Principles of BOLD Functional MRI

  • Seong-Gi Kim
  • Peter A. Bandettini


Functional magnetic resonance imaging (fMRI) is one of the most important tools for visualizing neural activity in the human brain. The blood oxygenation level-dependent (BOLD) contrast has been most widely used for its easy implementation and high sensitivity. However, the BOLD signal is dependent on various anatomical, physiological, and imaging parameters, thus its interpretation with respect to physiological parameters is not straightforward. To understand the physiological source of the BOLD signal, measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV) changes are helpful. In this chapter, we discussed (1) various fMRI techniques, (2) the sources of the BOLD fMRI signals, (3) improvement of BOLD techniques, (4) contrast-to-noise consideration, and (5) spatial and temporal resolution. CBF can be measured using arterial spin-labeling MR methods, and CBV change can be detected using a vascular space occupancy-dependent technique. Conventional gradient-echo BOLD fMRI is sensitive to intravascular and extravascular signals of small and large venous vessels, while spin-echo BOLD fMRI is sensitive to intravascular signals of all-sized venous vessels and extravascular signals of small vessels. At high magnetic fields, intravascular signals can be reduced by shortening blood T 2 relative to tissue T 2. Thus, SE BOLD fMRI at high fields improves spatial specificity. Intrinsic spatial and temporal resolution of hemodynamic-based fMRI techniques is dependent on vascular structures and responses. Using fMRI, submillimeter functional structures can be mapped, and an order of second temporal resolution can be achieved. Overall, fMRI opened a window of basic and clinical neuroscience research.


Cerebral Blood Flow Cerebral Blood Volume Arterial Spin Label Bold Signal Magnetic Resonance Imaging Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Supported by the National Institutes of Health.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of RadiologyUniversity of PittsburghPittsburghUSA
  2. 2.Section on Functional Imaging Methods, Laboratory of Brain and CognitionNational Institutes of HealthBethesdaUSA

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