title: Current source density reconstruction from incomplete data.
abstract: We propose two ways of estimating current source density (CSD) from measurements of voltage on a Cartesian grid with missing recording points using the inverse CSD method. The simplest approach is to substitute local averages (LA) in place of missing data. A more elaborate alternative is to estimate a smaller number of CSD parameters than the actual number of recordings and to take the least-squares fit (LS). We compare the two approaches in the three-dimensional case on several sets of surrogate and experimental data, for varying numbers of missing data points, and discuss their advantages and drawbacks. One can construct CSD distributions for which one or the other approach is better. However, in general, the LA method is to be recommended as being more stable and more robust to variations in the recorded fields.
authors.affiliation: Department of Neurophysiology, Nencki Institute of Experimental Biology, 02-093 Warsaw, Poland. d.wojcik@nencki.gov.pl
authors.forename: Daniel K
authors.initials: DK
authors.lastname: Wójcik
authors.forename: Szymon
authors.initials: S
authors.lastname: Leski
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods; D002490:Central Nervous System / Q000502:physiology*; D003198:Computer Simulation; D004594:Electrophysiology / Q000379:methods*; D008432:Mathematical Computing; D055641:Mathematical Concepts; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution; D016548:Prosencephalon / Q000502:physiology; D051381:Rats; D012815:Signal Processing, Computer-Assisted