A principal components-based method for the detection of neuronal activity maps: application to optical imaging.

Overview

We present a novel analysis technique for the extraction of neuronal activity patterns from functional imaging data. We illustrate this technique on data from optical imaging. Optical imaging of the mammalian visual cortex probe the patterns in which the neuronal responses to various aspects of the visual world, such as orientation and color, are spatially organized within the cortex. Recovering these patterns from the image data is a challenging problem as the neuronal response signal is extremely weak in comparison to the background vegetative processes (e.g., circulation and respiration). The proposed technique obtains the neuronal activity pattern using a combination of principal component analysis and statistical significance testing. The performance of this method is compared with the results of existing analysis techniques. The comparison shows the new method to be more sensitive than previous methods.