The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal

Abstract

Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygenlevel-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single-and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.

Neural evidence for competition-mediated suppression in the perception of a single object

“…We acknowledge that the BOLD signal is an indirect measure of neural activity; previous work has shown that the BOLD signal correlates most highly with local field potentials, which can arise from either excitatory or inhibitory synaptic events (Logothetis & Wandell, 2004;Logothetis, 2002). A reduction in the observed signal can therefore arise in multiple ways, including (but not limited to) a decrease in inhibitory interneuron activity, a decrease in inter-regional recurrent activity, or a decrease in excitatory activity.…”

A bilateral cortico-bulbar network associated with breath holding in humans, determined by functional magnetic resonance imaging

“…The BOLD signal is derived from changes in oxygenation within venous blood and is an indirect measure of neuronal activity; recent studies suggest that it is the increased metabolic activity at presynaptic terminals that drives the fMRI signal (Logothetis, 2002;Viswanathan and Freeman, 2007). The BOLD signal is dependent on blood flow, blood volume and blood oxygenation and is sensitive to non specific factors acting throughout the body such as fluctuations in PCO 2 and PO 2 , which means that the study of breath holding with fMRI is potentially problematic, even breath holds of a short duration (Kastrup et al, 1998;Liu et al, 2002).…”

Modeling the hemodynamic response to brain activation

“…In fact, there is no consensus on exactly which aspect of neural activity drives the hemodynamic response, and this is an active area of research. Experimental studies comparing electrophysiological measurements with BOLD and CBF changes have found that the hemodynamic responses correlate better with local mean field potential, rather then local spiking rates, suggesting that the hemodynamic response is dominantly driven by input synaptic activity rather than output spiking activity (Lauritzen, 2001;Lauritzen and Gold, 2003;Logothetis, 2002;Logothetis et al, 2001). Theoretical analyses of the energy budget for neuronal signaling provide some support for this picture as well (Attwell and Laughlin, 2001;Lennie, 2003).…”