Generation of field potentials and modulation of their dynamics through volume integration of cortical activity

Large-scale interactions in predictive processing: oscillatory versus transient dynamics

How do the two main types of neural dynamics, aperiodic transients and oscillations, contribute to the interactions between feedforward (FF) and feedback (FB) pathways in sensory inference and predictive processing? We discuss three theoretical perspectives. First, we critically evaluate the theory that gamma and alpha/beta rhythms play a role in classic hierarchical predictive coding (HPC) by mediating FF and FB communication, respectively. Second, we outline an alternative functional model in which rapid sensory inference is mediated by aperiodic transients, whereas oscillations contribute to the stabilization of neural representations over time and plasticity processes. Third, we propose that the strong dependence of oscillations on predictability can be explained based on a biologically plausible alternative to classic HPC, namely dendritic HPC.

Layer-specific dynamics of local field potentials in monkey V1 during electrical stimulation

The mammalian neocortex, organized into six cellular layers or laminae, forms a cortical network within layers. Layer specific computations are crucial for sensory processing of visual stimuli within primary visual cortex. Laminar recordings of local field potentials (LFPs) are a powerful tool to study neural activity within cortical layers. Electric brain stimulation is widely used in basic neuroscience and in a large range of clinical applications. However, the layer-specific effects of electric stimulation on LFPs remain unclear. To address this gap, we conducted laminar LFP recordings of the primary visual cortex in monkeys while presenting a flash visual stimulus. Simultaneously, we applied a low frequency sinusoidal current to the occipital lobe with offset frequency to the flash stimulus repetition rate. We analyzed the modulation of visual-evoked potentials with respect to the applied phase of the electric stimulation. Our results reveal that only the deeper layers, but not the superficial layers, show phase-dependent changes in LFP components with respect to the applied current. Employing a cortical column model, we demonstrate that these in vivo observations can be explained by phase-dependent changes in the driving force within neurons of deeper layers. Our findings offer crucial insight into the selective modulation of cortical layers through electric stimulation, thus advancing approaches for more targeted neuromodulation.

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Mendoza-Halliday, Major et al., 2024 ("The Paper")1 advocates a local field potential (LFP)-based approach to functional identification of cortical layers during "laminar" (simultaneous recordings from all cortical layers) multielectrode recordings in nonhuman primates (NHPs). The Paper describes a "ubiquitous spectrolaminar motif" in the primate neocortex: 1) 75-150 Hz power peaks in the supragranular layers, 2) 10-19 Hz power peaks in the infragranular layers and 3) the crossing point of their laminar power gradients identifies Layer 4 (L4). Identification of L4 is critical in general, but especially for The Paper as the "motif" discovery is couched within a framework whose central hypothesis is that gamma activity originates in the supragranular layers and reflects feedforward activity, while alpha-beta activity originates in the infragranular layers and reflects feedback activity. In an impressive scientific effort, The Paper analyzed laminar data from 14 cortical areas in 2 prior macaque studies and compared them to marmoset, mouse, and human data to further bolster the canonical nature of the motif. Identification of such canonical principles of brain operation is clearly a topic of broad scientific interest. Similarly, a reliable online method for L4 identification would be of broad scientific value for the rapidly increasing use of laminar recordings using numerous evolving technologies. Despite The Paper's strengths, and its potential for scientific impact, a series of concerns that are fundamental to the analysis and interpretation of laminar activity profile data in general, and local field potential (LFP) signals in particular, led us to question its conclusions. We thus evaluated the generality of The Paper's methods and findings using new sets of data comprised of stimulus-evoked laminar response profiles from primary and higher-order auditory cortices (A1 and belt cortex), and primary visual cortex (V1). The rationale for using these areas as a test bed for new methods is that their laminar anatomy and physiology have already been extensively characterized by prior studies, and there is general agreement across laboratories on key matters like L4 identification. Our analyses indicate that The Paper's findings do not generalize well to any of these cortical areas. In particular, we find The Paper's methods for L4 identification to be unreliable. Moreover, both methodological and statistical concerns, outlined below and in the supplement, question the stated prevalence of the motif in The Paper's published dataset. After summarizing our findings and related broader concerns, we briefly critique the evidence from biophysical modeling studies cited to support The Paper's conclusions. While our findings are at odds with the proposition of a ubiquitous spectrolaminar motif in the primate neocortex, The Paper already has, and will continue to spark debate and further experimentation. Hopefully this countervailing presentation will lead to robust collegial efforts to define optimal strategies for applying laminar recording methods in future studies.

Laminar pattern of sensory-evoked dynamic high-frequency oscillatory activity in the macaque auditory cortex

High-frequency (>60 Hz) neuroelectric signals likely have functional roles distinct from low-frequency (<30 Hz) signals. While high-gamma activity (>60 Hz) does not simply equate to neuronal spiking, they are highly correlated, having similar information encoding. High-gamma activity is typically considered broadband and poorly phase-locked to sensory stimuli and thus is typically analyzed after transformations into absolute amplitude or spectral power. However, those analyses discard signal polarity, compromising the interpretation of neuroelectric events that are essentially dipolar. In the spectrotemporal profiles of field potentials in auditory cortex, we show high-frequency spectral peaks not phase-locked to sound onset, which follow the broadband peak of phase-locked onset responses. Isolating the signal components comprising the high-frequency peaks reveals narrow-band high-frequency oscillatory events, whose instantaneous frequency changes rapidly from >150 to 60 Hz, which may underlie broadband high-frequency spectral peaks in previous reports. The laminar amplitude distributions of the isolated activity had two peak positions, while the laminar phase patterns showed a counterphase relationship between those peaks, indicating the formation of dipoles. Our findings suggest that nonphase-locked HGA arises in part from oscillatory or recurring activity of supragranular-layer neuronal ensembles in auditory cortex.

