SpecSeg: cross spectral power-based segmentation of neurons and neurites in chronic calcium imaging datasets

Abstract

Imaging calcium signals in neurons of awake, behaving animals using single- or multi-photon microscopy facilitates the study of coding in large neural populations. Such experiments produce massive datasets requiring powerful methods to extract responses from hundreds of neurons. We present SpecSeg, a new open-source toolbox for 1) segmentation of regions of interest (ROIs) representing neuronal structures, 2) inspection and manual editing of ROIs, 3) neuropil correction and signal extraction and 4) matching of ROIs in sequential recordings. SpecSeg uses a novel method for ROI registration, based on temporal cross-correlations of low-frequency components derived by Fourier analysis, of each pixel with its neighbors. The approach is insightful and enables ROI detection around neurons or neurites. It works for single- (miniscope) and multi-photon microscopy data, eliminating the need for separate toolboxes. SpecSeg thus provides an efficient and user-friendly approach for analyzing calcium responses in neuronal structures imaged over prolonged periods of time.