Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI

Soltysik, David A.; Thomasson, David; Rajan, Sunder S.; Biassou, Nadia

doi:10.1016/j.jneumeth.2014.11.015

Cited by 20 publications

(16 citation statements)

References 40 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We thus performed an additional analysis using PCA components derived from the entire brain, but observed similar results to those obtained from using WM/CSF derived components (cf Supplementary figures S8–S10). These results converge with those of Soltysik et al, (2015), which reveal that PCA extracted from whole brain yield similar results to those obtained from using WM and CSF regions. In summary, we would thus argue that PCA denoising has no beneficial effect on the test-retest reliability of RS-FC estimates, at least within the settings evaluated in this study.…”

Section: Discussionsupporting

confidence: 83%

“…II) PCA de-noising : It has been suggested (Behzadi et al 2007; Soltysik et al 2015), that computing a principal component analysis (PCA) decomposition across the WM and CSF regions of the brain and removing variance associated with the most dominant 5 components might remove many sources of artificial and confounding signals and hence increase the specificity of RS-FC results. We thus performed all analyses both with (PCA) and without (NoPCA) PCA de-noising.…”

Section: Methodsmentioning

confidence: 99%

“…Motion artifacts have been shown to produce spurious correlations in a systematic way (Van Dijk et al, 2012; Power et al, 2012; Satterthwaite et al, 2013), implying that the removal of motion related artifacts is a prerequisite for further analysis. Various approaches have been proposed for dealing with noise effects post-hoc, i.e., after the data have been acquired (Behzadi et al 2007; Fox et al 2009; Murphy et al 2009; Chai et al 2012; Griffanti et al 2014; Patriat et al 2015; Power et al 2015; Soltysik et al 2015; Wong et al 2016). Besides motion-related artifacts, one particular aspect that has received a lot of attention is the use of nuisance regressors reflecting global signals, either derived from the whole brain or from specific tissue types such as white-matter or cerebrospinal fluid.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

et al. 2016

View full text Add to dashboard Cite

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that grey matter masking improved the reliability of connectivity estimates, whereas de-noising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test-retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources.

show abstract

Section: Discussionsupporting

confidence: 83%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

et al. 2016

View full text Add to dashboard Cite

show abstract

“…In order to account for non-stimulus dependent noise variance we included principle component regressors as obtained from the CompCor method which has been shown to account for physiological noise (e.g. respiration and heart rate) without direct measurements (Soltysik et al, 2015). We also added 6 motion parameters (roll, pitch, yaw, x, y, z) into each regression model.…”

Section: Methodsmentioning

confidence: 99%

“…We then modeled the hemodynamic response for each condition in the reading experiment (Word, Checkerboard, and Fixation). A principal components analysis (PCA) was used to remove estimates of physiological noise as defined by the CompCor method (as described in Soltysik et al, 2015), and the 6 motion parameters (roll, pitch, yaw, x, y, z) were entered as regressors of no-interest. Then, we performed a whole brain contrast for Read > Fix (uncorrected threshold p < 0.001) which was masked inclusively by the Read > Checker contrast (uncorrected p = 0.05) in each subject.…”

Section: Methodsmentioning

confidence: 99%

Shared orthographic neuronal representations for spelling and reading

Purcell¹,

Jiang

Eden³

2017

NeuroImage

View full text Add to dashboard Cite

A central question in the study of the neural basis of written language is whether reading and spelling utilize shared orthographic representations. While recent studies employing fMRI to test this question report that the left inferior frontal gyrus (IFG) and ventral occipitotemporal cortex (vOTC) are active during both spelling and reading in the same subjects (Purcell et al., 2011a; Rapp and Lipka, 2011), the spatial resolution of fMRI limits the interpretation of these findings. Specifically, it is unknown if the neurons which encode orthography for reading are also involved in spelling of the same words. Here we address this question by employing an event-related functional magnetic resonance imaging-adaptation (fMRI-A) paradigm designed to examine shared orthographic representations across spelling and reading. First, we identified areas that independently showed adaptation to reading, and adaptation to spelling. Then we identified spatial convergence for these two separate maps via a conjunction analysis. Consistent with previous studies (Purcell et al., 2011a; Rapp and Lipka, 2011), this analysis revealed the left dorsal IFG, vOTC and supplementary motor area. To further validate these observations, we then interrogated these regions using an across-task adaptation technique, and found adaptation across reading and spelling in the left dorsal IFG (BA 44/9). Our final analysis focused specifically on the Visual Word Form Area (VWFA) in the vOTC, whose variability in location among subjects requires the use of subject-specific identification mechanisms (Glezer and Riesenhuber, 2013). Using a functional localizer for reading, we defined the VWFA in each subject, and found adaptation effects for both within the spelling and reading conditions, respectively, as well as across spelling and reading. Because none of these effects were observed during a phonological/semantic control condition, we conclude that the left dorsal IFG and VWFA are involved in accessing the same orthography-specific representations for spelling and reading.

show abstract

Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation

Bartoň

Mareček

Krajcovicova

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data‐driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal‐to‐noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal–noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting‐state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR‐corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.

show abstract

Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI

Cited by 20 publications

References 40 publications

Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

Shared orthographic neuronal representations for spelling and reading

Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation

Contact Info

Product

Resources

About