Acoustic and language-specific sources for phonemic abstraction from speech

Spoken language comprehension requires abstraction of linguistic information from speech, but the interaction between auditory and linguistic processing of speech remains poorly understood. Here, we investigate the nature of this abstraction using neural responses recorded intracranially while participants listened to conversational English speech. Capitalizing on multiple, language-specific patterns where phonological and acoustic information diverge, we demonstrate the causal efficacy of the phoneme as a unit of analysis and dissociate the unique contributions of phonemic and spectrographic information to neural responses. Quantitive higher-order response models also reveal that unique contributions of phonological information are carried in the covariance structure of the stimulus-response relationship. This suggests that linguistic abstraction is shaped by neurobiological mechanisms that involve integration across multiple spectro-temporal features and prior phonological information. These results link speech acoustics to phonology and morphosyntax, substantiating predictions about abstractness in linguistic theory and providing evidence for the acoustic features that support that abstraction.

Near-field potentials index local neural computations more accurately than population spiking

Local field potentials (LFP) are low-frequency extracellular voltage fluctuations thought to primarily arise from synaptic activity. However, unlike highly localized neuronal spiking, LFP is spatially less specific. LFP measured at one location is not entirely generated there due to far-field contributions that are passively conducted across volumes of neural tissue. We sought to quantify how much information within the locally generated, near-field low-frequency activity (nfLFP) is masked by volume-conducted far-field signals. To do so, we measured laminar neural activity in primary visual cortex (V1) of monkeys viewing sequences of multifeatured stimuli. We compared information content of regular LFP and nfLFP that was mathematically stripped of volume-conducted far-field contributions. Information content was estimated by decoding stimulus properties from neural responses via spatiotemporal multivariate pattern analysis. Volume-conducted information differed from locally generated information in two important ways: (1) for stimulus features relevant to V1 processing (orientation and eye-of-origin), nfLFP contained more information. (2) in contrast, the volume-conducted signal was more informative regarding temporal context (relative stimulus position in a sequence), a signal likely to be coming from elsewhere. Moreover, LFP and nfLFP differed both spectrally as well as spatially, urging caution regarding the interpretations of individual frequency bands and/or laminar patterns of LFP. Most importantly, we found that population spiking of local neurons was less informative than either the LFP or nfLFP, with nfLFP containing most of the relevant information regarding local stimulus processing. These findings suggest that the optimal way to read out local computational processing from neural activity is to decode the local contributions to LFP, with significant information loss hampering both regular LFP and local spiking.

Author’s Contributions

Conceptualization, D.A.T., J.A.W, and A.M.; Data Collection, J.A.W., M.A.C., K.D.; Formal Analysis, D.A.T. and J.A.W.; Data Visualization, D.A.T. and J.A.W.; Original Draft, D.A.T., J.A.W., and A.M.; Revisions and Final Draft, D.A.T., J.A.W., M.A.C., K.D., M.T.W., A.M.B., and A.M.

Competing Interests

The authors declare no conflicts of interest.

Activity-dependent feedback regulation of thalamocortical axon development by Lhx2 in cortical layer 4 neurons

Establishing neuronal circuits requires interactions between pre- and postsynaptic neurons. While presynaptic neurons were shown to play instructive roles for the postsynaptic neurons, how postsynaptic neurons provide feedback to regulate the presynaptic neuronal development remains elusive. To elucidate the mechanisms for circuit formation, we study the development of barrel cortex (the primary sensory cortex, S1), whose development is instructed by presynaptic thalamocortical axons (TCAs). In the first postnatal weeks, TCA terminals arborize in layer (L) 4 to fill in the barrel center, but it is unclear how TCA development is regulated. Here, we reported that the deletion of Lhx2 specifically in the cortical neurons in the conditional knockout (cKO) leads to TCA arborization defects, which is accompanied with deficits in sensory-evoked and spontaneous cortical activities and impaired lesion-induced plasticity following early whisker follicle ablation. Reintroducing Lhx2 back in L4 neurons in cKO ameliorated TCA arborization and plasticity defects. By manipulating L4 neuronal activity, we further demonstrated that Lhx2 induces TCA arborization via an activity-dependent mechanism. Additionally, we identified the extracellular signaling protein Sema7a as an activity-dependent downstream target of Lhx2 in regulating TCA branching. Thus, we discovered a bottom-up feedback mechanism for the L4 neurons to regulate TCA development.

Sensorimotor representation of observed dyadic actions with varying agent involvement: an EEG mu study

Observation of others' actions activates motor representations in sensorimotor cortex. Although action observation in the real-world often involves multiple agents displaying varying degrees of action involvement, most lab studies on action observation studied individual actions. We recorded EEG-mu suppression over sensorimotor cortex to investigate how the multi-agent nature of observed hand/arm actions is incorporated in sensorimotor action representations. Hereto we manipulated the extent of agent involvement in dyadic interactions presented in videos. In all clips two agents were present, of which agent-1 always performed the same action, while the involvement of agent-2 differed along three levels: (1) passive and uninvolved, (2) passively involved, (3) actively involved. Additionally, a no-action condition was presented. The occurrence of these four conditions was predictable thanks to cues at the start of each trial, which allowed to study possible mu anticipation effects. Dyadic interactions in which agent-2 was actively involved resulted in increased power suppression of the mu rhythm compared to dyadic interactions in which agent-2 was passively involved. The latter did not differ from actions in which agent-2 was present but not involved. No anticipation effects were found. The results suggest that the sensorimotor representation of a dyadic interaction takes into account the simultaneously performed bodily articulations of both agents, but no evidence was found for incorporation of their static articulated postures.

