Physiological Noise in Brainstem fMRI
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 cerebrospina...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-10-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00623/full |
| _version_ | 1818264281028755456 |
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| author | Jonathan Charles William Brooks Olivia Kate Faull Kyle T S Pattinson Mark eJenkinson |
| author_facet | Jonathan Charles William Brooks Olivia Kate Faull Kyle T S Pattinson Mark eJenkinson |
| author_sort | Jonathan Charles William Brooks |
| collection | DOAJ |
| description | 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 3T and 7T is presented. Data driven approaches based such as independent component analysis (ICA) are described. MR acquisition strategies that attempt to either minimise the influence of physiological fluctuations on recorded fMRI data, or provide additional information to correct for their presence, will be mentioned. General advice on modelling 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 modelling are presented. |
| first_indexed | 2024-12-12T19:32:25Z |
| format | Article |
| id | doaj.art-ecbf0eca73e8423f97f11ecb966c1c64 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-12T19:32:25Z |
| publishDate | 2013-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-ecbf0eca73e8423f97f11ecb966c1c642022-12-22T00:14:23ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612013-10-01710.3389/fnhum.2013.0062359364Physiological Noise in Brainstem fMRIJonathan Charles William Brooks0Olivia Kate Faull1Kyle T S Pattinson2Mark eJenkinson3University of BristolUniversity of OxfordUniversity of OxfordUniversity of OxfordThe 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 3T and 7T is presented. Data driven approaches based such as independent component analysis (ICA) are described. MR acquisition strategies that attempt to either minimise the influence of physiological fluctuations on recorded fMRI data, or provide additional information to correct for their presence, will be mentioned. General advice on modelling 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 modelling are presented.http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00623/fullfMRIimagingbrainstemphysiological noise7T |
| spellingShingle | Jonathan Charles William Brooks Olivia Kate Faull Kyle T S Pattinson Mark eJenkinson Physiological Noise in Brainstem fMRI Frontiers in Human Neuroscience fMRI imaging brainstem physiological noise 7T |
| title | Physiological Noise in Brainstem fMRI |
| title_full | Physiological Noise in Brainstem fMRI |
| title_fullStr | Physiological Noise in Brainstem fMRI |
| title_full_unstemmed | Physiological Noise in Brainstem fMRI |
| title_short | Physiological Noise in Brainstem fMRI |
| title_sort | physiological noise in brainstem fmri |
| topic | fMRI imaging brainstem physiological noise 7T |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00623/full |
| work_keys_str_mv | AT jonathancharleswilliambrooks physiologicalnoiseinbrainstemfmri AT oliviakatefaull physiologicalnoiseinbrainstemfmri AT kyletspattinson physiologicalnoiseinbrainstemfmri AT markejenkinson physiologicalnoiseinbrainstemfmri |