The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies

Abstract

There is growing evidence that several components of the mass neural activity contributing to the local field potential (LFP) can be partly separated by decomposing the LFP into nonoverlapping frequency bands. Although the blood oxygen level-dependent (BOLD) signal has been found to correlate preferentially with specific frequency bands of the LFP, it is still unclear whether the BOLD signal relates to the activity expressed by each LFP band independently of the others or if, instead, it also reflects specific relationships among different bands. We investigated these issues by recording, simultaneously and with high spatiotemporal resolution, BOLD signal and LFP during spontaneous activity in early visual cortices of anesthetized monkeys (Macaca mulatta). We used information theory to characterize the statistical dependency between BOLD and LFP. We found that the alpha (8 -12 Hz), beta (18 -30 Hz), and gamma (40 -100 Hz) LFP bands were informative about the BOLD signal. In agreement with previous studies, gamma was the most informative band. Both increases and decreases in BOLD signal reliably followed increases and decreases in gamma power. However, both alpha and beta power signals carried information about BOLD that was largely complementary to that carried by gamma power. In particular, the relationship between alpha and gamma power was reflected in the amplitude of the BOLD signal, while the relationship between beta and gamma bands was reflected in the latency of BOLD with respect to significant changes in gamma power. These results lay the basis for identifying contributions of different neural pathways to cortical processing using fMRI.

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

“…We report differential contributions of individual EEG frequency bands to the fMRI signal, as depicted by region-specific spectral profiles. Previous studies have suggested that local and network fMRI signals are associated with a mixture of electrophysiological processes underlying dynamic neuronal interactions at different temporal and spatial scales, which may be characterized by a varying interplay of distinct frequency components (Mantini et al 2007;Goense and Logothetis 2008;Scheeringa et al 2011;Magri et al 2012). Thus, the observed spectral profile may serve as an index, or "fingerprint" ) that reflects the relative contributions of the various distinct networks that contribute to a region's activity.…”

“…Part of this spectral range is accessible with EEG. Over a much longer time scale in the order of several seconds to tens of seconds, network activity presents as comodulations in the power envelop of specific spectral bands, to which neuronal oscillations contribute (Leopold et al 2003;Lu et al 2007;Nir et al 2008) and which have an fMRI correlate (Magri et al 2012). Although extensive efforts have been made to characterize these "fast" and "slow" network interactions separately, their relationship has rarely been studied and remains largely unknown.…”

“…This is achieved by combining spectral information from EEG with spatial information from fMRI based on a putative temporal coupling between the spontaneous fMRI signals and the power fluctuations of individual EEG spectral components (Goldman et al 2002;Laufs et al 2003;Leopold et al 2003;Moosmann et al 2003;Lu et al 2007;Mantini et al 2007;Goense and Logothetis 2008;Scheeringa et al 2011;Liu et al 2012;Magri et al 2012). Specifically, we developed a novel "subspace" analysis method to decompose each brain region's fMRI signal into multiple component time-series signals that were temporally correlated with distinct EEG spectral components.…”

Relationship altered between functional T1ρ and BOLD signals in bipolar disorder

“…The physiological source of BOLD signal has been well‐characterized by previous research and it is known to reflect changes in blood oxygenation that occur following coherent neuronal activity (Baumann et al., 2010; Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001; Magri, Schridde, Murayama, Panzeri, & Logothetis, 2012). However, a major limitation of BOLD imaging is that changes in blood flow are not a direct measurement of neural activity, likely requiring the involvement of astrocyte‐mediated signaling pathways (Rossi, 2006; Takano et al., 2006) and occurring approximately 4–6 s after neuronal activity (Baumann et al., 2010).…”

Functional localization of the human color center by decreased water displacement using diffusion‐weighted fMRI

“…2010; Magri et al. 2012). The high‐frequency gamma range has been associated with excitatory and inhibitory synaptic activity (Brunel and Wang 2003) and is believed to represent synchronous activity from large neural networks (Jensen et al.…”