The Spatial Reach of Neuronal Coherence and Spike-Field Coupling across the Human Neocortex

Neuronal coherence is thought to be a fundamental mechanism of communication in the brain, where synchronized field potentials coordinate synaptic and spiking events to support plasticity and learning. Although the spread of field potentials has garnered great interest, little is known about the spatial reach of phase synchronization, or neuronal coherence. Functional connectivity between different brain regions is known to occur across long distances, but the locality of synchronization across the neocortex is understudied. Here we used simultaneous recordings from electrocorticography (ECoG) grids and high-density microelectrode arrays to estimate the spatial reach of neuronal coherence and spike-field coherence (SFC) across frontal, temporal, and occipital cortices during cognitive tasks in humans. We observed the strongest coherence within a 2-3 cm distance from the microelectrode arrays, potentially defining an effective range for local communication. This range was relatively consistent across brain regions, spectral frequencies, and cognitive tasks. The magnitude of coherence showed power law decay with increasing distance from the microelectrode arrays, where the highest coherence occurred between ECoG contacts, followed by coherence between ECoG and deep cortical local field potential (LFP), and then SFC (i.e., ECoG > LFP > SFC). The spectral frequency of coherence also affected its magnitude. Alpha coherence (8-14 Hz) was generally higher than other frequencies for signals nearest the microelectrode arrays, whereas delta coherence (1-3 Hz) was higher for signals that were farther away. Action potentials in all brain regions were most coherent with the phase of alpha oscillations, which suggests that alpha waves could play a larger, more spatially local role in spike timing than other frequencies. These findings provide a deeper understanding of the spatial and spectral dynamics of neuronal synchronization, further advancing knowledge about how activity propagates across the human brain.SIGNIFICANCE STATEMENT Coherence is theorized to facilitate information transfer across cerebral space by providing a convenient electrophysiological mechanism to modulate membrane potentials in spatiotemporally complex patterns. Our work uses a multiscale approach to evaluate the spatial reach of phase coherence and spike-field coherence during cognitive tasks in humans. Locally, coherence can reach up to 3 cm around a given area of neocortex. The spectral properties of coherence revealed that alpha phase-field and spike-field coherence were higher within ranges <2 cm, whereas lower-frequency delta coherence was higher for contacts farther away. Spatiotemporally shared information (i.e., coherence) across neocortex seems to reach farther than field potentials alone.

Magnifying Traveling Waves on the Scalp

Traveling waves appear in various signals that measure neuronal activity. Some signals measured in animals have singles-cell resolution and directly point to neuronal activity. In those cases, activation of distributed neurons forms a wave front, and the front propagates across the cortical surface. Other signals are variants of neuroelectric potentials, i.e. electroencephalography, electrocorticography and field potentials. Instead of having fine spatial resolution, these signals reflect the activity of neuronal populations via volume conduction (VC). Sources of traveling waves in neuroelectric potentials have not been well addressed so far. As animal studies show propagating activation of neurons that spread in measured areas, it is often considered that neuronal activations during scalp waves have similar trajectories of activation, spreading like scalp waves. However, traveling waves on the scalp differ from those found directly on the cortical surface in several dimensions: traveling velocity, traveling distance and areal size occupied by single polarity. We describe that the simplest sources can produce scalp waves with perceived spatial dimensions which are actually a magnification of neuronal activity emanating from local sources due to VC. This viewpoint is not a rigorous proof of our magnification concept. However, we suggest the possibility that the actual dimensions of neuronal activity producing traveling waves is not as large as the dimension of the traveling waves.

Cross Laminar Traveling Components of Field Potentials due to Volume Conduction of Non-Traveling Neuronal Activity in Macaque Sensory Cortices

Field potentials (FPs) reflect neuronal activities in the brain, and often exhibit traveling peaks across recording sites. While traveling FPs are interpreted as propagation of neuronal activity, not all studies directly reveal such propagating patterns of neuronal activation. Neuronal activity is associated with transmembrane currents that form dipoles and produce negative and positive fields. Thereby, FP components reverse polarity between those fields and have minimal amplitudes at the center of dipoles. Although their amplitudes could be smaller, FPs are never flat even around these reversals. What occurs around the reversal has not been addressed explicitly, although those are rationally in the middle of active neurons. We show that sensory FPs around the reversal appeared with peaks traveling across cortical laminae in macaque sensory cortices. Interestingly, analyses of current source density did not depict traveling patterns but lamina-delimited current sinks and sources. We simulated FPs produced by volume conduction of a simplified 2 dipoles' model mimicking sensory cortical laminar current source density components. While FPs generated by single dipoles followed the temporal patterns of the dipole moments without traveling peaks, FPs generated by concurrently active dipole moments appeared with traveling components in the vicinity of dipoles by superimposition of individually non-traveling FPs generated by single dipoles. These results indicate that not all traveling FP are generated by traveling neuronal activity, and that recording positions need to be taken into account to describe FP peak components around active neuronal populations.SIGNIFICANCE STATEMENT Field potentials (FPs) generated by neuronal activity in the brain occur with fields of opposite polarity. Likewise, in the cerebral cortices, they have mirror-imaged waveforms in upper and lower layers. We show that FPs appear like traveling across the cortical layers. Interestingly, the traveling FPs occur without traveling components of current source density, which represents transmembrane currents associated with neuronal activity. These seemingly odd findings are explained using current source density models of multiple dipoles. Concurrently active, non-traveling dipoles produce FPs as mixtures of FPs produced by individual dipoles, and result in traveling FP waveforms as the mixing ratio depends on the distances from those dipoles. The results suggest that not all traveling FP components are associated with propagating neuronal activity.

