Detection of physiological noise in resting state fMRI using machine learning

Ash, Tom; Suckling, John; Walter, Martin; Ooi, Cinly; Tempelmann, Claus; Carpenter, Adrian; Williams, Guy B.

doi:10.1002/hbm.21487

Cited by 8 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With a similar goal to PESTICA, a multiclass support vector machine classifier can also be used to assign each fMRI volume to a certain bin within the physiological phase cycle and thereby predict the cardiac and respiratory phase time series from the fMRI data every TR. These physiological phase time series can then be incorporated into RETROICOR (Ash et al, 2013). …”

Section: Denoising Physiological-related Noise: Cardiac Respiratimentioning

confidence: 99%

Methods for cleaning the BOLD fMRI signal

2017

View full text Add to dashboard Cite

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.

show abstract

Section: Denoising Physiological-related Noise: Cardiac Respiratimentioning

confidence: 99%

Methods for cleaning the BOLD fMRI signal

2017

View full text Add to dashboard Cite

show abstract

“…Several previous studies have proposed methods for reconstructing cyclic cardiac and respiratory phase information from fMRI itself, using techniques that leverage sub-second fMRI acquisition rates and/or slice-timing offsets ( Agrawal et al, 2020 ; Ash et al, 2013 ; Aslan et al, 2019 ; Beall and Lowe, 2007 ). With regard to low-frequency physiology, Tong and Frederick (2014) and Tong et al (2017) revealed dynamic fMRI patterns linked with cerebral circulation, and which correlated with low-frequency hemoglobin oscillations at the fingertip ( Tong et al, 2012 ), providing a data-driven approach for tracking BOLD hemodynamics of systemic origin.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of respiratory variation signals from fMRI data

et al. 2021

View full text Add to dashboard Cite

Functional MRI signals can be heavily influenced by systemic physiological processes in addition to local neural activity. For example, widespread hemodynamic fluctuations across the brain have been found to correlate with natural, low-frequency variations in the depth and rate of breathing over time. Acquiring peripheral measures of respiration during fMRI scanning not only allows for modeling such effects in fMRI analysis, but also provides valuable information for interrogating brain-body physiology. However, physiological recordings are frequently unavailable or have insufficient quality. Here, we propose a computational technique for reconstructing continuous low-frequency respiration volume (RV) fluctuations from fMRI data alone. We evaluate the performance of this approach across different fMRI preprocessing strategies. Further, we demonstrate that the predicted RV signals can account for similar patterns of temporal variation in resting-state fMRI data compared to measured RV fluctuations. These findings indicate that fluctuations in respiration volume can be extracted from fMRI alone, in the common scenario of missing or corrupted respiration recordings. The results have implications for enriching a large volume of existing fMRI datasets through retrospective addition of respiratory variations information.

show abstract

“…Due to the inherent complexity of the problems under consideration (e.g., biological vs. clinical homogeneity of subject groups), current experiments are more often focused on non-clinical research questions as proofs-of-concept. Among the toy research questions more often tackled are classification of sex (Wang et al, 2012 ), age (Dosenbach et al, 2010 ; Vergun et al, 2013 ), or other clearly identifiable target variables (Tagliazucchi et al, 2012 ; Ash et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

On the generalizability of resting-state fMRI machine learning classifiers

Huf

Kalcher

Boubela

et al. 2014

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generalizability to other subject samples, limiting the use of such published classifiers in other research projects. We conducted a simulation study using publicly available resting-state fMRI data from the 1000 Functional Connectomes and COBRE projects to examine the generalizability of classifiers based on regional homogeneity of resting-state time series. While classification accuracies of up to 0.8 (using sex as the target variable) could be achieved on test datasets drawn from the same study as the training dataset, the generalizability of classifiers to different study samples proved to be limited albeit above chance. This shows that on the one hand a certain amount of generalizability can robustly be expected, but on the other hand this generalizability should not be overestimated. Indeed, this study substantiates the need to include data from several sites in a study investigating machine learning classifiers with the aim of generalizability.

show abstract

Detection of physiological noise in resting state fMRI using machine learning

Cited by 8 publications

References 40 publications

Methods for cleaning the BOLD fMRI signal

Methods for cleaning the BOLD fMRI signal

Reconstruction of respiratory variation signals from fMRI data

On the generalizability of resting-state fMRI machine learning classifiers

Contact Info

Product

Resources

About