Sensitivity of PPI analysis to differences in noise reduction strategies

Bartoň, Marek; Mareček, Radek; Rektor, Ivan; Filip, Pavel; Janousova, E.; Mikl, Michal

doi:10.1016/j.jneumeth.2015.06.021

Cited by 10 publications

(7 citation statements)

References 56 publications

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“…This suggests complex interactions between (generally weak) gPPI effects and data filtering, which is consistent with our previous work (Barton et al, ). The difference in sensitivity may be caused not only by noise reduction but also by useful signal variability manipulation—if we disrupt the seed neural signal, we can expect sensitivity reduction; if we regress out part of the (possibly task‐modulated) signals related to different neural networks, we can impose false gPPI effects to the regions where these effects were not originally present, or it can lead to a reduction of data variability that is relevant (i.e., of neural origin), and its removal will cause a higher sensitivity of current gPPI effect detection.…”

Section: Discussionsupporting

confidence: 92%

“…Noise management is important especially in connectivity analyses, that is, when computing seed functional connectivity (FC) (Weissenbacher et al, ), in graph theory analysis based on FC (Gargouri et al, ) and in analyses studying task‐modulated intrinsic fluctuations where noise management also plays a role, for example, in psychophysiological interactions (Barton et al, ). This is because the complicated spatiotemporal structure of noise (Jo et al, ) introduces (even long‐distance) correlations to the data, and hence, the connectivity estimate can be biased.…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

show abstract

“…Physiological noise correction was conducted for the brain and spinal cord (Brooks et al, 2008;Harvey et al, 2008) within FEAT, and as recommended for use in PPI analyses (Barton et al, 2015). Cardiac and respiratory phases were determined using a physiological noise model (PNM, part of FSL), and slice specific regressors determined for the entire CNS coverage.…”

Section: Pre-processing Of Functional Data and Single-subject Analysismentioning

confidence: 99%

Simultaneous brain, brainstem, and spinal cord pharmacological-fMRI reveals involvement of an endogenous opioid network in attentional analgesia

Oliva

Hartley-Davies

Moran

et al. 2022

eLife

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Pain perception is decreased by shifting attentional focus away from a threatening event. This attentional analgesia engages parallel descending control pathways from anterior cingulate (ACC) to locus coeruleus, and ACC to periaqueductal grey (PAG) - rostral ventromedial medulla (RVM), indicating possible roles for noradrenergic or opioidergic neuromodulators. To determine which pathway modulates nociceptive activity in humans we used simultaneous whole brain-spinal cord pharmacological-fMRI (N=39) across three sessions. Noxious thermal forearm stimulation generated somatotopic-activation of dorsal horn (DH) whose activity correlated with pain report and mirrored attentional pain modulation. Activity in an adjacent cluster reported the interaction between task and noxious stimulus. Effective connectivity analysis revealed that ACC interacts with PAG and RVM to modulate spinal cord activity. Blocking endogenous opioids with Naltrexone impairs attentional analgesia and disrupts RVM-spinal and ACC-PAG connectivity. Noradrenergic augmentation with Reboxetine did not alter attentional analgesia. Cognitive pain modulation involves opioidergic ACC-PAG-RVM descending control which suppresses spinal nociceptive activity.

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“…Thus, we emphasized the preliminary nature of the current findings and the results would need to be replicated in studies with more balanced sample size. Fourth, a recent study recommended including signals from white matter and CSF as additional regressors in gPPI analyses (Barton et al, 2015). With the new model, the IFG cluster was not significant at cluster-level p<0.05 FWE, while the vmPFC findings remain the same (Supplementary Figure 2).…”

Section: Limitations and Conclusionmentioning

confidence: 96%

Cue-elicited functional connectivity of the periaqueductal gray and tonic cocaine craving

Zhang

Zhornitsky

Wang

et al. 2020

Drug and Alcohol Dependence

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Sensitivity of PPI analysis to differences in noise reduction strategies

Cited by 10 publications

References 56 publications

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

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

Simultaneous brain, brainstem, and spinal cord pharmacological-fMRI reveals involvement of an endogenous opioid network in attentional analgesia

Cue-elicited functional connectivity of the periaqueductal gray and tonic cocaine craving

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