A methodological framework for inverse-modeling of propagating cortical activity using MEG/EEG

The prevailing view on the dynamics of large-scale electrical activity in the human cortex is that it constitutes a functional network of discrete and localized circuits. Within this view, a natural way to analyse magnetoencephalographic (MEG) and electroencephalographic (EEG) data is by adopting methods from network theory. Invasive recordings, however, demonstrate that cortical activity is spatially continuous, rather than discrete, and exhibits propagation behavior. Furthermore, human cortical activity is known to propagate under a variety of conditions such as non-REM sleep, general anesthesia, and coma. Although several MEG/EEG studies have investigated propagating cortical activity, not much is known about the conditions under which such activity can be successfully reconstructed from MEG/EEG sensor-data. This study provides a methodological framework for inverse-modeling of propagating cortical activity. Within this framework, cortical activity is represented in the spatial frequency domain, which is more natural than the dipole domain when dealing with spatially continuous activity. We define angular power spectra, which show how the power of cortical activity is distributed across spatial frequencies, angular gain/phase spectra, which characterize the spatial filtering properties of linear inverse operators, and angular resolution matrices, which summarize how linear inverse operators leak signal within and across spatial frequencies. We adopt the framework to provide insight into the performance of several linear inverse operators in reconstructing propagating cortical activity from MEG/EEG sensor-data. We also describe how prior spatial frequency information can be incorporated into the inverse-modeling to obtain better reconstructions.

The Role of Motor and Environmental Visual Rhythms in Structuring Auditory Cortical Excitability

Previous studies indicate that motor sampling patterns modulate neuronal excitability in sensory brain regions by entraining brain rhythms, a process termed motor-initiated entrainment. In addition, rhythms of the external environment are also capable of entraining brain rhythms. Our first goal was to investigate the properties of motor-initiated entrainment in the auditory system using a prominent visual motor sampling pattern in primates, saccades. Second, we wanted to determine whether/how motor-initiated entrainment interacts with visual environmental entrainment. We examined laminar profiles of neuronal ensemble activity in primary auditory cortex and found that whereas motor-initiated entrainment has a suppressive effect, visual environmental entrainment has an enhancive effect. We also found that these processes are temporally coupled, and their temporal relationship ensures that their effect on excitability is complementary rather than interfering. Altogether, our results demonstrate that motor and sensory systems continuously interact in orchestrating the brain's context for the optimal sampling of our multisensory environment.

Impact of Brain Surface Boundary Conditions on Electrophysiology and Implications for Electrocorticography

Volume conduction of electrical potentials in the brain is highly influenced by the material properties and geometry of the tissue and recording devices implanted into the tissue. These effects are very large in EEG due to the volume conduction through the skull and scalp but are often neglected in intracranial electrophysiology. When considering penetrating electrodes deep in the brain, the assumption of an infinite and homogenous medium can be used when the sources are far enough from the brain surface and the electrodes to minimize the boundary effect. When the electrodes are recording from the brain's surface the effect of the boundary cannot be neglected, and the large surface area and commonly used insulating materials in surface electrode arrays may further increase the effect by altering the nature of the boundary in the immediate vicinity of the electrodes. This gives the experimenter some control over the spatial profiles of the potentials by appropriate design of the electrode arrays. We construct a simple three-layer model to describe the effect of material properties and geometry above the brain surface on the electric potentials and conduct empirical experiments to validate this model. A laminar electrode array is used to measure the effect of insulating and relatively conducting layers above the cortical surface by recording evoked potentials alternating between a dried surface and saline covering layer, respectively. Empirically, we find that an insulating boundary amplifies the potentials relative to conductive saline by about a factor of 4, and that the effect is not constrained to potentials that originate near the surface. The model is applied to predict the influence of array design and implantation procedure on the recording amplitude and spatial selectivity of the surface electrode arrays.

Dissociation of broadband high-frequency activity and neuronal firing in the neocortex

Broadband high-frequency activity (BHA; 70 to 150 Hz), also known as "high gamma," a key analytic signal in human intracranial (electrocorticographic) recordings, is often assumed to reflect local neural firing [multiunit activity (MUA)]. As the precise physiological substrates of BHA are unknown, this assumption remains controversial. Our analysis of laminar multielectrode data from V1 and A1 in monkeys outlines two components of stimulus-evoked BHA distributed across the cortical layers: an "early-deep" and "late-superficial" response. Early-deep BHA has a clear spatial and temporal overlap with MUA. Late-superficial BHA was more prominent and accounted for more of the BHA signal measured near the cortical pial surface. However, its association with local MUA is weak and often undetectable, consistent with the view that it reflects dendritic processes separable from local neuronal firing.

Theta Oscillations Alternate With High Amplitude Neocortical Population Within Synchronized States

Synchronized states are marked by large-amplitude low-frequency oscillations in the cortex. These states can be seen during quiet waking or slow-wave sleep. Within synchronized states, previous studies have noted a plethora of different types of activity, including delta oscillations (0.5-4 Hz) and slow oscillations (<1 Hz) in the neocortex and large- and small- irregular activity in the hippocampus. However, it is not still fully characterized how neural populations contribute to the synchronized state. Here we apply independent component analysis to parse which populations are involved in different kinds of neocortical activity, and find two populations that alternate throughout synchronized states. One population broadly affects neocortical deep layers, and is associated with larger amplitude slower neocortical oscillations. The other population exhibits theta-frequency oscillations that are not easily observed in raw field potential recordings. These theta oscillations apparently come from below the neocortex, suggesting hippocampal origin, and are associated with smaller amplitude faster neocortical oscillations. Relative involvement of these two alternating populations may indicate different modes of operation within synchronized states.

