Abstract
A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or “pipeline”) may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a quantitative procedure to denoise data that would otherwise be discarded due to artifact; this is particularly relevant for weak signal contrasts in single-subject, small-sample and clinical datasets.
Highlights
Blood Oxygenation Level Dependent fMRI (BOLD fMRI) is an invaluable tool for non-invasive studies of sensory, cognitive and motor neuroscience, and more recently, a range of clinical applications including pre-surgical planning, assessing stroke recovery, and quantifying the effects of therapeutic interventions, e.g. [4,5,6]
This paper presents the first comprehensive study of the interaction between multiple steps of the fMRI experimental pipeline, including task contrast, preprocessing pipeline, and heterogeneity of subject effects
Testing bias of individual subject optimization In the initial simulation analyses, we demonstrated that individual subject optimization does not significantly increase model bias, relative to fixed preprocessing
Summary
Blood Oxygenation Level Dependent fMRI (BOLD fMRI) is an invaluable tool for non-invasive studies of sensory, cognitive and motor neuroscience, and more recently, a range of clinical applications including pre-surgical planning (see review by Fernandez et al [1]), assessing stroke recovery (reviewed in [2,3]), and quantifying the effects of therapeutic interventions, e.g. [4,5,6]. It is important to optimize pipeline choices, as better denoising improves signal detection and allows researchers to retain artifact-corrupted data that would otherwise have been discarded from analyses. This is relevant for studies of aging and clinical groups, where signal is weaker, and head motion and physiological noise have a greater impact on fMRI data than for young normal controls [59,63]. Each of these steps influence signal and noise in fMRI, and may interact with preprocessing choices. This paper will focus on the interactions of data preprocessing choices (Step #4) with both betweensubject variability (Step #1) and experimental contrast design (Step #2), as there is currently limited information regarding how these steps interact
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