Optimization of rs-fMRI Pre-processing for Enhanced Signal-Noise Separation, Test-Retest Reliability, and Group Discrimination
Introduction
Resting-state functional magnetic resonance imaging (rs-fMRI) has been an important tool in mapping the functional networks of the brain, and in examining network changes induced by cognitive and emotional states, traits, and disorders. Many findings related to resting-state networks, including the spatial distribution of functional networks and their cognitive correlates (Biswal et al., 1995, Hampson et al., 2002, Greicius et al., 2003, Kiviniemi et al., 2003, Beckmann et al., 2005, Bellec et al., 2006, Damoiseaux et al., 2006, Seeley et al., 2007, Kiviniemi et al., 2009, Smith et al., 2009), changes in network connectivity induced by cognitive states (Richiardi et al., 2010, Shirer et al., 2012), and group differences in network connectivity due to brain disorders (Greicius et al., 2004, Greicius et al., 2007, Hedden et al., 2009, Seeley et al., 2009, Sheline et al., 2010, Zhou et al., 2010, Johnson et al., 2013), are well-replicated at the group level. However, reliability at the individual level remains rather limited (Damoiseaux and Greicius, 2009, Honey et al., 2009). Previous studies that have evaluated the consistency of resting-state networks in individuals have mainly used the intra-class correlation coefficient (ICC) as a measure of test-retest reliability, which is scaled from 0 to 1—where 0 represents no reliability, and 1 represents perfect reliability. These studies have reported values on the order of 0.5 (Shehzad et al., 2009) to 0.6 (Zuo et al., 2010, Thomason et al., 2011, Guo et al., 2012), which reflect only modest test-retest reliability, thereby diminishing confidence in group contrasts, undermining generalizability of classification approaches, and confounding interpretation of subject-level results. The low test-retest reliability of rs-fMRI data is largely attributable to noise that remains in the data after preprocessing, wherein scanner artifacts, subject movements, and other noise sources induce non-neural temporal correlations in the blood oxygen level-dependent (BOLD) signal. Numerous preprocessing methods have been proposed to isolate and remove these confounds (Fox et al., 2009, Murphy et al., 2009, Weissenbacher et al., 2009, Braun et al., 2012, Friston et al., 1996; Power et al., 2012, Power et al., 2013, Power et al., 2014, Satterthwaite et al., 2013, Siegel et al., 2014, Yan et al., 2013a).
The present study seeks to comprehensively and simultaneously compare various preprocessing strategies, and assess their effects on multiple outcome measures: signal-noise separation, test-retest reliability, and group discriminability. Previous studies attempting to optimize rs-fMRI data have focused exclusively on one of these metrics without consideration as to how other aspects of data quality are affected. In particular, studies examining test-retest reliability of rs-fMRI data do not quantify how much noise remains in the data, or how maximizing this measure affects the ability to distinguish different groups of subjects (Shehzad et al., 2009, Zuo et al., 2010, Thomason et al., 2011, Guo et al., 2012, Yan et al., 2013b).
We focus our analyses on the effects of common preprocessing steps, such as global signal regression (GS) (Weissenbacher et al., 2009, Shirer et al., 2012); removal of cerebrospinal fluid (CSF) and white matter (WM) confounds (Shirer et al., 2012); noise regression of motion parameters estimated during motion correction (Friston et al., 1996, Power et al., 2012, Power et al., 2013, Satterthwaite et al., 2013, Yan et al., 2013a); and temporal filtering at various frequency bands reported in the literature (Achard et al., 2006, Ko et al., 2011, Guo et al., 2012). To maximize generalizability of our results, we elect not to examine preprocessing techniques that are unavailable at some imaging centers, such as removal of physiological noise using RETROICOR or RVHRCOR (Chang and Glover, 2009).