Distinct frequency bands in the local field potential are differently tuned to stimulus drift rate

Local field potential (LFP) recorded with a microelectrode reflects the activity of several neural processes, including afferent synaptic inputs, microcircuit-level computations, and spiking activity. Objectively probing their contribution requires a design that allows dissociation between these potential contributors. Earlier reports have shown that the primate lateral geniculate nucleus (LGN) has a higher temporal frequency (drift rate) cutoff than the primary visual cortex (V1), such that at higher drift rates inputs into V1 from the LGN continue to persist, whereas output ceases, permitting partial dissociation. Using chronic microelectrode arrays, we recorded spikes and LFP from V1 of passively fixating macaques while presenting sinusoidal gratings drifting over a wide range. We further optimized the gratings to produce strong gamma oscillations, since recent studies in rodent V1 have reported LGN-dependent narrow-band gamma oscillations. Consistent with earlier reports, power in higher LFP frequencies (above ~140 Hz) tracked the population firing rate and were tuned to preferred drift rates similar to those for spikes. Significantly, power in the lower (up to ~40 Hz) frequencies increased transiently in the early epoch after stimulus onset, even at high drift rates, and had preferred drift rates higher than for spikes/high gamma. Narrow-band gamma (50-80 Hz) power was not strongly correlated with power in high or low frequencies and had much lower preferred temporal frequencies. Our results demonstrate that distinct frequency bands of the V1 LFP show diverse tuning profiles, which may potentially convey different attributes of the underlying neural activity. NEW & NOTEWORTHY In recent years the local field potential (LFP) has been increasingly studied, but interpreting its rich frequency content has been difficult. We use a stimulus manipulation that generates different tuning profiles for low, gamma, and high frequencies of the LFP, suggesting contributions from potentially different sources. Our results have possible implications for design of better neural prosthesis systems and brain-machine interfacing applications.

Top-down, contextual entrainment of neuronal oscillations in the auditory thalamocortical circuit

Our results indicate that nonhuman primates detect complex repeating acoustic sequences in a continuous auditory stream, which is an important precursor for human speech learning and perception. We demonstrate that oscillatory entrainment, known to support the attentive perception of rhythmic stimulus sequences, can occur for rhythms defined solely by stimulus context rather than physical boundaries. As opposed to acoustically driven entrainment by rhythmic tone sequences demonstrated previously, this form of entrainment relies on the brain’s ability to group auditory inputs based on their statistical regularities. The internally initiated, context-driven modulation of excitability in the medial pulvinar prior to A1 supports the notion of top-down entrainment.

Prior studies have shown that repetitive presentation of acoustic stimuli results in an alignment of ongoing neuronal oscillations to the sequence rhythm via oscillatory entrainment by external cues. Our study aimed to explore the neural correlates of the perceptual parsing and grouping of complex repeating auditory patterns that occur based solely on statistical regularities, or context. Human psychophysical studies suggest that the recognition of novel auditory patterns amid a continuous auditory stimulus sequence occurs automatically halfway through the first repetition. We hypothesized that once repeating patterns were detected by the brain, internal rhythms would become entrained, demarcating the temporal structure of these repetitions despite lacking external cues defining pattern on- or offsets. To examine the neural correlates of pattern perception, neuroelectric activity of primary auditory cortex (A1) and thalamic nuclei was recorded while nonhuman primates passively listened to streams of rapidly presented pure tones and bandpass noise bursts. At arbitrary intervals, random acoustic patterns composed of 11 stimuli were repeated five times without any perturbance of the constant stimulus flow. We found significant delta entrainment by these patterns in the A1, medial geniculate body, and medial pulvinar. In A1 and pulvinar, we observed a statistically significant, pattern structure-aligned modulation of neuronal firing that occurred earliest in the pulvinar, supporting the idea that grouping and detecting complex auditory patterns is a top-down, context-driven process. Besides electrophysiological measures, a pattern-related modulation of pupil diameter verified that, like humans, nonhuman primates consciously detect complex repetitive patterns that lack physical boundaries.

Ongoing Alpha Activity in V1 Regulates Visually Driven Spiking Responses

The interlaminar connections in the primate primary visual cortex (V1) are well described, as is the presence of ongoing alpha-range (7-14 Hz) fluctuations in this area. Less well understood is how these interlaminar connections and ongoing fluctuations contribute to the regulation of visual spiking responses. Here, we investigate the relationship between alpha fluctuations and spiking responses to visual stimuli across cortical layers. Using laminar probes in macaque V1, we show that neural firing couples with the phase of alpha fluctuations, and that magnitude of this coupling is particularly pronounced during visual stimulation. The strongest modulation of spiking activity was observed in layers 2/3. Alpha-spike coupling and current source density analysis pointed to an infragranular origin of the alpha fluctuations. Taken together, these results indicate that ongoing infragranular alpha-range fluctuations in V1 play a role in regulating columnar visual activity.

