An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI

Abstract

BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.

Methods for cleaning the BOLD fMRI signal

“…The performance of denoising techniques considerably depends on the preprocessing steps and their relative order in the preprocessing pipeline (Carp, 2012a; 2012b; Churchill et al, 2012a; 2012b; 2015; Hallquist et al, 2013; Jo et al, 2013; Jones et al, 2008; Shirer et al, 2015). Assuming that the data remains in the original subject’s space, a standard preprocessing pipeline can include any of the following steps: despiking, slice-timing correction, volume registration (a.k.a.…”

“…Multiple types of algorithms can be categorized within each preprocessing step, and each step involves selecting a set of parameters. It is therefore clear that the number of unique data preprocessing workflows can be enormous in fMRI data analysis, which may lead to substantial variability in the quality of the preprocessed data and conclusions from fMRI results (Carp, 2012a; Churchill et al, 2015). In practice, it is unfeasible to assess thoroughly all combinations of preprocessing pipelines.…”

“…In practice, it is unfeasible to assess thoroughly all combinations of preprocessing pipelines. To simplify the evaluation, some studies have attempted to investigate the optimal workflow in a rigorous, systematic manner by only including a subset of options and steps (Carp, 2012a; Churchill et al, 2012a; 2012b; 2015; Jones et al, 2008; Shirer et al, 2015). As a matter of fact, establishing a fixed preprocessing pipeline across all individuals in a study might not even be the best approach.…”

“…As a matter of fact, establishing a fixed preprocessing pipeline across all individuals in a study might not even be the best approach. Instead, defining individually-optimized pipelines may be more advantageous if examined with caution and carefully evaluated in terms of reproducibility and predictability of the results (Churchill et al, 2012a; 2012b; 2015). …”

“…Of course, this is a challenging task and automatized adaptive frameworks must be developed, for instance following Carp (2012b), because the optimal pipeline for a given dataset will likely depend on: a) the MR acquisition parameters (type of sequence, voxel size, TR, order of slice acquisition, slice orientation); b) subject-specific traits that modulate the type of noise existing in the data (level of head motion, respiration pattern, heart rate variability, blood pressure); c) the type of experiment, either resting state or task-based fMRI (block, event-related or mixed designs) and its goals, for instance detection versus efficiency (Liu et al, 2001) or brain-behaviour correlations (Churchill et al, 2015). …”