We analyze the rs-fMRI data using two measures of functional connectivity (FC): Pearson’s correlation between regions of interest (ROI) pairs, and ICA (dual regression, or DR). We then assess the quality of the outcome data based on three metrics. First, we propose a novel method of estimating signal-noise separation (SNS) in rs-fMRI data. Here, we calculate the functional connectivity between brain areas within well-established networks, and contrast this with spurious connectivity between these areas and non-neural regions outside the brain. This procedure is performed for each preprocessing permutation to identify the preprocessing parameters that produce the greatest ratio of network connectivity to noise. Second, we assess the impact of preprocessing parameters on the test-retest reliability (TRT) of whole-brain connectivity. We do so by measuring the TRT of network connectivity produced by each preprocessing pipeline, using the same intra-class correlation (ICC) metric as reported in previous rs-fMRI test-retest reliability studies (Shehzad et al., 2009, Zuo et al., 2010, Thomason et al., 2011, Guo et al., 2012). The effects of preprocessing on SNS and TRT are first evaluated on data collected by us, and then examined on an independent dataset that has been widely used in previous test-retest reliability studies for comparison (Shehzad et al., 2009, Zuo et al., 2010, Thomason et al., 2011, Guo et al., 2012). Finally, we assess the clinical relevance of different preprocessing pipelines by examining group discriminability (GD), wherein we compare different pipelines’ accuracy in classification of Alzheimer’s disease (AD) patients and controls. To ensure that the preprocessing strategies most successful for classifying disease states are not simply due to non-neural artifacts (e.g. differences in the amount of movement between groups), we also explore the effect of different preprocessing pipelines on the ability to classify different cognitive states within individual subjects.
Section snippets
Stanford University data
Data were compiled from three datasets, resulting in a total of thirty-eight healthy right-handed subjects (21 females) aged 18-78, and fifteen subjects with mild AD (8 females) aged 51-86 who participated in this study. The first Stanford healthy control dataset (S-HC1) contained twenty-three healthy right-handed subjects (15 females) aged 56-76. The second Stanford healthy control dataset (S-HC2) contained fifteen healthy right-handed subjects (6 females) aged 22-46. The third dataset, S-AD,
Results
A multiple regression analysis across 120 pipeline combinations revealed a significant effect of preprocessing parameters on all three metrics of data quality. This was true for both types of functional connectivity analyses (Table 1A).
Discussion
The present study examines numerous common preprocessing strategies, synthesizing the unique and aggregate effects of these techniques across multiple metrics of rs-fMRI data quality. Many previous studies assessing rs-fMRI data quality focus on a single preprocessing strategy, such as motion artifact removal (Power et al., 2014), or a single metric of data quality, such as reliability (Shehzad et al., 2009). However, some degree of variability may be attributable to biologically meaningful
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements
This work was supported by the National Institutes of Health (NS073498). We thank Jonas Richiardi, Andre Altmann, and Jeffrey Bernstein for helpful discussions and advice.
References (61)
- et al.
Voxel-based morphometry–the methods
NeuroImage
(2000) - et al.
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
NeuroImage
(2007) - et al.
Identification of large-scale networks in the brain using fMRI
NeuroImage
(2006) - et al.
Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures
NeuroImage
(2012) - et al.
Anticorrelations in resting state networks without global signal regression
NeuroImage
(2012) - et al.
Effects of model-based physiological noise correction on default mode network anti-correlations and correlations
NeuroImage
(2009) - et al.
Filtering induces correlation in fMRI resting state data
NeuroImage
(2013) Spiral imaging in fMRI
NeuroImage
(2012)- et al.
Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus
Biol. Psychiatry
(2007) - et al.
Improved optimization for the robust and accurate linear registration and motion correction of brain images
NeuroImage
(2002)
Functional connectivity in Alzheimer's disease: measurement and meaning
Biol. Psychiatry
Independent component analysis of nondeterministic fMRI signal sources
NeuroImage
Modelling large motion events in fMRI studies of patients with epilepsy
Magn. Reson. Imaging
The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?
NeuroImage
CORSICA: correlation of structured noise in fMRI by automatic identification of ICA components
Magn. Reson. Imaging
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
NeuroImage
Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp
NeuroImage
Methods to detect, characterize, and remove artifact in resting-state fMRI
NeuroImage
Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers
NeuroImage
An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data
NeuroImage
Neurodegenerative diseases target large-scale human brain networks
Neuron
The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework
NeuroImage
Resting-state fMRI can reliably map neural networks in children
NeuroImage
Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies
NeuroImage
A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics
NeuroImage
Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes
NeuroImage
Reliable intrinsic connectivity networks: Test-retest evaluation using ICA and dual regression approach
NeuroImage
A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs
J. Neurosci.
Non-linear registration, aka Spatial normalisation
Investigations into resting-state connectivity using independent component analysis
Philos. Trans. R. Soc. Lond. B Biol. Sci.
Cited by (149)
Delineating disorder-general and disorder-specific dimensions of psychopathology from functional brain networks in a developmental clinical sample
2023, Developmental Cognitive NeuroscienceStatistical power in network neuroscience
2023, Trends in Cognitive Sciences
- 1
Authors contributed equally to this work.