Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest

We assessed the relation between hemodynamic and electrical indices of brain function by performing simultaneous functional MRI (fMRI) and electroencephalography (EEG) in awake subjects at rest with eyes closed. Spontaneous power fluctuations of electrical rhythms were determined for multiple discrete frequency bands, and associated fMRI signal modulations were mapped on a voxel-by-voxel basis. There was little positive correlation of localized brain activity with alpha power (8-12 Hz), but strong and widespread negative correlation in lateral frontal and parietal cortices that are known to support attentional processes. Power in a 17-23 Hz range of beta activity was positively correlated with activity in retrosplenial, temporo-parietal, and dorsomedial prefrontal cortices. This set of areas has previously been characterized by high but coupled metabolism and blood flow at rest that decrease whenever subjects engage in explicit perception or action. The distributed patterns of fMRI activity that were correlated with power in different EEG bands overlapped strongly with those of functional connectivity, i.e., intrinsic covariations of regional activity at rest. This result indicates that, during resting wakefulness, and hence the absence of a task, these areas constitute separable and dynamic functional networks, and that activity in these networks is associated with distinct EEG signatures. Taken together with studies that have explicitly characterized the response properties of these distributed cortical systems, our findings may suggest that alpha oscillations signal a neural baseline with "inattention" whereas beta rhythms index spontaneous cognitive operations during conscious rest.     

Synchronized delta oscillations correlate with the resting-state functional MRI signal

Synchronized low-frequency spontaneous fluctuations of the functional MRI (fMRI) signal have recently been applied to investigate large-scale neuronal networks of the brain in the absence of specific task instructions. However, the underlying neural mechanisms of these fluctuations remain largely unknown. To this end, electrophysiological recordings and resting-state fMRI measurements were conducted in alpha-chloralose-anesthetized rats. Using a seed-voxel analysis strategy, region-specific, anesthetic dose-dependent fMRI resting-state functional connectivity was detected in bilateral primary somatosensory cortex (S1FL) of the resting brain. Cortical electroencephalographic signals were also recorded from bilateral S1FL; a visual cortex locus served as a control site. Results demonstrate that, unlike the evoked fMRI response that correlates with power changes in the gamma bands, the resting-state fMRI signal correlates with the power coherence in low-frequency bands, particularly the delta band. These data indicate that hemodynamic fMRI signal differentially registers specific electrical oscillatory frequency band activity, suggesting that fMRI may be able to distinguish the ongoing from the evoked activity of the brain.     

Category-selective phase coding in the superior temporal sulcus

Object perception and categorization can occur so rapidly that behavioral responses precede or co-occur with the firing rate changes in the object-selective neocortex. Phase coding could, in principle, support rapid representation of object categories, whereby the first spikes evoked by a stimulus would appear at different phases of an oscillation, depending on the object category. To determine whether object-selective regions of the neo-cortex demonstrate phase coding, we presented images of faces and objects to two monkeys while recording local field potentials (LFP) and single unit activity from object-selective regions in the upper bank superior temporal sulcus. Single units showed preferred phases of firing that depended on stimulus category, emerging with the initiation of spiking responses after stimulus onset. Differences in phase of firing were seen below 20 Hz and in the gamma and high-gamma frequency ranges. For all but the <20-Hz cluster, phase differences remained category-specific even when controlling for stimulus-locked activity, revealing that phase-specific firing is not a simple consequence of category-specific differences in the evoked responses of the LFP. In addition, we tested for firing rate-to-phase conversion. Category-specific differences in firing rates accounted for 30–40% of the explained variance in phase occurring at lower frequencies (<20 Hz) during the initial response, but was limited (<20% of the explained variance) in the 30- to 60-Hz frequency range, suggesting that gamma phase-of-firing effects reflect more than evoked LFP and firing rate responses. The present results are consistent with theoretical models of rapid object processing and extend previous observations of phase coding to include object-selective neocortex.     

Poststimulus undershoots in cerebral blood flow and BOLD fMRI responses are modulated by poststimulus neuronal activity

fMRI is the foremost technique for noninvasive measurement of human brain function. However, its utility is limited by an incomplete understanding of the relationship between neuronal activity and the hemodynamic response. Though the primary peak of the hemodynamic response is modulated by neuronal activity, the origin of the typically negative poststimulus signal is poorly understood and its amplitude assumed to covary with the primary response. We use simultaneous recordings of EEG with blood oxygenation level-dependent (BOLD) and cerebral blood flow (CBF) fMRI during unilateral median nerve stimulation to show that the poststimulus fMRI signal is neuronally modulated. We observe high spatial agreement between concurrent BOLD and CBF responses to median nerve stimulation, with primary signal increases in contralateral sensorimotor cortex and primary signal decreases in ipsilateral sensorimotor cortex. During the poststimulus period, the amplitude and directionality (positive/negative) of the BOLD signal in both contralateral and ipsilateral sensorimotor cortex depends on the poststimulus synchrony of 8–13 Hz EEG neuronal activity, which is often considered to reflect cortical inhibition, along with concordant changes in CBF and metabolism. Therefore we present conclusive evidence that the fMRI time course represents a hemodynamic signature of at least two distinct temporal phases of neuronal activity, substantially improving understanding of the origin of the BOLD response and increasing the potential measurements of brain function provided by fMRI. We suggest that the poststimulus EEG and fMRI responses may be required for the resetting of the entire sensory network to enable a return to resting-state activity levels.     

