Enhancing Resting-State fMRI Analysis: Development of an Automated Multi-Echo Preprocessing Pipeline and Functional Connectivity Exploration in Parkinson's Disease

The human brain is a network of more than 80 billion individual nerve cells interconnected in neural circuits that build our perceptions of the external world, fix our attention, guide our decisions, and implement our actions. Functional magnetic resonance imaging (fMRI) is widely used to study these brain connections and to investigate neurological disorders, such as Parkinson’s disease. The primary objective of this study is to develop an automated pipeline for preprocessing multi-echo fMRI data at resting state: for this purpose, FSL and AFNI methodologies will be compared based on the evaluation of tSNR maps to evaluate the most effective preprocessing approach. After preprocessing, the data will be subjected to denoising with tedana. The optimized pipeline will then be applied to the analysis of fMRI data to study differences in functional connectivity between patients with Parkinson’s disease and healthy controls, focusing on the relationship between subcortical and cortical areas, as they can be detected more accurately with multi-echo data. By identifying differences in brain connectivity between the groups through this research, a better understanding of Parkinson’s disease can be gained: analysis of subcortical and cortical areas could reveal distinctive patterns useful for diagnosis and evaluation of the disease. The use of multi-echo data could also improve temporal resolution and increase sensitivity in detecting alterations in brain connectivity, providing valuable insights into Parkinson’s disease. In conclusion, this automated pipeline for processing resting-state multi-echo fMRI data in Parkinson’s disease facilitates the study of functional connectivity, and the analysis of subcortical and cortical connections contributes to the understanding of the neural basis of the disease and the search for biomarkers for early diagnosis and treatment monitoring.