Two-photon calcium imaging sequence Analysis Pipeline : a method for analyzing neuronal network activity

Abstract

Investigating the development of neuronal networks can help us to identify new therapies and treatments for conditions that affect the brain, such as autism and Alzheimer's disease. Two-photon calcium imaging has been a powerful tool for the investigation of the development of neuronal networks. However, one of the major challenges of working with two-photon calcium images is processing the large data sets, which often requires manual analysis by a skilled researcher. Here, we introduce a machine learning (ML) pipeline for the analysis of two-photon calcium image sequences. This semi-autonomous ML pipeline includes proposed methods for automatically identifying neurons, signal extraction, signal processing, event detection, feature extraction, and analysis. We run our ML pipeline on a dataset of two-photon calcium image sequences extracted by our team. This dataset includes two-photon calcium image sequences of spontaneous network activity from primary cortical cultures of Mecp2-deficient and wild-type mice. Loss-of-function mutation in the MECP2 gene, causes 95% of Rett syndrome cases and some cases of autism. We evaluate our ML pipeline using this dataset. Our ML pipeline reduces the time required to analyze two-photon calcium images from over 10 minutes to about 30 seconds per sample. Our goal is to accelerate the analysis of neuronal network function to aid in our understanding of neurological disorders and the identification of novel therapeutic targets.