Quantitative basis for neuroimaging of cortical laminae with calibrated functional MRI

Layer-specific neurophysiologic, hemodynamic, and metabolic measurements are needed to interpret high-resolution functional magnetic resonance imaging (fMRI) data in the cerebral cortex. We examined how neurovascular and neurometabolic couplings vary vertically in the rat’s somatosensory cortex. During sensory stimulation we measured dynamic layer-specific responses of local field potential (LFP) and multiunit activity (MUA) as well as blood oxygenation level-dependent (BOLD) signal and cerebral blood volume (CBV) and blood flow (CBF), which in turn were used to calculate changes in oxidative metabolism (CMR_O2) with calibrated fMRI. Both BOLD signal and CBV decreased from superficial to deep laminae, but these responses were not well correlated with either layer-specific LFP or MUA. However, CBF changes were quite stable across laminae, similar to LFP. However, changes in CMR_O2 and MUA varied across cortex in a correlated manner and both were reduced in superficial lamina. These results lay the framework for quantitative neuroimaging across cortical laminae with calibrated fMRI methods.The most recognizable features of the cerebral cortex across phyla are the layers (i.e., laminae) representing different cell types that project and connect to create networks, both in the horizontal and vertical directions of the cortex (1). Functional MRI (fMRI) with high-field magnets has been used to image this complex heterogeneous system of connections across cortical laminae. Given the complexity of the blood oxygenation level-dependent (BOLD) signal (2), quantitative assessment of neurophysiologic, hemodynamic, and metabolic responses across cortical laminae is needed to interpret high-resolution fMRI data in terms of neural activity. Because synaptic density (1) and commensurate electrical and chemical activities vary across cortical layers (3, 4), it is hypothesized that hemodynamic and metabolic responses would also vary. However, there are limited results on layer-specific variations in these parameters.High magnetic fields have improved BOLD sensitivity and specificity (5), whereas other MRI developments have allowed cerebral blood volume (CBV) and flow (CBF) measurements to calibrate fMRI signal so that changes in cerebral metabolic rate of oxygen consumption (CMR_O2) can be calculated with a biophysical model of BOLD (6). These multimodal fMRI techniques in conjunction with other related magnetic resonance spectroscopy methods have allowed quantitative insights into the molecular and cellular bases of neurovascular and neurometabolic couplings (7).In vivo recordings of neural activity with metal microelectrodes depict fluctuations of extracellular voltage, where the high- and low-frequency components, respectively, reflect multiunit activity (MUA) and local field potential (LFP) in a region (8). MUA is believed to reflect the output spiking activity of an ensemble of neurons because it reveals action potentials of large pyramidal neurons, whereas LFP reflects the synaptic input of a particular region because it depicts the weighted sum of changing membrane potentials along dendritic branches and soma (9, 10).To interpret the functional organization of the mammalian cerebral cortex from high-resolution fMRI data, the relation of the BOLD signal to underlying neural activities and hemodynamic or metabolic responses is needed at the laminar level. Although many animal studies have contributed to our knowledge about fMRI and its relation to multimodal functional responses (9–16), these past reports have focused primarily on dynamic correlations of signals in a specific cortical region. Here we measured the degree to which neurovascular and neurometabolic couplings vary in the vertical direction of the rat’s primary somatosensory cortex. Briefly, our results show that during sensory stimulation transcortical BOLD and CBV response patterns are uncoupled with both neural activity measures across cortical laminae, whereas neurometabolic coupling of MUA vs. CMR_O2 and neurovascular coupling of LFP vs. CBF have different spatial distributions in the superficial lamina.     

