Thesis Project Form

This thesis focuses on the analysis of structural and functional brain connectivity using diffusion MRI and electrophysiological recordings in rodent models (rats). The project belongs to the field of neuroengineering and translational neuroscience, with the aim of understanding how structural connectivity relates to neural dynamics and functional brain activity. The work will contribute to the development of analysis frameworks for studying both physiological and pathological brain connectivity.

The objective of the thesis is to develop and apply multimodal analysis pipelines for the investigation of structural and functional brain connectivity in rodent models using diffusion MRI and electrophysiological data.. The student will review and adapt state-of-the-art processing pipelines for rat diffusion tensor imaging (DTI), starting from existing MRI datasets and acquisition protocols. The work will include preprocessing of diffusion MRI data, extraction of DTI scalar maps (FA, MD, AD, RD), tractography, and structural connectivity analysis to reconstruct white matter pathways and characterize brain network organization in healthy rodents. When available, additional datasets from lesion models may be included to explore connectivity alterations associated with brain injury.

Statistical analyses, including TBSS, will be used to evaluate structural connectivity patterns across experimental conditions. Imaging-derived connectivity measures will then be correlated with electrophysiological recordings acquired from cortical and subcortical regions to study the relationship between structural connectivity and spontaneous neural activity. The project is part of a broader research activity on multimodal brain modeling and neuroengineering approaches for the study of brain function and recovery mechanisms.

The student will acquire practical skills in diffusion MRI preprocessing, DTI analysis, tractography, structural connectivity reconstruction, and TBSS using tools such as FSL and MRtrix. Additional training will include electrophysiological signal analysis, multimodal data integration, and statistical methods for studying stroke-related alterations in brain connectivity.