Physiological noise in brainstem fMRI
AuthorBrooks, JCW; Faull, OK; Pattinson, KTS; Jenkinson, M
Source TitleFrontiers in Human Neuroscience
PublisherFRONTIERS MEDIA SA
University of Melbourne Author/sJenkinson, Mark
AffiliationCentre for Neuroscience
Document TypeJournal Article
CitationsBrooks, J. C. W., Faull, O. K., Pattinson, K. T. S. & Jenkinson, M. (2013). Physiological noise in brainstem fMRI. FRONTIERS IN HUMAN NEUROSCIENCE, 7 (OCT), https://doi.org/10.3389/fnhum.2013.00623.
Access StatusOpen Access
The brainstem is directly involved in controlling blood pressure, respiration, sleep/wake cycles, pain modulation, motor, and cardiac output. As such it is of significant basic science and clinical interest. However, the brainstem's location close to major arteries and adjacent pulsatile cerebrospinal fluid filled spaces, means that it is difficult to reliably record functional magnetic resonance imaging (fMRI) data from. These physiological sources of noise generate time varying signals in fMRI data, which if left uncorrected can obscure signals of interest. In this Methods Article we will provide a practical introduction to the techniques used to correct for the presence of physiological noise in time series fMRI data. Techniques based on independent measurement of the cardiac and respiratory cycles, such as retrospective image correction (RETROICOR, Glover et al., 2000), will be described and their application and limitations discussed. The impact of a physiological noise model, implemented in the framework of the general linear model, on resting fMRI data acquired at 3 and 7 T is presented. Data driven approaches based such as independent component analysis (ICA) are described. MR acquisition strategies that attempt to either minimize the influence of physiological fluctuations on recorded fMRI data, or provide additional information to correct for their presence, will be mentioned. General advice on modeling noise sources, and its effect on statistical inference via loss of degrees of freedom, and non-orthogonality of regressors, is given. Lastly, different strategies for assessing the benefit of different approaches to physiological noise modeling are presented.
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