Quality control in resting-state functional connectivity: qualitative and quantitative measures

Palais 

The monitoring and assessment of data quality is an essential step in the acquisition and analysis of functional MRI (fMRI) data. Various quality control (QC) metrics can determine what subjects to exclude from the group analyses, and can also guide additional processing steps that may be necessary. This presentation describes a combination of qualitative and quantitative assessments to determine the quality of fMRI data, particularly resting-state fMRI data used to estimate functional connectivity. Processing is performed using the AFNI data analysis package, but can in principle be implemented using any fMRI processing package. QC measures are evaluated at different steps in the processing pipeline to catch gross abnormalities in the data, determine deviations in acquisition parameters, evaluate the alignment to template space, determine the level of head motion, and detect other sources of noise. This presentation also shows the effect of different quantitative QC cutoffs, specifically the motion censoring threshold, and the impact of bandpass filtering. This analysis shows that while motion censoring reduces artifacts, overly stringent censoring can result in more noisy functional connectivity estimates particularly when combined with bandpass filtering. The qualitative and quantitative metrics presented here can provide information about what subjects to exclude and what subjects to examine more closely in the analysis of large datasets.