title: Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.
abstract: OBJECTIVE
  During deep brain stimulation (DBS) surgery for the treatment of advanced Parkinson's disease (PD), microelectrode recording (MER) in conjunction with functional stimulation techniques are commonly applied for accurate electrode implantation. However, the development of automatic methods for clinical decision making has to date been characterized by the absence of a robust single-biomarker approach. Moreover, it has only been restricted to the framework of MER without encompassing intraoperative macrostimulation. Here, we propose an integrated series of novel single-biomarker approaches applicable to the entire electrophysiological procedure by means of a stochastic dynamical model.
  
  
  METHODS
  The methods are applied to MER data pertinent to ten DBS procedures. Considering the presence of measurement noise, we initially employ a multivariate phase synchronization index for automatic delineation of the functional boundaries of the subthalamic nucleus (STN) and determination of the acceptable MER trajectories. By introducing the index into a nonlinear stochastic model, appropriately fitted to pre-selected MERs, we simulate the neuronal response to periodic stimuli (130 Hz), and examine the Lyapunov exponent as an indirect indicator of the clinical effectiveness yielded by stimulation at the corresponding sites.
  
  
  RESULTS
  Compared with the gold-standard dataset of annotations made intraoperatively by clinical experts, the STN detection methodology demonstrates a false negative rate of 4.8% and a false positive rate of 0%, across all trajectories. Site eligibility for implantation of the DBS electrode, as implicitly determined through the Lyapunov exponent of the proposed stochastic model, displays a sensitivity of 71.43%.
  
  
  CONCLUSIONS
  The suggested comprehensive method exhibits remarkable performance in automatically determining both the acceptable MER trajectories and the optimal stimulation sites, thereby having the potential to accelerate precise target finalization during DBS surgery for PD.
authors.affiliation: Biomedical Simulations and Imaging Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 15780 Athens, Greece.
authors.forename: Sofia D
authors.initials: SD
authors.lastname: Karamintziou
authors.forename: George L
authors.initials: GL
authors.lastname: Tsirogiannis
authors.forename: Pantelis G
authors.initials: PG
authors.lastname: Stathis
authors.forename: George A
authors.initials: GA
authors.lastname: Tagaris
authors.forename: Efstathios J
authors.initials: EJ
authors.lastname: Boviatsis
authors.forename: Damianos E
authors.initials: DE
authors.lastname: Sakas
authors.forename: Konstantina S
authors.initials: KS
authors.lastname: Nikita
mesh_terms: D000368:Aged; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D046690:Deep Brain Stimulation / Q000295:instrumentation / Q000379:methods*; D004567:Electrodes, Implanted; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D015233:Models, Statistical; D016343:Monitoring, Intraoperative / Q000379:methods*; D010300:Parkinson Disease / Q000503:physiopathology* / Q000628:therapy*; D019919:Prosthesis Implantation / Q000379:methods; D013269:Stochastic Processes; D020531:Subthalamic Nucleus / Q000503:physiopathology / Q000601:surgery*; D016896:Treatment Outcome