Superficial Slow Rhythms Integrate Cortical Processing in Humans

The neocortex is composed of six anatomically and physiologically specialized layers. It has been proposed that integration of activity across cortical areas is mediated anatomically by associative connections terminating in superficial layers, and physiologically by slow cortical rhythms. However, the means through which neocortical anatomy and physiology interact to coordinate neural activity remains obscure. Using laminar microelectrode arrays in 19 human participants, we found that most EEG activity is below 10-Hz (delta/theta) and generated by superficial cortical layers during both wakefulness and sleep. Cortical surface grid, grid-laminar, and dual-laminar recordings demonstrate that these slow rhythms are synchronous within upper layers across broad cortical areas. The phase of this superficial slow activity is reset by infrequent stimuli and coupled to the amplitude of faster oscillations and neuronal firing across all layers. These findings support a primary role of superficial slow rhythms in generating the EEG and integrating cortical activity.

Linear distributed source modeling of local field potentials recorded with intra-cortical electrode arrays

Planar intra-cortical electrode (Utah) arrays provide a unique window into the spatial organization of cortical activity. Reconstruction of the current source density (CSD) underlying such recordings, however, requires “inverting” Poisson’s equation. For inter-laminar recordings, this is commonly done by the CSD method, which consists in taking the second-order spatial derivative of the recorded local field potentials (LFPs). Although the CSD method has been tremendously successful in mapping the current generators underlying inter-laminar LFPs, its application to planar recordings is more challenging. While for inter-laminar recordings the CSD method seems reasonably robust against violations of its assumptions, is it unclear as to what extent this holds for planar recordings. One of the objectives of this study is to characterize the conditions under which the CSD method can be successfully applied to Utah array data. Using forward modeling, we find that for spatially coherent CSDs, the CSD method yields inaccurate reconstructions due to volume-conducted contamination from currents in deeper cortical layers. An alternative approach is to “invert” a constructed forward model. The advantage of this approach is that any a priori knowledge about the geometrical and electrical properties of the tissue can be taken into account. Although several inverse methods have been proposed for LFP data, the applicability of existing electroencephalographic (EEG) and magnetoencephalographic (MEG) inverse methods to LFP data is largely unexplored. Another objective of our study therefore, is to assess the applicability of the most commonly used EEG/MEG inverse methods to Utah array data. Our main conclusion is that these inverse methods provide more accurate CSD reconstructions than the CSD method. We illustrate the inverse methods using event-related potentials recorded from primary visual cortex of a macaque monkey during a motion discrimination task.

Signatures of Somatic Inhibition and Dendritic Excitation in Auditory Brainstem Field Potentials

Extracellular voltage recordings (V_e ; field potentials) provide an accessible view of in vivo neural activity, but proper interpretation of field potentials is a long-standing challenge. Computational modeling can aid in identifying neural generators of field potentials. In the auditory brainstem of cats, spatial patterns of sound-evoked V_e can resemble, strikingly, V_e generated by current dipoles. Previously, we developed a biophysically-based model of a binaural brainstem nucleus, the medial superior olive (MSO), that accounts qualitatively for observed dipole-like V_e patterns in sustained responses to monaural tones with frequencies >∼1000 Hz (Goldwyn et al., 2014). We have observed, however, that V_e patterns in cats of both sexes appear more monopole-like for lower-frequency tones. Here, we enhance our theory to accurately reproduce dipole and non-dipole features of V_e responses to monaural tones with frequencies ranging from 600 to 1800 Hz. By applying our model to data, we estimate time courses of paired input currents to MSO neurons. We interpret these inputs as dendrite-targeting excitation and soma-targeting inhibition (the latter contributes non-dipole-like features to V_e responses). Aspects of inferred inputs are consistent with synaptic inputs to MSO neurons including the tendencies of inhibitory inputs to attenuate in response to high-frequency tones and to precede excitatory inputs. Importantly, our updated theory can be tested experimentally by blocking synaptic inputs. MSO neurons perform a critical role in sound localization and binaural hearing. By solving an inverse problem to uncover synaptic inputs from V_e patterns we provide a new perspective on MSO physiology.SIGNIFICANCE STATEMENT Extracellular voltages (field potentials) are a common measure of brain activity. Ideally, one could infer from these data the activity of neurons and synapses that generate field potentials, but this "inverse problem" is not easily solved. We study brainstem field potentials in the region of the medial superior olive (MSO); a critical center in the auditory pathway. These field potentials exhibit distinctive spatial and temporal patterns in response to pure tone sounds. We use mathematical modeling in combination with physiological and anatomical knowledge of MSO neurons to plausibly explain how dendrite-targeting excitation and soma-targeting inhibition generate these field potentials. Inferring putative synaptic currents from field potentials advances our ability to study neural processing of sound in the MSO.

Primary Generators of Visually Evoked Field Potentials Recorded in the Macaque Auditory Cortex

Prior studies have reported "local" field potential (LFP) responses to faces in the macaque auditory cortex and have suggested that such face-LFPs may be substrates of audiovisual integration. However, although field potentials (FPs) may reflect the synaptic currents of neurons near the recording electrode, due to the use of a distant reference electrode, they often reflect those of synaptic activity occurring in distant sites as well. Thus, FP recordings within a given brain region (e.g., auditory cortex) may be "contaminated" by activity generated elsewhere in the brain. To determine whether face responses are indeed generated within macaque auditory cortex, we recorded FPs and concomitant multiunit activity with linear array multielectrodes across auditory cortex in three macaques (one female), and applied current source density (CSD) analysis to the laminar FP profile. CSD analysis revealed no appreciable local generator contribution to the visual FP in auditory cortex, although we did note an increase in the amplitude of visual FP with cortical depth, suggesting that their generators are located below auditory cortex. In the underlying inferotemporal cortex, we found polarity inversions of the main visual FP components accompanied by robust CSD responses and large-amplitude multiunit activity. These results indicate that face-evoked FP responses in auditory cortex are not generated locally but are volume-conducted from other face-responsive regions. In broader terms, our results underscore the caution that, unless far-field contamination is removed, LFPs in general may reflect such "far-field" activity, in addition to, or in absence of, local synaptic responses.SIGNIFICANCE STATEMENT Field potentials (FPs) can index neuronal population activity that is not evident in action potentials. However, due to volume conduction, FPs may reflect activity in distant neurons superimposed upon that of neurons close to the recording electrode. This is problematic as the default assumption is that FPs originate from local activity, and thus are termed "local" (LFP). We examine this general problem in the context of previously reported face-evoked FPs in macaque auditory cortex. Our findings suggest that face-FPs are indeed generated in the underlying inferotemporal cortex and volume-conducted to the auditory cortex. The note of caution raised by these findings is of particular importance for studies that seek to assign FP/LFP recordings to specific cortical layers.

A quantitative theory of gamma synchronization in macaque V1

Gamma-band synchronization coordinates brief periods of excitability in oscillating neuronal populations to optimize information transmission during sensation and cognition. Commonly, a stable, shared frequency over time is considered a condition for functional neural synchronization. Here, we demonstrate the opposite: instantaneous frequency modulations are critical to regulate phase relations and synchronization. In monkey visual area V1, nearby local populations driven by different visual stimulation showed different gamma frequencies. When similar enough, these frequencies continually attracted and repulsed each other, which enabled preferred phase relations to be maintained in periods of minimized frequency difference. Crucially, the precise dynamics of frequencies and phases across a wide range of stimulus conditions was predicted from a physics theory that describes how weakly coupled oscillators influence each other's phase relations. Hence, the fundamental mathematical principle of synchronization through instantaneous frequency modulations applies to gamma in V1 and is likely generalizable to other brain regions and rhythms.

Gamma Oscillations in the Rat Ventral Striatum Originate in the Piriform Cortex

Local field potentials (LFPs) recorded from the human and rodent ventral striatum (vStr) exhibit prominent, behaviorally relevant gamma-band oscillations. These oscillations are related to local spiking activity and transiently synchronize with anatomically related areas, suggesting a possible role in organizing vStr activity. However, the origin of vStr gamma is unknown. We recorded vStr gamma oscillations across a 1.4 mm² grid spanned by 64 recording electrodes as male rats rested and foraged for rewards, revealing a highly consistent power gradient originating in the adjacent piriform cortex. Phase differences across the vStr were consistently small (<15°) and current source density analysis further confirmed the absence of local sink-source pairs in the vStr. Reversible occlusions of the ipsilateral (but not contralateral) nostril, known to abolish gamma oscillations in the piriform cortex, strongly reduced vStr gamma power and the occurrence of transient gamma-band events. These results imply that local circuitry is not a major contributor to gamma oscillations in the vStr LFP and that piriform cortex is an important driver of gamma-band oscillations in the vStr and associated limbic areas.SIGNIFICANCE STATEMENT The ventral striatum (vStr) is an area of anatomical convergence in circuits underlying motivated behavior, but it remains unclear how its inputs from different sources interact. A major proposal about how neural circuits may switch dynamically between convergent inputs is through temporal organization reflected in local field potential (LFP) oscillations. Our results show that, in the rat, the mechanisms controlling gamma-band oscillations in the vStr LFP are primarily located in the in the adjacent piriform cortex rather than in the vStr itself, providing a novel interpretation of previous rodent work on gamma oscillations in the vStr and related circuits and an important consideration for future work seeking to use oscillations in these areas as biomarkers for behavioral and neurological disorders.

Shaping Intrinsic Neural Oscillations with Periodic Stimulation

Rhythmic brain activity plays an important role in neural processing and behavior. Features of these oscillations, including amplitude, phase, and spectrum, can be influenced by internal states (e.g., shifts in arousal, attention or cognitive ability) or external stimulation. Electromagnetic stimulation techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, and transcranial alternating current stimulation are used increasingly in both research and clinical settings. Currently, the mechanisms whereby time-dependent external stimuli influence population-scale oscillations remain poorly understood. Here, we provide computational insights regarding the mapping between periodic pulsatile stimulation parameters such as amplitude and frequency and the response dynamics of recurrent, nonlinear spiking neural networks. Using a cortical model built of excitatory and inhibitory neurons, we explored a wide range of stimulation intensities and frequencies systematically. Our results suggest that rhythmic stimulation can form the basis of a control paradigm in which one can manipulate the intrinsic oscillatory properties of driven networks via a plurality of input-driven mechanisms. Our results show that, in addition to resonance and entrainment, nonlinear acceleration is involved in shaping the rhythmic response of our modeled network. Such nonlinear acceleration of spontaneous and synchronous oscillatory activity in a neural network occurs in regimes of intense, high-frequency rhythmic stimulation. These results open new perspectives on the manipulation of synchronous neural activity for basic and clinical research.

SIGNIFICANCE STATEMENT

Oscillatory activity is widely recognized as a core mechanism for information transmission within and between brain circuits. Noninvasive stimulation methods can shape this activity, something that is increasingly capitalized upon in basic research and clinical practice. Here, we provide computational insights on the mechanistic bases for such effects. Our results show that rhythmic stimulation forms the basis of a control paradigm in which one can manipulate the intrinsic oscillatory properties of driven networks via a plurality of input-driven mechanisms. In addition to resonance and entrainment, nonlinear acceleration is involved in shaping the rhythmic response of our modeled network, particularly in regimes of high-frequency rhythmic stimulation. These results open new perspectives on the manipulation of synchronous neural activity for basic and clinical research.

