PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

Churchill, Nathan W.; Yourganov, Grigori; Spring, Robyn; Rasmussen, Peter Mondrup; Lee, Wayne; Ween, Jon Erik; Strother, Stephen C.

doi:10.1016/j.neuroimage.2011.08.021

Cited by 26 publications

(19 citation statements)

References 54 publications

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“…Finally, beyond PCA- and ICA-based methods, Churchill et al (2012c, 2013) developed a multivariate framework for physiological noise correction based on an adaptation of canonical correlation analysis (CCA), termed Physiological Correction using Canonical Autocorrelation Analysis (PHYCAA). The initial version of the PHYCAA algorithm identifies high frequency autocorrelated physiological noise sources with reproducible spatial structure in task-based fMRI (Churchill et al, 2012c).…”

Section: Denoising Physiological-related Noise: Cardiac Respiratimentioning

confidence: 99%

“…The initial version of the PHYCAA algorithm identifies high frequency autocorrelated physiological noise sources with reproducible spatial structure in task-based fMRI (Churchill et al, 2012c). The selection of the physiological noise components was constrained to those with more than 50% of the power spectrum above 0.1 Hz.…”

Section: Denoising Physiological-related Noise: Cardiac Respiratimentioning

confidence: 99%

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Methods for cleaning the BOLD fMRI signal

2017

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Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

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Section: Denoising Physiological-related Noise: Cardiac Respiratimentioning

confidence: 99%

Section: Denoising Physiological-related Noise: Cardiac Respiratimentioning

confidence: 99%

Methods for cleaning the BOLD fMRI signal

2017

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“…Independent Component Analysis (ICA) is also commonly used for fMRI de-noising by separating multiple signal sources, associated with processes such as scanner artifacts, physiological noise and brain activity (Beckmann and Smith, 2004; Brooks et al, 2008). Non-neuronal fluctuations are usually identified manually or resorting to automatic classification tools (Churchill et al, 2012; De Martino et al, 2007; Salimi-Khorshidi et al, 2014; Tohka et al, 2008). …”

Section: Discussionmentioning

confidence: 99%

Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects

et al. 2017

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Several strategies have been proposed to model and remove physiological noise from resting-state fMRI (rs-fMRI) data, particularly at ultrahigh fields (7 Tesla), including contributions from respiratory volume (RV) and heart rate (HR) signal fluctuations. Recent studies suggest that these contributions are highly variable across subjects and that physiological noise correction may thus benefit from optimization at the subject or even voxel level. Here, we systematically investigated the impact of the degree of spatial specificity (group, subject, newly proposed cluster, and voxel levels) on the optimization of RV and HR models. For each degree of spatial specificity, we measured the fMRI signal variance explained (VE) by each model, as well as the functional connectivity underlying three well-known resting-state networks (RSNs) obtained from the fMRI data after removal of RV+HR contributions. Whole-brain, high-resolution rs-fMRI data were acquired from twelve healthy volunteers at 7 Tesla, while simultaneously recording their cardiac and respiratory signals. Although VE increased with spatial specificity up to the voxel level, the accuracy of functional connectivity measurements improved only up to the cluster level, and subsequently decreased at the voxel level. This suggests that voxelwise modeling over-fits to local fluctuations with no physiological meaning. In conclusion, our results indicate that 7 Tesla rs-fMRI connectivity measurements improve if a cluster-based physiological noise correction approach is employed in order to take into account the individual spatial variability in the HR and RV contributions.

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“…Due to the long repetition time (TR, see below) of standard BOLD EPI acquisitions (2–3 s) the fluctuations are aliased into low-frequency signals which may be mistaken for neural activity-related BOLD oscillations, especially on rs-fMRI (Birn, 2012; Murphy et al, 2013; Cordes et al, 2014). A number of strategies have been used in an attempt to reduce these artifacts, and include the use of band-stop filtering, dynamic retrospective filtering (Särkkä et al, 2012), image-based methods (RETROICOR; Glover et al, 2000), corrections based on canonical correlation analysis (Churchill et al, 2012c) and through the use of externally recorded cardiac and respiratory waveforms as regressors (Falahpour et al, 2013). …”

Section: Data Acquisition Techniques and Artifactsmentioning

confidence: 99%

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

et al. 2016

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Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

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PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

Cited by 26 publications

References 54 publications

Methods for cleaning the BOLD fMRI signal

Methods for cleaning the BOLD fMRI signal

Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

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