Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly important tool in mapping the functional networks of the brain. This tool has been used to examine network changes induced by cognitive and emotional states, neurological traits, and neuropsychiatric disorders. However, noise that remains in the rs-fMRI data after preprocessing has limited the reliability of individual-subject results, wherein scanner artifacts, subject movements, and other noise sources induce non-neural temporal correlations in the blood oxygen level-dependent (BOLD) timeseries. Numerous preprocessing methods have been proposed to isolate and remove these confounds; however, the field has not coalesced around a standard preprocessing pipeline. In comparisons, these preprocessing methods are often assessed with only a single metric of rs-fMRI data quality, such as reliability, without considering other aspects in tandem, such as signal-to-noise ratio and group discriminability. The present study seeks to identify the data preprocessing pipeline that optimizes rs-fMRI data across multiple outcome measures. Specifically, we aim to minimize the noise in the data and maximize result reliability, while retaining the unique features that characterize distinct groups. We examine how these metrics are influenced by bandpass filter selection and noise regression in four datasets, totaling 181 rs-fMRI scans and 38 subject-driven memory scans. Additionally, we perform two different rs-fMRI analysis methods - dual regression and region-of-interest based functional connectivity - and highlight the preprocessing parameters that optimize both approaches. Our results expand upon previous reports of individual-scan reliability, and demonstrate that preprocessing parameter selection can significantly change the noisiness, reliability, and heterogeneity of rs-fMRI data. The application of our findings to rs-fMRI data analysis should improve the validity and reliability of rs-fMRI results, both at the individual-subject level and the group level.
View details for DOI 10.1016/j.neuroimage.2015.05.015
View details for Web of Science ID 000358045100007
View details for PubMedID 25987368