-Ríos, A. L., & Hernández, A. M. (2020). Beta, gamma and High-Frequency Oscillation characterization for targeting in Deep Brain Stimulation procedures . TecnoLógicas, 23(49), 11-32.


Download data is not yet available.
Research Papers

More on this topic