Development of a data analysis platform for characterizing functional connectivity networks in rodents

Abstract: This document addresses the development and implementation of a routine for analyzing resting-state functional Magnetic Resonance Imaging (rs-fMRI) data in rodents. Even though resting-state connectivity is studied in humans already for several years with diverse applications in mental disorders or degenerative brain diseases, the interest for this modality is much more recent and less common in rodents. The goal of this project is to set an ensemble of tools in order to be able for the experimental MR team of KERIC to analyze rs-fMRI in rodents in a well defined and easy way. During this project several critical choices have been done, one of them is to use the Independent Component Analysis (ICA) in order to process the data rather than a seed-based approach. Also it was decided to use medetomidine as anesthesia rather than isoflurane for the experiments. The routine developed during this project was applied for a project studying the effects of running on an animal model of depression. The routine is composed of several steps, the preprocessing of the data mainly realized with SPM8, the processing using GIFT and the postprocessing which is some statistic tests on the results from GIFT in order to reveal differences between groups using the 2nd level analysis from SPM8 and the testing the correlations between components using the FNC toolbox.