Modeling data acquisition and the effects of patient motion in magnetic resonance imaging

1991 ◽  
Author(s):  
Jonathan K. Riek ◽  
Warren E. Smith ◽  
A. Murat Tekalp ◽  
Kevin J. Parker ◽  
Theophano Mitsa ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Data Acquisition ◽  
Resonance Imaging ◽  
Patient Motion ◽  
Modeling Data

scholarly journals Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science

2016 ◽  
Vol 371 (1705) ◽  
pp. 20150349 ◽  
Author(s):  
Robert Turner
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Data Acquisition ◽  
Cerebral Blood Volume ◽  
Brain Activity ◽  
Blood Oxygenation ◽  
Human Cognition ◽  
Resonance Imaging ◽  
Functional Connections ◽  
Fundamental Limitations

When blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) was discovered in the early 1990s, it provoked an explosion of interest in exploring human cognition, using brain mapping techniques based on MRI. Standards for data acquisition and analysis were rapidly put in place, in order to assist comparison of results across laboratories. Recently, MRI data acquisition capabilities have improved dramatically, inviting a rethink of strategies for relating functional brain activity at the systems level with its neuronal substrates and functional connections. This paper reviews the established capabilities of BOLD contrast fMRI, the perceived weaknesses of major methods of analysis, and current results that may provide insights into improved brain modelling. These results have inspired the use of in vivo myeloarchitecture for localizing brain activity, individual subject analysis without spatial smoothing and mapping of changes in cerebral blood volume instead of BOLD activation changes. The apparent fundamental limitations of all methods based on nuclear magnetic resonance are also discussed. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

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