Elsevier

Brain Research Reviews

Volume 62, Issue 2, March 2010, Pages 233-244
Brain Research Reviews

Review
How and when the fMRI BOLD signal relates to underlying neural activity: The danger in dissociation

https://doi.org/10.1016/j.brainresrev.2009.12.004Get rights and content

Abstract

Functional magnetic resonance imaging (fMRI) has become the dominant means of measuring behavior-related neural activity in the human brain. Yet the relation between the blood oxygen-level dependent (BOLD) signal and underlying neural activity remains an open and actively researched question. A widely accepted model, established for sensory neo-cortex, suggests that the BOLD signal reflects peri-synaptic activity in the form of the local field potential rather than the spiking rate of individual neurons. Several recent experimental results, however, suggest situations in which BOLD, spiking, and the local field potential dissociate. Two different models are discussed, based on the literature reviewed to account for this dissociation, a circuitry-based and vascular-based explanation. Both models are found to account for existing data under some testing situations and in certain brain regions. Because both the vascular and local circuitry-based explanations challenge the BOLD-LFP coupling model, these models provide guidance in predicting when BOLD can be expected to reflect neural processing and when the underlying relation with BOLD may be more complex than a direct correspondence.

Section snippets

Introduction: fMRI and cognitive neuroscience

Functional magnetic resonance imaging (fMRI) has become a mainstay of research in both clinical and cognitive neuroscience and is currently the dominant paradigm for assessing behavior-related brain physiological changes in humans. Yet there still remains much for us to understand about this relatively new methodology. Perhaps most importantly, we are still learning what aspect of neural processing the blood oxygen level-dependent (BOLD) response, the signal that forms the basis of fMRI,

Acknowledgments

Thanks to Eve Isham, Charan Ranganath, Andy Yonelinas, Yevgeniy Sirotin, Michael Kahana, and Andrew Watrous for constructive comments on the manuscript.

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