Functional MRI (fMRI) has uncovered widespread hemodynamic fluctuations in the brain during rest. Recent electroencephalographic work in humans and microelectrode recordings in anesthetized monkeys have shown this activity to be correlated with slow changes in neural activity. Here we report that the spontaneous fluctuations in the local field potential (LFP) measured from a single cortical site in monkeys at rest exhibit widespread, positive correlations with fMRI signals over nearly the entire cerebral cortex. This correlation was especially consistent in a band of upper gamma-range frequencies (40-80 Hz), for which the hemodynamic signal lagged the neural signal by 6-8 s. A strong, positive correlation was also observed in a band of lower frequencies (2-15 Hz), albeit with a lag closer to zero. The global pattern of correlation with spontaneous fMRI fluctuations was similar whether the LFP signal was measured in occipital, parietal, or frontal electrodes. This coupling was, however, dependent on the monkey's behavioral state, being stronger and anticipatory when the animals' eyes were closed. These results indicate that the often discarded global component of fMRI fluctuations measured during the resting state is tightly coupled with underlying neural activity.cortex | electrophysiology | local field potential | functional connectivity | monkey T he mammalian cerebral cortex is subdivided into specialized regions for various cognitive functions, such as the processing of sensory stimuli, memory, and the execution of movements. This functional specialization notwithstanding, the brain does not cease to show pronounced dynamic activity in the absence of cognitive or sensory stimulation. Significant ongoing spontaneous activity has been demonstrated using optical (1, 2), electrophysiological (3-5), and functional imaging (6, 7) techniques in several species under a variety of behavioral states. FMRI allows for visualization of large-scale, spatial patterns of such intrinsic activity, which is achieved by mapping patterns of activity covariation between brain regions. The temporal correlation between fluctuations in different regions is then often taken as a measure of "functional connectivity" between the corresponding brain areas (8-11). These fluctuations typically exhibit their highest intervoxel coherence at low temporal frequencies (<0.1 Hz) and can be observed during alertness (12, 13), sleep (14, 15), light sedation (16), and general anesthesia (17,18). Experiments are beginning to address the spatiotemporal characteristics of these spontaneous fluctuations in animal models (19)(20)(21), with initial studies in macaques suggesting a human-like pattern of functional connectivity (7,22).In humans, spontaneous activity is typically investigated in the so-called resting state, a term that is only loosely defined and which typically amounts to a subject lying in the scanner without an explicit stimulus or task. Under these conditions, analysis of spatiotemporal coherence of fMRI activity reveals several distin...

The mouse is becoming a key species for research on the neural circuits of the early visual system. To relate such circuits to perception, one must measure visually guided behavior and ask how it depends on fundamental stimulus attributes such as visual contrast. Using operant conditioning, we trained mice to detect visual contrast in a two-alternative forced-choice task. After 3-4 weeks of training, mice performed hundreds of trials in each session. Numerous sessions yielded high-quality psychometric curves from which we inferred measures of contrast sensitivity. In multiple sessions, however, choices were influenced not only by contrast, but also by estimates of reward value and by irrelevant factors such as recent failures and rewards. This behavior was captured by a generalized linear model involving not only the visual responses to the current stimulus but also a bias term and history terms depending on the outcome of the previous trial. We compared the behavioral performance of the mice to predictions of a simple decoder applied to neural responses measured in primary visual cortex of awake mice during passive viewing. The decoder performed better than the animal, suggesting that mice might not use optimally the information contained in the activity of visual cortex.

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the timevarying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.resting-state fMRI | spontaneous fluctuations | arousal | electrophysiology D uring both active task engagement and rest, the human brain exhibits fluctuations in neural activity that can be readily measured using functional MRI (fMRI). In recent years, examining the spatiotemporal organization of these fluctuations has generated novel insight into the functional architecture of the human brain and its changes with development and disease (1). A prominent approach for mapping this architecture is to study interregional correlations in the fMRI signal fluctuations, which, even during rest, appear to be indicative of networks supporting specific functions. However, despite the promise and rapidly increasing application of this technique in the endeavor of brain connectomics (2-4), its sensitivity and specificity are compromised by unexplained variability arising from multiple neural and nonneural sources (e.g., refs. 5-11). As a result, the interpretation of resting-state fMRI data and the efficacy of these data as a biomarker rely critically on understanding and accounting for such sources of variability (11-13).Changes in arousal, mediated by an interaction between the ascending arousal system and the neocortex, may strongly modulate neuronal activity in much of the brain (14-18). Indeed, changes in vigilance and arousal (hereafter described jointly as "arousal"), which can be especially prevalent during the passive and uncontrolled resting state, result in fMRI signal variability that may confound the extraction of functional networks (5, 19). For example, the amplitude and extent of correlations in restingstate fMRI data vary with EEG-and behaviorally defined indicators of drowsiness and light sleep (20)(21)(22)(23)(24)(25) and are altered by sleep deprivation (26-28) and caffeine-induced changes in arousal state (29). Distinct patterns of functional connectivity across multiple networks have been associated with distinct EEG-defined sleep stages (30, 31) with sufficient reliabilit...