Functional connectivity analysis in the human brain using ultra-high field MRI

Abstract: Introduction: Functional magnetic resonance imaging (MRI) is a non-invasive method which uses a combination of a strong magnetic field and radio frequency pulses to image magnetic difference between oxygenated and deoxygenated blood in the human brain. This contrast differences can be used to identify areas in the brain when subjects performing an active task in the MRI scanner. It is also possible to measure spontaneous BOLD oscillation in absence of an external stimuli, a method called resting-state fMRI (rsfMRI). However, it is necessary to estimated and remove physiological noise, like head movements or heartbeat, as well as MRI scanner noise. Those estimated signals are called confounds. Therefore, an accurate preprocessing of the time signals is necessary. However, available preprocessing pipelines are not well established yet for rsfMRI data from ultra-high field MRI scanners. The goal of this study was to compare two slightly different rsfMRI preprocessing pipelines on the same dataset. Further, to investigate the influence of these differences on the robustness and functional connectivity of specific resting-state networks (RSN). Methods: A rsfMRI dataset from ten healthy subjects was acquired on an ultra-high field seven Tesla MRI scanner and preprocessed with both pipelines, CPAC and fMRIprep. A group-wise independent component analysis (ICA) was performed to measure the functional and spatial connectivity between and within RSN. Additionally, we performed a detailed comparison of the confounds between the pipelines. Results: We identified six different RSN. Subjects preprocessed with fMRIprep showed a strong temporal correlation within the visual, sensory motor as well as between the left and right memory function network. However, there were no significant spatial differences between the pipelines. Although head motion confounds were similar, confounds using brain masks to extract the signal differ. Discussion: The stronger positive and negative correlation is in line with the literature although the study lack in statistical power. The major impact of the pipeline differences could be addressed to varying brain masks from the estimated confounds. This detailed comparison may help to further investigate the influence of different preprocessing steps to functional connectivity.