Improving the Accuracy of Non-task fMRI Analysis by Quantifying Spatiotemporal Processing Induced Correlations

Non-task fMRI has become one of the most popular noninvasive areas of brain mapping research for neuroscientists. In non-task fMRI, various sources of “noise” corrupt the measured blood oxygenation level dependent (BOLD) signal. Many studies have aimed to attenuate the noise in reconstructed voxel measurements through spatial and temporal processing operations. While these solutions make the data more “appealing,” many commonly used processing operations induce artificial correlations in the acquired data. As such, it becomes increasingly more difficult to derive the true underlying covariance structure once the data has been processed. As the goal of non-task fMRI studies is to determine, utilize and analyze the true covariance structure of acquired data, such processing can lead to inaccurate and misleading conclusions drawn from the data if they are unaccounted for in the final connectivity analysis. In this manuscript, we develop a framework that represents the spatiotemporal processing and reconstruction operations as linear operators, providing a means of precisely quantifying the correlations induced or modified by such processing rather than by performing lengthy Monte Carlo simulations. A framework of this kind allows one to appropriately model the statistical properties of the processed data, optimize the data processing pipeline, characterize excessive processing, and draw more accurate functional connectivity conclusions.