Comparison of PCA vs KPCA for physiological noise removal in resting state FMRI

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

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Physiological noise model comparison for resting-state fMRI at 7 T

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi.2016.7493434 ◽

2016 ◽

Cited By ~ 1

Author(s):

Sandro Nunes ◽

Marta Bianciardi ◽

Afonso Dias ◽

Luis M. Silveira ◽

Lawrence L. Wald ◽

...

Keyword(s):

Resting State ◽

Model Comparison ◽

Resting State Fmri ◽

Noise Model ◽

Physiological Noise

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Detection of physiological noise in resting state fMRI using machine learning

Human Brain Mapping ◽

10.1002/hbm.21487 ◽

2011 ◽

Vol 34 (4) ◽

pp. 985-998 ◽

Cited By ~ 6

Author(s):

Tom Ash ◽

John Suckling ◽

Martin Walter ◽

Cinly Ooi ◽

Claus Tempelmann ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Resting State Fmri ◽

Physiological Noise

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Evaluating denoising strategies in resting-state fMRI in traumatic brain injury (EpiBioS4Rx)

10.1101/2021.12.10.472139 ◽

2021 ◽

Author(s):

Marina Weiler ◽

Raphael Fernandes Casseb ◽

Brunno Machado de Campos ◽

Julia Sophia Crone ◽

Evan S Lutkenhoff ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Resting State Fmri ◽

Clinical Population ◽

Quality Data ◽

Physiological Noise ◽

Quality Control Metrics

Objective: Resting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity of traumatic brain injury patients. Methods: We applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 traumatic brain injury patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial (EpiBioS4Rx). Pipelines were evaluated by three quality control metrics across three exclusion regimes based on the participant's head movement profile. Results: While no pipeline eliminated noise effects on functional connectivity, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data driven methods performed comparatively worse. Conclusion: In this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related functional connectivity in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as traumatic brain injury datasets.

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Controlling for the effect of arterial-CO2 fluctuations in resting-state fMRI: Comparing end-tidal CO2 clamping and retroactive CO2 correction

10.1101/2020.02.12.945881 ◽

2020 ◽

Author(s):

Ali M. Golestani ◽

J. Jean Chen

Keyword(s):

Resting State ◽

Grey Matter ◽

Brain Connectivity ◽

Free Breathing ◽

Resting State Fmri ◽

Heart Beat ◽

Physiological Noise ◽

Intrinsic Connectivity ◽

End Tidal ◽

Global Brain

AbstractThe BOLD signal, as the basis of functional MRI, arises from both neuronal and vascular factors, with their respective contributions to resting state-fMRI still unknown. Among the factors contributing to “physiological noise”, dynamic arterial CO2 fluctuations constitutes the strongest and the most widespread modulator of the grey-matter rs-fMRI signal. Some important questions are: (1) if we were able to clamp arterial CO2 such that fluctuations are removed, what would happen to rs-fMRI measures? (2) falling short of that, is it possible to retroactively correct for CO2 effects with equivalent outcome? In this study 13 healthy subjects underwent two rs-fMRI acquisition: During the “clamped” run, end-tidal CO2 (PETCO2) is clamped to the average PETCO2 level of each participant, while during the “free-breathing” run, the PETCO2 level is passively monitored but not controlled. PETCO2 correction was applied to the free-breathing data by convolving PETCO2 with its BOLD response function, and then regressing out the result. We computed the BOLD resting-state fluctuation amplitude (RSFA), as well as seed-independent mean functional connectivity (FC) as the weighted global brain connectivity (wGBC). Furthermore, connectivity between conditions were compared using coupled intrinsic-connectivity distribution (ICD) method. We ensured that PETCO2 clamping did not significantly alter heart-beat and respiratory variation. We found that neither PETCO2 clamping nor correction produced significant change in RSFA and wGBC. In terms of the ICD, PETCO2 clamping and correction both reduced FC strength in the majority of grey matter regions, although the effect of PETCO2 correction is considerably smaller than the effect of PETCO2 clamping. Furthermore, while PETCO2 clamping reduced inter-subject variability in FC, PETCO2 correction increased the variability. Overall PETCO2 correction is not the equivalent of PETCO2 clamping, although it shifts FC values towards the same direction as clamping does.

