Extraction of Neural Activity from In Vivo Optical Recordings Using Multiple Independent Component Analysis

Abstract

A multiple independent component analysis (ICA) method based on the noisy time-delayed decorrelation algorithm is described that overcomes the problems and improves the usefulness of conventional ICA, which is commonly used for extracting the actual neural activity from data measured using optical recording with a voltage-sensitive dye to visualize neural activities in cortical areas as two-dimensional images. The problems with conventional ICA extraction include the lack of an a priori guarantee that the solution will be appropriate, the linear mixing of mutually independent random variables although the mixtures are not random variables but time signals in many applications, and the general requirement for repetitive calculation of large matrices. Application of multiple ICA to the extraction of neural activities in the guinea pig auditory cortex evoked by both click sounds and pure tones from optical recordings made using a voltage sensitive dye demonstrated that it effectively removes pulsatile and respiratory components from the measurement data and extracts neural activities from the optical recordings.