Spatio-temporal patterns of neuronal activity: analysis of optical imaging data using geometric shape matching

Abstract

Optical imaging of neuronal network activity yields information of spatial dynamics which generally is analyzed visually. The transient appearance of spatial activity patterns is difficult to gauge in a quantifiable manner, or may even altogether escape detection. Here, we employ geometric shape matching using Fréchet distances or straight skeletons to search for pre-selected patterns in optical imaging data with adjustable degrees of tolerance. Data were sampled from fluorescence changes of a voltage-sensitive dye recorded with a 464-photodiode array. Fluorescence was monitored in a neuronal network in vitro. Neuronal activity prompting fluorescence fluctuations consisted of spontaneous epileptiform discharges in neocortical slices from patients undergoing epilepsy surgery. The experiments show that: (a) spatial activity patterns can be detected in optical imaging data; (b) shapes such as ‘mini-foci’ appear in close correlation to bioelectric discharges monitored with field potential electrodes in a reproducible manner; (c) Fréchet distances yield more conservative matches regarding rectangular, and less conservative hits with respect to radially symmetric shapes than the straight skeleton approach; and (d) tolerances of 0.03–0.1 are suited to detect faithful images of pre-selected shapes, whereas values >0.8 will report matches with any polygonal pattern. In conclusion, the methods reported here are suited to detect and analyze spatial, geometric dynamics in optical imaging data.