Deterministic Tractography Analysis of Rat Brain Using SIGMA Atlas in 9.4T MRI

Preclinical studies using rodents have been the choice for many neuroscience researchers due totheir close reflection of human biology. In particular, research involving rodents has utilized MRI to accurately identify brain regions and characteristics by acquiring high resolution cavity images with different contrasts non-invasively, and this has resulted in high reproducibility and throughput. In addition, tractographic analysis using diffusion tensor imaging to obtain information on the neural structure of white matter has emerged as a major methodology in the field of neuroscience due to its contribution in discovering significant correlations between altered neural connections and various neurological and psychiatric diseases. However, unlike image analysis studies with human subjects where a myriad of human image analysis programs and procedures have been thoroughly developed and validated, methods for analyzing rat image data using MRI in preclinical research settings have seen significantly less developed. Therefore, in this study, we present a deterministic tractographic analysis pipeline using the SIGMA atlas for a detailed structural segmentation and structural connectivity analysis of the rat brain's structural connectivity. In addition, the structural connectivity analysis pipeline presented in this study was preliminarily tested on normal and stroke rat models for initial observation.

Automated summary

Preclinical studies using rodents have been popular among neuroscience researchers for their close reflection of human biology. While MRI technology has been utilized to accurately identify brain regions in rodents, the analysis methods in this field are not as well-developed compared to those for human subjects. This study introduces a deterministic tractographic analysis pipeline for detailed structural segmentation and connectivity analysis of the rat brain, which has been preliminarily tested on normal and stroke rat models.