Low-frequency calcium oscillations accompany deoxyhemoglobin oscillations in rat somatosensory cortex

Spontaneous low-frequency oscillations (LFOs) of blood-oxygen-level-dependent (BOLD) signals are used to map brain functional connectivity with functional MRI, but their source is not well understood. Here we used optical imaging to assess whether LFOs from vascular signals covary with oscillatory intracellular calcium (Ca²⁺_i) and with local field potentials in the rat’s somatosensory cortex. We observed that the frequency of Ca²⁺_i oscillations in tissue (∼0.07 Hz) was similar to the LFOs of deoxyhemoglobin (HbR) and oxyhemoglobin (HbO₂) in both large blood vessels and capillaries. The HbR and HbO₂ fluctuations within tissue correlated with Ca²⁺_i oscillations with a lag time of ∼5–6 s. The Ca²⁺_i and hemoglobin oscillations were insensitive to hypercapnia. In contrast, cerebral-blood-flow velocity (CBFv) in arteries and veins fluctuated at a higher frequency (∼0.12 Hz) and was sensitive to hypercapnia. However, in parenchymal tissue, CBFv oscillated with peaks at both ∼0.06 Hz and ∼0.12 Hz. Although the higher-frequency CBFv oscillation (∼0.12 Hz) was decreased by hypercapnia, its lower-frequency component (∼0.06 Hz) was not. The sensitivity of the higher CBF_V oscillations to hypercapnia, which triggers blood vessel vasodilation, suggests its dependence on vascular effects that are distinct from the LFOs detected in HbR, HbO₂, Ca²⁺_i, and the lower-frequency tissue CBFv, which were insensitive to hypercapnia. Hemodynamic LFOs correlated both with Ca²⁺_i and neuronal firing (local field potentials), indicating that they directly reflect neuronal activity (perhaps also glial). These findings show that HbR fluctuations (basis of BOLD oscillations) are linked to oscillatory cellular activity and detectable throughout the vascular tree (arteries, capillaries, and veins).Measures of resting-state functional connectivity with functional MRI (fMRI) are based on spontaneous low-frequency blood-oxygen-level-dependent (BOLD) oscillations that occur throughout the brain with the assumption that regions with correlated oscillations are functionally connected (1, 2). The networks that emerge from resting-state functional connectivity correspond roughly with neuroanatomical connectivity (3, 4) and are modified by brain diseases (5–7). BOLD signals in fMRI reflect the interplay between hemodynamics (including blood volume and velocity of blood flowing in the vessels) and cellular (neuronal and glial) metabolism, which affect the amount of deoxygenated hemoglobin (HbR) in brain tissue that leads to changes in BOLD fMRI (8, 9). Human studies using near-infrared spectroscopy (NIRS) (10) have reported low-frequency oscillations (LFOs) of ∼0.04–0.1 Hz for oxygenated hemoglobin (HbO₂) and HbR in the brain consistent with those measured by BOLD (11). However, there is still no quantitative understanding of the relative direct contribution of spontaneous oscillations in cellular activity (neuronal and glial) vs. oscillations that reflect hemodynamic coupling (velocity and vessel diameter) (12) to the resting-state signal. It is also unclear how fluctuations in HbR progress through the vascular tree (13); whereas BOLD signals are believed to predominantly reflect postcapillary and venous compartments, recent evidence suggests that capillaries and arteries also contribute (14).Here we test the hypothesis that slow BOLD oscillations reflect neuronal oscillatory activity that drives the hemodynamic changes detected with fMRI. For this purpose we use a multimodal optical imaging platform whose high spatiotemporal resolution allowed us to measure spontaneous LFOs in cerebral blood flow velocity (CBFv), HbO₂, and HbR in different vascular compartments (veins vs. arteries) and in parenchymal tissue in the rat’s somatosensory cortex both under normocapnia (baseline) and hypercapnia (5% CO₂). In parallel we measured spontaneous LFOs in intracellular calcium fluorescence (Ca²⁺_i) using the fluorescent indicator Rhod₂-AM (Molecular Probes), which serves as a marker of cellular activity (15). In addition, local field potentials (LFPs) from neurons were measured to assess their correlations with the hemodynamic LFOs. Hypercapnia dilates cerebral blood vessels, increasing blood flow, but has minimal effects on neuronal activity (16, 17) and neurovascular coupling (18–20). Thus, we used hypercapnia as a strategy to differentiate oscillatory components that are due to neurovascular coupling as opposed to other mechanisms that affect vascular tone. Fluorescence histochemistry experiments of Rhod₂-Ca²⁺_i revealed that the Ca²⁺_i signal reflected cellular activity (neuronal and perhaps also glial activity). The results indicate that HbR fluctuations occur throughout the vascular tree (arteries, veins, and capillaries) and that Ca²⁺_i oscillations are strongly correlated but occur before HbR fluctuations. Interestingly, we uncovered that CBFv fluctuations have two distinct components, one at the frequency of HbR that was insensitive to hypercapnia and one at a higher frequency that was reduced by hypercapnia, suggesting its dependence on vascular effects. Parallel studies of LFP signals of neurons showed oscillations correlated with those of CBFv, HbR, and HbO₂ that indicate they directly reflect neuronal oscillatory activity. Thus, the HbR fluctuations that are the basis of resting-state fMRI are linked to cellular oscillations and observed in arteries, veins, and capillaries.     

