↓ Skip to main content

Physiological Noise in Brainstem fMRI

Overview of attention for article published in Frontiers in Human Neuroscience, January 2013
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Citations

dimensions_citation
200 Dimensions

Readers on

mendeley
275 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Physiological Noise in Brainstem fMRI
Published in
Frontiers in Human Neuroscience, January 2013
DOI 10.3389/fnhum.2013.00623
Pubmed ID
Authors

Jonathan C. W. Brooks, Olivia K. Faull, Kyle T. S. Pattinson, Mark Jenkinson

Abstract

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.

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 275 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
Germany 3 1%
United Kingdom 3 1%
Switzerland 1 <1%
France 1 <1%
Portugal 1 <1%
Japan 1 <1%
Netherlands 1 <1%
Unknown 260 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 59 21%
Student > Ph. D. Student 58 21%
Student > Master 33 12%
Student > Bachelor 19 7%
Other 16 6%
Other 51 19%
Unknown 39 14%
Readers by discipline Count As %
Neuroscience 58 21%
Medicine and Dentistry 43 16%
Psychology 36 13%
Engineering 35 13%
Agricultural and Biological Sciences 12 4%
Other 30 11%
Unknown 61 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 October 2013.
All research outputs
#15,043,783
of 24,323,543 outputs
Outputs from Frontiers in Human Neuroscience
#4,451
of 7,457 outputs
Outputs of similar age
#173,867
of 289,355 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#570
of 860 outputs
Altmetric has tracked 24,323,543 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,457 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 289,355 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 860 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.