How ignoring physiological noise can bias the conclusions from fMRI simulation results

Welvaert, Marijke; Rosseel, Yves

doi:10.1016/j.jneumeth.2012.08.022

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…We use the neuRosim R package [ 47 ] and a canonical HRF to set up the first level activation [ 26 ] in ( 19 ). In total there are 1934 active voxels, distributed over two clusters, and 89191 inactive voxels in a 45 × 45 × 45 volume (±2.5% of the voxels).…”

Section: Simulationsmentioning

confidence: 99%

Evaluation of Second-Level Inference in fMRI Analysis

Roels

Loeys

Moerkerke

2016

Computational Intelligence and Neuroscience

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We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference.

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Section: Simulationsmentioning

confidence: 99%

Evaluation of Second-Level Inference in fMRI Analysis

Roels

Loeys

Moerkerke

2016

Computational Intelligence and Neuroscience

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“…CSF pulsation leads to large signal variations due to unsaturated spins moving into imaging slices, which cannot be ignored because the spinal cord is surrounded by CSF. It has been reported that tissue motion and CSF pulsation might lead to detection of blood oxygenation level‐dependent (BOLD) signal changes, which could generate false‐positive correlations in rsfMRI analysis.…”

mentioning

confidence: 99%

Robust spinal cord resting‐state fMRI using independent component analysis‐based nuisance regression noise reduction

Jin

et al. 2018

Magnetic Resonance Imaging

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“…First, the simulated data was generated by adding Gaussian noise to the ground truth signal, which did not account for any other type of physiological noise that may be present in the real data (e.g., colored noise). Adding physiological noise may affect the TPR, FPR, and AUC results (Welvaert and Rosseel, 2012). Second, the simulated datasets did not include other types of BOLD responses, such as nonstationary or biphasic responses (Fox et al, 2005a; Fox et al, 2005b; Gonzalez-Castillo et al, 2012; Harms and Melcher, 2002; Uludag, 2008).…”

Section: Discussionmentioning

confidence: 99%

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies

et al. 2017

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A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods’ performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data.

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How ignoring physiological noise can bias the conclusions from fMRI simulation results

Cited by 8 publications

References 40 publications

Evaluation of Second-Level Inference in fMRI Analysis

Evaluation of Second-Level Inference in fMRI Analysis

Robust spinal cord resting‐state fMRI using independent component analysis‐based nuisance regression noise reduction

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies

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