Neural activity in the visual thalamus reflects perceptual suppression

To examine the role of the visual thalamus in perception, we recorded neural activity in the lateral geniculate nucleus (LGN) and pulvinar of 2 macaque monkeys during a visual illusion that induced the intermittent perceptual suppression of a bright luminance patch. Neural responses were sorted on the basis of the trial-to-trial visibility of the stimulus, as reported by the animals. We found that neurons in the dorsal and ventral pulvinar, but not the LGN, showed changes in spiking rate according to stimulus visibility. Passive viewing control sessions showed such modulation to be independent of the monkeys' active report. Perceptual suppression was also accompanied by a marked drop in low-frequency power (9–30 Hz) of the local field potential (LFP) throughout the visual thalamus, but this modulation was not observed during passive viewing. Our findings demonstrate that visual responses of pulvinar neurons reflect the perceptual awareness of a stimulus, while those of LGN neurons do not.     

Effects of dopamine depletion on LFP oscillations in striatum are task- and learning-dependent and selectively reversed by l-DOPA

A major physiologic sign in Parkinson disease is the occurrence of abnormal oscillations in cortico-basal ganglia circuits, which can be normalized by l-DOPA therapy. Under normal circumstances, oscillatory activity in these circuits is modulated as behaviors are learned and performed, but how dopamine depletion affects such modulation is not yet known. We here induced unilateral dopamine depletion in the sensorimotor striatum of rats and then recorded local field potential (LFP) activity in the dopamine-depleted region and its contralateral correspondent as we trained the rats on a conditional T-maze task. Unexpectedly, the dopamine depletion had little effect on oscillations recorded in the pretask baseline period. Instead, the depletion amplified oscillations across delta (∼3 Hz), theta (∼8 Hz), beta (∼13 Hz), and low-gamma (∼48 Hz) ranges selectively during task performance times when each frequency band was most strongly modulated, and only after extensive training had occurred. High-gamma activity (65–100 Hz), in contrast, was weakened independent of task time or learning stage. The depletion also increased spike-field coupling of fast-spiking interneurons to low-gamma oscillations. l-DOPA therapy normalized all of these effects except those at low gamma. Our findings suggest that the task-related and learning-related dynamics of LFP oscillations are the primary targets of dopamine depletion, resulting in overexpression of behaviorally relevant oscillations. l-DOPA normalizes these dynamics except at low-gamma, linked by spike-field coupling to fast-spiking interneurons, now known to undergo structural changes after dopamine depletion and to lack normalization of spike activity following l-DOPA therapy.Loss of the dopamine-containing innervation of the basal ganglia is a primary pathology in Parkinson disease, resulting, in addition to its behavioral effects, in abnormal local field potential (LFP) oscillations within cortico-basal ganglia circuits (1–4). Clinical evidence suggests that successful therapies for Parkinson disease reduce these abnormal LFP oscillations (3–6), establishing them as a central feature of Parkinson disease. In particular, abnormally strong beta-range oscillations (12–30 Hz) and weakened high-frequency gamma oscillations (>70 Hz) have been found in basal ganglia structures. The “antimovement” beta-band oscillations are reduced by both l-DOPA therapy and by deep brain stimulation (DBS) (3–6). How these observations relate to the proposed network functions of oscillatory neural activity is not yet clear. LFP oscillations have been linked not only to motor control but also to sensory perception, attention, learning, memory formation, and interregional communication (7–11). In Parkinson disease models, abnormal patterns of synchrony have been found in rest and locomotion (12–14), but the effect of dopamine loss on LFP oscillations during complex tasks requiring learning and decision making has not been explored.Here we report that dopamine depletion in the sensorimotor striatum has striking effects both on oscillatory power in multiple frequency ranges and on spike-field synchrony, but that the abnormal patterns of synchronization are behaviorally regulated and are not omnipresent features of the dopamine-depleted state.     

Cortical brightness adaptation when darkness and brightness produce different dynamical states in the visual cortex

Darkness and brightness are very different perceptually. To understand the neural basis for the visual difference, we studied the dynamical states of populations of neurons in macaque primary visual cortex when a spatially uniform area (8° × 8°) of the visual field alternated between black and white. Darkness evoked sustained nerve-impulse spiking in primary visual cortex neurons, but bright stimuli evoked only a transient response. A peak in the local field potential (LFP) γ band (30–80 Hz) occurred during darkness; white-induced LFP fluctuations were of lower amplitude, peaking at 25 Hz. However, the sustained response to white in the evoked LFP was larger than for black. Together with the results on spiking, the LFP results imply that, throughout the stimulus period, bright fields evoked strong net sustained inhibition. Such cortical brightness adaptation can explain many perceptual phenomena: interocular speeding up of dark adaptation, tonic interocular suppression, and interocular masking.Light adaptation is a vitally important visual function for enabling a stable perception of the visual world when background luminance levels can be as different as night and day. Previous psychophysical studies suggested that light adaptation was caused mainly by gain control mechanisms in the retina (1–3) that have been well studied (4). However, some psychophysical results suggested that there might be also a cortical contribution to light adaptation (5), but the nature of the cortical contribution is much less well understood. Here, we report our studies of cortical adaptation to brightness and darkness in macaque primary visual cortex (V1) and the implications for visual perception.We asked the following question: how does macaque V1 cortex respond to large dark and bright regions like those that would comprise the background of a visual scene during the night or the day, respectively? The experiments reported here focused on two cortical layers, 4C and 2/3. The layers of V1 are distinct stages of processing of visual signals (6, 7). The input layer 4C is the first cortical stage where the cortex could distinguish between blackness and whiteness (8). Layer 2/3 comprise one of the main visual outputs of V1 to extrastriate visual cortex (9). To obtain a comprehensive view of the response to black and white in cortical layers 4C and 2/3, we used measurements of population activity: multiunit spike rate, termed multiunit activity (MUA), and local field potential (LFP) (10–12).Cortical brightness adaptation was evident in the qualitatively different dynamics of neural population activity in layers 4C and 2/3 when the monkeys viewed black and white regions. Both black and white large-area stimuli evoked transient excitatory responses in MUA, but in response to a white region, there was a slowly developing but much stronger inhibition of spike activity. Such suppression of sustained spiking in cortical neurons by white backgrounds would increase the signal/noise ratio of targets on white backgrounds. Such cortical brightness adaptation is likely the explanation for many previously observed perceptual phenomena such as tonic interocular suppression, dichoptic effects in light and dark adaptation, and interocular masking (5, 13–16).     

Toward discovery science of human brain function

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual''s “functional connectome.” Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual''s functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain–behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.     

Spatiotemporal precision and hemodynamic mechanism of optical point spreads in alert primates

In functional brain imaging there is controversy over which hemodynamic signal best represents neural activity. Intrinsic signal optical imaging (ISOI) suggests that the best signal is the early darkening observed at wavelengths absorbed preferentially by deoxyhemoglobin (HbR). It is assumed that this darkening or “initial dip” reports local conversion of oxyhemoglobin (HbO) to HbR, i.e., oxygen consumption caused by local neural activity, thus giving the most specific measure of such activity. The blood volume signal, by contrast, is believed to be more delayed and less specific. Here, we used multiwavelength ISOI to simultaneously map oxygenation and blood volume [i.e., total hemoglobin (HbT)] in primary visual cortex (V1) of the alert macaque. We found that the hemodynamic “point spread,” i.e., impulse response to a minimal visual stimulus, was as rapid and retinotopically specific when imaged by using blood volume as when using the initial dip. Quantitative separation of the imaged signal into HbR, HbO, and HbT showed, moreover, that the initial dip was dominated by a fast local increase in HbT, with no increase in HbR. We found only a delayed HbR decrease that was broader in retinotopic spread than HbO or HbT. Further, we show that the multiphasic time course of typical ISOI signals and the strength of the initial dip may reflect the temporal interplay of monophasic HbO, HbR, and HbT signals. Characterizing the hemodynamic response is important for understanding neurovascular coupling and elucidating the physiological basis of imaging techniques such as fMRI.     

Electrophysiological signatures of resting state networks in the human brain

Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.     

Baseline brain energy supports the state of consciousness

An individual, human or animal, is defined to be in a conscious state empirically by the behavioral ability to respond meaningfully to stimuli, whereas the loss of consciousness is defined by unresponsiveness. PET measurements of glucose or oxygen consumption show a widespread ≈45% reduction in cerebral energy consumption with anesthesia-induced loss of consciousness. Because baseline brain energy consumption has been shown by ¹³C magnetic resonance spectroscopy to be almost exclusively dedicated to neuronal signaling, we propose that the high level of brain energy is a necessary property of the conscious state. Two additional neuronal properties of the conscious state change with anesthesia. The delocalized fMRI activity patterns in rat brain during sensory stimulation at a higher energy state (close to the awake) collapse to a contralateral somatosensory response at lower energy state (deep anesthesia). Firing rates of an ensemble of neurons in the rat somatosensory cortex shift from the γ-band range (20–40 Hz) at higher energy state to <10 Hz at lower energy state. With the conscious state defined by the individual''s behavior and maintained by high cerebral energy, measurable properties of that state are the widespread fMRI patterns and high frequency neuronal activity, both of which support the extensive interregional communication characteristic of consciousness. This usage of high brain energies when the person is in the “state” of consciousness differs from most studies, which attend the smaller energy increments observed during the stimulations that form the “contents” of that state.     

Single-neuron firing cascades underlie global spontaneous brain events

The resting brain consumes enormous energy and shows highly organized spontaneous activity. To investigate how this activity is manifest among single neurons, we analyzed spiking discharges of ∼10,000 isolated cells recorded from multiple cortical and subcortical regions of the mouse brain during immobile rest. We found that firing of a significant proportion (∼70%) of neurons conformed to a ubiquitous, temporally sequenced cascade of spiking that was synchronized with global events and elapsed over timescales of 5 to 10 s. Across the brain, two intermixed populations of neurons supported orthogonal cascades. The relative phases of these cascades determined, at each moment, the response magnitude evoked by an external visual stimulus. Furthermore, the spiking of individual neurons embedded in these cascades was time locked to physiological indicators of arousal, including local field potential power, pupil diameter, and hippocampal ripples. These findings demonstrate that the large-scale coordination of low-frequency spontaneous activity, which is commonly observed in brain imaging and linked to arousal, sensory processing, and memory, is underpinned by sequential, large-scale temporal cascades of neuronal spiking across the brain.

The brain at rest exhibits slow (<0.1 Hz) but highly organized spontaneous activity as measured by functional MRI (fMRI) (1, 2). Much research in this area has utilized the temporal coordination of these signals to assess the functional organization a large number of brain networks. In recent years, however, new attention has been directed to a less-studied aspect of this signal, namely the conspicuous and discrete spontaneous events that occur simultaneously across the brain (3–5). These global resting-state fMRI events appear to reflect transient arousal modulations at a timescale of ∼10 s (4, 6) and also to be closely related to activity among clusters of cholinergic projection neurons in the basal forebrain (4, 5).The nature of global brain events is of great interest, as is their spatiotemporal dynamics. Some evidence suggests they take the form of traveling waves, propagating coherently according to the principles of the cortical hierarchy (7, 8), and shaping functional connectivity measures important for assessing the healthy and diseased brain (7, 9, 10). Other work has linked such global activity to phenomena as varied as modulation of the autonomic nervous system (11–14), cleansing circulation of cerebrospinal fluid in the glymphatic system (15–19), and memory consolidation mediated by hippocampal sharp-wave ripples (20, 21). In general, the global activity measured through brain imaging appears coordinated over timescales of seconds with a range of other neural and physiological events (11, 12, 14, 21–23). In a few cases, the relationship between local and global neural events has been studied using simultaneous measurements. For example, brain-wide fMRI fluctuations and local field potential (LFP) power changes are locked to the issuance of hippocampal ripples (21, 24). However, very little is understood about the extent to which single neurons participate in the expression and coordination of global spontaneous events of the seconds timescale. To approach this topic, recent technological advances have made it possible to track and compare the spiking activity of a large number of isolated neurons recorded simultaneously across multiple brain areas.A few recent studies utilizing high-density neuronal recording (25) have accumulated initial evidence suggesting a close relationship between the brain state and neuronal population dynamics (26–28). A large proportion of neurons, regardless of their location, showed strong modulations in their discharging rate that were coordinated in time with physiological arousal measures (28), across thirsty and sated states (26), and during exploratory and nonexploratory behaviors (27). Nevertheless, these studies leave open the question of how neuronal population dynamics are organized at a finer timescale of seconds surrounding spontaneous global events during immobile rest, and whether and how such dynamics are coincident with arousal modulations, hippocampal ripples, and sensory excitability. To investigate this topic, we examine the spiking activity recorded from large neuronal populations of neurons in immobilized mice, focusing on their seconds-scale coordination with global events and with one another. We further studied the impact of this spontaneous spiking on the magnitude of visually evoked responses and its time locking with other physiological signals related to arousal, such as LFP changes, hippocampal ripples, and changes in pupil diameter.     

fMRI spectral signatures of sleep

Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level–dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (<0.1 Hz) oscillation prominent in light sleep and correlated with the occurrence of spindles, and a high-frequency oscillation (>0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake–sleep transitions, and investigate local homeostatic sleep processes.

Traditionally, sleep is considered to be a global state that affects the whole brain uniformly and simultaneously. Correspondingly, brain activity during human sleep is typically measured using scalp electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by slow waves and spindles. Slow waves are associated with the near-synchronous transitions in large populations of neurons between depolarized up states of intense firing and hyperpolarized down states of silence (1). They are generated primarily in the cerebral cortex and affect virtually all cortical neurons, as well as neurons in several subcortical structures (2). By contrast, spindles are associated with cycles of depolarization and hyperpolarization triggered by the interactions between reticular thalamic nucleus and specific thalamic nuclei and amplified by the thalamo-cortico-thalamic circuits. Based on the prominence of slow waves and spindles, NREM sleep can be subdivided into transitional (N1), intermediate (N2), and deep (N3) sleep stages.Recently, the view of sleep as a global state has been overturned by the intracranial findings of local sleep and local wakefulness (3). During wakefulness, individual neurons were found to display brief periods of slow wave activity, accompanied by transient behavioral impairments (4). Conversely, during deep NREM sleep, subsets of brain regions were found to display wake-like activity (5), which was associated with dreaming (6). These findings establish that sleep-like and wakefulness-like states are not mutually exclusive, but can occur simultaneously in the same brain, with some neuronal populations showing one state and the rest the other. They highlight the importance to monitor the local state of individual neuronal populations, as opposed to the global state of the brain as a whole. However, EEG lacks both the spatial resolution and the brain coverage required for monitoring local neuronal state. It is difficult to identify the brain regions that generate the scalp EEG signal, where different source configurations can give rise to the same EEG topography. Moreover, the scalp and the intracranial EEG signals are both insensitive to neuronal activities in deep brain structures, making it difficult to monitor the neuronal state in these brain regions.Here we employed functional MRI (fMRI) to explore, with a full brain coverage and higher spatial resolution, local signatures of sleep in brain hemodynamic activity. We reasoned that the frequency content of fMRI blood oxygen level–dependent (BOLD) activity would show systematic changes from wake to sleep, reflecting the local groupings of spindles or slow waves by infra-slow fluctuations within the frequency range of brain hemodynamic activity. Our hypothesis builds upon previous reports of BOLD spectral changes from wake to sleep. Previous studies reported increases in low-frequency BOLD activity (<0.1 Hz) from wake to light sleep (7–9), as well as increases in higher-frequency BOLD activity (>0.1 Hz) from wake to propofol anesthesia (10). Although the relationships between these BOLD spectral changes and spindle or slow wave activity were not examined, it is interesting to note that propofol anesthesia can induce slow waves similar to those of NREM sleep (11), which might underlie the observed increase in high-frequency BOLD activity; moreover, the emergence of sleep spindles during child development (12) coincides with an increase in low-frequency BOLD activity (13). These studies hinted at a possible link between BOLD frequency content and spindle or slow wave activity. However, the exact link has remained unclear.Using simultaneous fMRI and EEG, we found that, during the transition from wake to sleep, fMRI BOLD activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency oscillation (<0.1 Hz) prominent in light sleep and a higher-frequency oscillation (>0.1 Hz) in deep sleep. The time courses of low-frequency and high-frequency BOLD oscillation power correlated, respectively, with the time courses of spindle and slow wave activities. Moreover, the regional distributions and the onset, offset patterns of low-frequency and high-frequency BOLD oscillation were similar to those of spindle and slow wave activity. By providing local signatures of spindle and slow wave activity, these two BOLD oscillations may be employed to monitor the local neuronal state and detect local sleep or local wakefulness.     

Energetics of neuronal signaling and fMRI activity

Energetics of resting and evoked fMRI signals were related to localized ensemble firing rates (nu) measured by electrophysiology in rats. Two different unstimulated, or baseline, states were established by anesthesia. Halothane and alpha-chloralose established baseline states of high and low energy, respectively, in which forepaw stimulation excited the contralateral primary somatosensory cortex (S1). With alpha-chloralose, forepaw stimulation induced strong and reproducible fMRI activations in the contralateral S1, where the ensemble firing was dominated by slow signaling neurons (SSN; nu range of 1-13 Hz). Under halothane, weaker and less reproducible fMRI activations were observed in the contralateral S1 and elsewhere in the cortex, but ensemble activity in S1 was dominated by rapid signaling neurons (RSN; nu range of 13-40 Hz). For both baseline states, the RSN activity (i.e., higher frequencies, including the gamma band) did not vary upon stimulation, whereas the SSN activity (i.e., alpha band and lower frequencies) did change. In the high energy baseline state, a large majority of total oxidative energy [cerebral metabolic rate of oxygen consumption (CMR(O2))] was devoted to RSN activity, whereas in the low energy baseline state, it was roughly divided between SSN and RSN activities. We hypothesize that in the high energy baseline state, the evoked changes in fMRI activation in areas beyond S1 are supported by rich intracortical interactions represented by RSN. We discuss implications for interpreting fMRI data where stimulus-specific DeltaCMR(O2) is generally small compared with baseline CMR(O2).     