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Adaptive filtering for removing nonstationary physiological noise from resting state fMRI BOLD signals

2011 11th International Conference on Intelligent Systems Design and Applications ◽

10.1109/isda.2011.6121661 ◽

2011 ◽

Author(s):

Paolo Piaggi ◽

Danilo Menicucci ◽

Claudio Gentili ◽

Giacomo Handjaras ◽

Marco Laurino ◽

...

Keyword(s):

Adaptive Filtering ◽

Resting State ◽

Resting State Fmri ◽

Physiological Noise

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An open database of resting-state fMRI in awake rats

10.1101/842807 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yikang Liu ◽

Pablo D. Perez ◽

Zilu Ma ◽

Zhiwei Ma ◽

David Dopfel ◽

...

Keyword(s):

Resting State ◽

Brain Function ◽

High Spatial Resolution ◽

Resting State Fmri ◽

Psychiatric Research ◽

Functional Magnetic Resonance ◽

Physiological Noise ◽

Rodent Brain ◽

Health And Disease ◽

Human Neuroimaging

AbstractRodent models are essential to translational research in health and disease. Investigation in rodent brain function and organization at the systems level using resting-state functional magnetic resonance imaging (rsfMRI) has become increasingly popular, owing to its high spatial resolution and whole-brain coverage. Due to this rapid progress, shared rodent rsfMRI databases can be of particular interest and importance to the scientific community, as inspired by human neuroscience and psychiatric research that are substantially facilitated by open human neuroimaging datasets. However, such databases in rats are still lacking. In this paper, we share an open rsfMRI database acquired in 90 rats with a well-established awake imaging paradigm that avoids anesthesia interference. Both raw and preprocessed data are made publically available. Procedures in data preprocessing to remove artefacts induced by the scanner, head motion, non-neural physiological noise are described in details. We also showcase inter-regional functional connectivity and functional networks calculated from the database.

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Impact of physiological noise in characterizing the functional MRI default-mode network in Alzheimer’s disease

Journal of Cerebral Blood Flow & Metabolism ◽

10.1177/0271678x19897442 ◽

2020 ◽

Vol 41 (1) ◽

pp. 166-181 ◽

Cited By ~ 1

Author(s):

Yi-Tien Li ◽

Chun-Yuan Chang ◽

Yi-Cheng Hsu ◽

Jong-Ling Fuh ◽

Wen-Jui Kuo ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Default Mode Network ◽

Resting State ◽

Physiological Responses ◽

Resting State Fmri ◽

Fmri Data ◽

Physiological Noise ◽

Default Mode ◽

Heart Beating ◽

Healthy Participants

The functional connectivity of the default-mode network (DMN) monitored by functional magnetic resonance imaging (fMRI) in Alzheimer's disease (AD) patients has been found weaker than that in healthy participants. Since breathing and heart beating can cause fluctuations in the fMRI signal, these physiological activities may affect the fMRI data differently between AD patients and healthy participants. We collected resting-state fMRI data from AD patients and age-matched healthy participants. With concurrent cardiac and respiratory recordings, we estimated both physiological responses phase-locked and non-phase-locked to heart beating and breathing. We found that the cardiac and respiratory physiological responses in AD patients were 3.00 ± 0.51 s and 3.96 ± 0.52 s later (both p < 0.0001) than those in healthy participants, respectively. After correcting the physiological noise in the resting-state fMRI data by population-specific physiological response functions, the DMN estimated by seed-correlation was more localized to the seed region. The DMN difference between AD patients and healthy controls became insignificant after suppressing physiological noise. Our results indicate the importance of controlling physiological noise in the resting-state fMRI analysis to obtain clinically related characterizations in AD.

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