Neuronal correlate of BOLD signal fluctuations at rest: err on the side of the baseline.

F unctional MRI (fMRI) indirectly measures changes in neuronal activity because the blood oxygenation level-dependent (BOLD) signal is sensitive to changing concentrations of oxyhemoglobin (vs. deoxyhemoglobin) to support functional energy demand (1). Changes in the BOLD signal are usually interpreted from an unspecified baseline state (2). However, there is no true baseline because the brain is never actually at rest (3), measured either in terms of neuronal activity (4) or the energy that activities demand (5). Recently, however, investigators have begun to study the resting state using fMRI, but interpretation remains controversial because of questions about the relationship between the BOLD signal and neuronal activity. In PNAS, Schölvinck et al. (6) tackle this controversy by correlating slow modulations of neuronal activity in the resting state with spontaneous fluctuations in the BOLD signal. Neuroscientists typically use fMRI with task-based paradigms (or T-fMRI), in which the mean of the baseline state is subtracted from the mean of the stimulated state to unveil activated (or deactivated) regions associated with the task (1). T-fMRI experiments in the human brain generally report small evoked changes in the BOLD signal, which peak within ∼6 s after task onset. The magnitude of the evoked BOLD response varies with the task type (e.g., sensory and cognitive) and with the cortical area (i.e., >1% and <1%, respectively, in primary sensory and highorder areas). However, fMRI is also used to study the brain at rest in the absence of any explicit task. Biswal et al. (7) observed that resting human brain fMRI data contain high-amplitude (∼1%), lowfrequency (<0.1 Hz) fluctuations in the spontaneous BOLD signal that are temporally correlated across vast spans of cerebral cortex. In the resting-state fMRI paradigm (or R-fMRI), in which the data are analyzed for spatiotemporal coherence to reveal correlated networks, a preprocessing step is used to regress out contributions from the global BOLD signal fluctuations (8). This process presumably eliminates “noise” from nonneuronal sources (9) and/or uncorrelated neuronal activities. The remaining smaller (or filtered) fluctuations in the spontaneous BOLD signal facilitate detection of network-level correlations (10). These tiny fluctuations in the spontaneous BOLD signal are often assigned to neuronal activity that supports networks (e.g., the default mode) and are believed to be representative of resting brain function rather than the total neuronal activity that characterizes the baseline state (2). If, however, there is a significant correlated neuronal component associated with the global BOLD signal, important information about resting-state brain connectivity is being discarded by this process. Schölvinck et al. (6) demonstrate that the global component of BOLD signal fluctuations measured at rest is indeed tightly coupled with a slow modulation of neuronal events that appear to be ubiquitous in the cerebral cortex. They thereby recommend caution when arbitrarily removing the global BOLD signal because in doing so a global correlate of the brain’s baseline neuronal activity is thrown away, which in turn may affect regions that are defined as either correlated or anticorrelated. Thus, this study has strong ramifications for interpretation of default mode or other networks using R-fMRI data that are assigned on the basis of the removal of the global average fluctuations. In addition, it is also likely that this study may impact interpretation of T-fMRI data in cases in which task-specific BOLD signal increases (or decreases) are detected from the baseline state because the evoked changes (e.g., with cognitive tasks) may be on the order of the spontaneous fluctuations. Although correlations between slow (<0.1 Hz) modulations of ongoing neuronal activity, as measured by local field potential (LFP) or multiunit activity (MUA), and fluctuations of the resting BOLD signal have been previously reported both locally near the microelectrode and extending over regions of the visual cortex (11), Schölvinck et al. (6) show that these correlations extend over nearly the entire cortical surface with a correlation strength that is not obviously related to the position of the electrode. They simultaneously recorded LFP and cerebral blood volume (CBV)-weighted fMRI signal (see ref. 12 for details) from the resting (awake) primate brain and compared a regional fMRI signal to the slow temporal variations in the power of the LFP in low-, intermediate-, and high-frequency bands (see ref. 13 for frequency distributions of neuronal activities). Slow fluctuations of the spontaneous neuronal activity—in either highor lowbut not intermediate-frequency LFP bands—measured from a single cortical site in one hemisphere exhibited widespread correlations with spontaneous fluctuations in fMRI signals. Global patterns of these spatial correlations were quite similar whether the LFP was measured from the frontal, parietal, or occipital cortices of the primate brain. How much of the spontaneous fluctuations in the fMRI data can be accounted for by the slow modulation of neuronal events? Schölvinck et al. (6) estimate that a considerable portion of the variance in their fMRI signal is related to the slow Fig. 1. Schema of neuronal histogram and neuronal energetics. Histograms of total neuronal activity represented by distribution of firing rates (ν) of the same neuronal ensemble, composed of many neurons, in low (blue; left) and high (green; right) baseline energy states. The histograms can be converted to CMRO2 by multiplying the number of neurons firing at a given rate over the entire range of frequencies. See refs. 5 and 22 for details on neuronal histogram and neuronal energetics.