title: Optimal stimulus scheduling for active estimation of evoked brain networks.
abstract: OBJECTIVE
  We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation.
  
  
  METHODS
  We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling.
  
  
  RESULTS
  By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples.
  
  
  CONCLUSIONS
  The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.
authors.affiliation: Department of Electrical and Systems Engineering, Washington University in St. Louis, MO 63130, USA.
authors.forename: MohammadMehdi
authors.initials: M
authors.lastname: Kafashan
authors.forename: ShiNung
authors.initials: S
authors.lastname: Ching
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer