Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions

Yun Zhao, Felix Luong, Simon Teshuva, Andria Pelentritou, William Woods, David Liley, Daniel F. Schmidt, Mario Boley, Levin Kuhlmann

Research output: Contribution to journalArticlepeer-review

Abstract

Recent work presented a framework for space-time-resolved neurophysiological process imaging that augments existing electromagnetic source imaging techniques. In particular, a nonlinear Analytic Kalman filter (AKF) has been developed to efficiently infer the states and parameters of neural mass models believed to underlie the generation of electromagnetic source currents. Unfortunately, as the initialization determines the performance of the Kalman filter, and the ground truth is typically unavailable for initialization, this framework might produce suboptimal results unless significant effort is spent on tuning the initialization. Notably, the relation between the initialization and overall filter performance is only given implicitly and is expensive to evaluate; implying that conventional optimization techniques, e.g. gradient or sampling based, are inapplicable. To address this problem, a novel efficient framework based on blackbox optimization has been developed to find the optimal initialization by reducing the signal prediction error. Multiple state-of-the-art optimization methods were compared and distinctively, Gaussian process optimization decreased the objective function by 82.1% and parameter estimation error by 62.5% on average with the simulation data compared to no optimization applied. The framework took only 1.6h and reduced the objective function by an average of 13.2% on 3.75min 4714-source channel magnetoencephalography data. This yields an improved method of neurophysiological process imaging that can be used to uncover complex underpinnings of brain dynamics.

Original languageEnglish
Article number2350024
JournalInternational Journal of Neural Systems
Volume33
Issue number5
StatePublished - 1 May 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 World Scientific Publishing Company.

Keywords

  • Blackbox optimization
  • brain imaging
  • Gaussian process optimization
  • Kalman filter
  • neural mass model

ASJC Scopus subject areas

  • Computer Networks and Communications

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