Cortical activity during motor execution,motor imagery,and imagery-based online feedback

Imagery of motor movement plays an important role in learning of complex motor skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie motor imagery-based learning? We measured electrocorticographic cortical surface potentials in eight human subjects during overt action and kinesthetic imagery of the same movement, focusing on power in “high frequency” (76–100 Hz) and “low frequency” (8–32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population activity during motor imagery mimics the spatial distribution of activity during actual motor movement. By comparing responses to electrocortical stimulation with imagery-induced cortical surface activity, we demonstrate the role of primary motor areas in movement imagery. The magnitude of imagery-induced cortical activity change was ∼25% of that associated with actual movement. However, when subjects learned to use this imagery to control a computer cursor in a simple feedback task, the imagery-induced activity change was significantly augmented, even exceeding that of overt movement.     

Cross Spectral Analysis in Assessment of Baroreflex Gain in Patients with Coronary Artery Disease

Background: Interest in determination of baroreflex sensitivity in clinical practice is growing because of its prognostic information in patients with heart disease. The purpose of the present study was to assess the feasibility of cross spectral analysis in the determination of baroreflex gain from spontaneous RR interval and systolic pressure fluctuations, and to compare the results to the traditional pharmacological method in patients with coronary artery disease. Methods: We measured the gain and time lag between RR interval and systolic pressure variabilities in the frequency domain, and compared baroreflex indexes obtained by this technique with standard phenylephrine tests in 32 patients with coronary artery disease. Results: Cross spectral analysis by fast Fourier transform techniques yielded acceptable (> 0.5) coherence between systolic pressure and RR interval in the mid- (0.07–0.15 Hz) and in the respiratory-frequency (0.15–0.40 Hz) band fluctuations in 30 patients (94%), with mean coherences of 0.69 and 0.74. The mean phase difference in the mid-frequency band was greater than in the respiratory-frequency band (?83 vs ?23 degrees, P < 0.001), suggesting that the mid-frequency fluctuations of RR intervals followed nearly 2 seconds after pressure changes, while respiratory-frequency fluctuations of RR intervals occurred nearly concomitantly with systolic pressure. The mean baroreflex slope derived from the bolus phenylephrine technique was 6.2 ms/mmHg (range 1.6–16.0), 5 patients had an abnormally low (< 3 ms/mmHg) baroreflex sensitivity. Baroreflex gain determined by cross spectral analysis from the mid-frequency band correlated significantly (r = 0.60, P < 0.001, n = 27) with the baroreflex gain determined by the phenylephrine test, while the correlation in the respiratory-frequency band was not significant (r = 0.35, P = 0.09, n = 26). Conclusions: Baroreflex slopes derived from cross spectral techniques provide reliable (but not perfect) information regarding baroreflex gain derived from the classic phenylephrine technique, even in patients with depressed baroreflex responses. Cross correlation calculation of spontaneous baroreflex slopes should be limited to data in the mid-frequency range, where the slopes are likely to reflect simple baroreflex physiology.     

Effect of binaural beat in the inaudible band on EEG (STROBE)

This study aimed to determine the effects of the binaural beat (BB) on brainwave induction using an inaudible baseline frequency outside the audible frequency range. Experiments were conducted on 18 subjects (11 males [mean age: 25.7 ± 1.6 years] and 7 females [mean age: 24.0 ± 0.6 years]). A BB stimulation of 10 Hz was exerted by presenting frequencies of 18,000 Hz and 18,010 Hz to the left and right ears, respectively. A power spectrum analysis was performed to estimate the mean of the absolute power of the alpha frequency range (8–13 Hz). The variation in the mean alpha power during the rest and stimulation phases in each brain area was compared using the Wilcoxon signed-rank test. Compared to the rest phase, the stimulation phase with BB showed an increasing trend in the mean alpha power across all 5 brain areas. Notably, a significant increase was found in the frontal, central, and temporal areas. This is a significant study in that it determines the effects of only BB without the influence of auditory perception, which has been overlooked in previous studies.     

Layer-specific network oscillation and spatiotemporal receptive field in the visual cortex

A quintessential feature of the neocortex is its laminar organization, and characterizing the activity patterns in different layers is an important step in understanding cortical processing. Using in vivo whole-cell recordings in rat visual cortex, we show that the temporal patterns of ongoing synaptic inputs to pyramidal neurons exhibit clear laminar specificity. Although low-frequency (∼2 Hz) activity is widely observed in layer 2/3 (L2/3), a narrow-band fast oscillation (10–15 Hz) is prominent in layer 5 (L5). This fast oscillation is carried exclusively by excitatory inputs. Moreover, the frequency of ongoing activity is strongly correlated with the spatiotemporal window of visual integration: Neurons with fast-oscillating spontaneous inputs exhibit transient visual responses and small receptive fields (RFs), whereas those with slow inputs show prolonged responses and large RFs. These findings suggest that the neural representation of visual information within each layer is strongly influenced by the temporal dynamics of the local network manifest in spontaneous activity.