The impact of ultra-high field MRI on cognitive and computational neuroimaging

The ability to measure functional brain responses non-invasively with ultra high field MRI (7 T and above) represents a unique opportunity in advancing our understanding of the human brain. Compared to lower fields (3 T and below), ultra high field MRI has an increased sensitivity, which can be used to acquire functional images with greater spatial resolution, and greater specificity of the blood oxygen level dependent (BOLD) signal to the underlying neuronal responses. Together, increased resolution and specificity enable investigating brain functions at a submillimeter scale, which so far could only be done with invasive techniques. At this mesoscopic spatial scale, perception, cognition and behavior can be probed at the level of fundamental units of neural computations, such as cortical columns, cortical layers, and subcortical nuclei. This represents a unique and distinctive advantage that differentiates ultra high from lower field imaging and that can foster a tighter link between fMRI and computational modeling of neural networks. So far, functional brain mapping at submillimeter scale has focused on the processing of sensory information and on well-known systems for which extensive information is available from invasive recordings in animals. It remains an open challenge to extend this methodology to uniquely human functions and, more generally, to systems for which animal models may be problematic. To succeed, the possibility to acquire high-resolution functional data with large spatial coverage, the availability of computational models of neural processing as well as accurate biophysical modeling of neurovascular coupling at mesoscopic scale all appear necessary.

Evaluation of cortical local field potential diffusion in stereotactic electro-encephalography recordings: A glimpse on white matter signal

While there is a strong interest in meso-scale field potential recording using intracranial electroencephalography with penetrating depth electrodes (i.e. stereotactic EEG or S-EEG) in humans, the signal recorded in the white matter remains ignored. White matter is generally considered electrically neutral and often included in the reference montage. Moreover, re-referencing electrophysiological data is a critical preprocessing choice that could drastically impact signal content and consequently the results of any given analysis. In the present stereotactic electroencephalography study, we first illustrate empirically the consequences of commonly used references (subdermal, white matter, global average, local montage) on inter-electrode signal correlation. Since most of these reference montages incorporate white matter signal, we next consider the difference between signals recorded in cortical gray matter and white matter. Our results reveal that electrode contacts located in the white matter record a mixture of activity, with part arising from the volume conduction (zero time delay) of activity from nearby gray matter. Furthermore, our analysis shows that white matter signal may be correlated with distant gray matter signal. While residual passive electrical spread from nearby matter may account for this relationship, our results suggest the possibility that this long distance correlation arises from the white matter fiber tracts themselves (i.e. activity from distant gray matter traveling along axonal fibers with time lag larger than zero); yet definitive conclusions about the origin of the white matter signal would require further experimental substantiation. By characterizing the properties of signals recorded in white matter and in gray matter, this study illustrates the importance of including anatomical prior knowledge when analyzing S-EEG data.

Discrepancies between Multi-Electrode LFP and CSD Phase-Patterns: A Forward Modeling Study

Multi-electrode recordings of local field potentials (LFPs) provide the opportunity to investigate the spatiotemporal organization of neural activity on the scale of several millimeters. In particular, the phases of oscillatory LFPs allow studying the coordination of neural oscillations in time and space and to tie it to cognitive processing. Given the computational roles of LFP phases, it is important to know how they relate to the phases of the underlying current source densities (CSDs) that generate them. Although CSDs and LFPs are distinct physical quantities, they are often (implicitly) identified when interpreting experimental observations. That this identification is problematic is clear from the fact that LFP phases change when switching to different electrode montages, while the underlying CSD phases remain unchanged. In this study we use a volume-conductor model to characterize discrepancies between LFP and CSD phase-patterns, to identify the contributing factors, and to assess the effect of different electrode montages. Although we focus on cortical LFPs recorded with two-dimensional (Utah) arrays, our findings are also relevant for other electrode configurations. We found that the main factors that determine the discrepancy between CSD and LFP phase-patterns are the frequency of the neural oscillations and the extent to which the laminar CSD profile is balanced. Furthermore, the presence of laminar phase-differences in cortical oscillations, as commonly observed in experiments, precludes identifying LFP phases with those of the CSD oscillations at a given cortical depth. This observation potentially complicates the interpretation of spike-LFP coherence and spike-triggered LFP averages. With respect to reference strategies, we found that the average-reference montage leads to larger discrepancies between LFP and CSD phases as compared with the referential montage, while the Laplacian montage reduces these discrepancies. We therefore advice to conduct analysis of two-dimensional LFP recordings using the Laplacian montage.

Low- and high-gamma oscillations deviate in opposite directions from zero-phase synchrony in the limbic corticostriatal loop

The loop structure of cortico-striatal anatomy in principle enables both descending (cortico-striatal) and ascending (striato-cortical) influences, but the factors that regulate the flow of information in these loops are not known. We report that low- and high-gamma oscillations (∼50 and ∼80 Hz, respectively) in the local field potential of freely moving rats are highly synchronous between the infralimbic region of the medial prefrontal cortex (mPFC) and the ventral striatum (vStr). Strikingly, high-gamma oscillations in mPFC preceded those in vStr, whereas low-gamma oscillations in mPFC lagged those in vStr, with short (∼1 ms) time lags. These systematic deviations from zero-phase synchrony were consistent across measures based on amplitude cross-correlation and phase slopes and were robustly maintained between behavioral states and different individual subjects. Furthermore, low- and high-gamma oscillations were associated with distinct ensemble spiking patterns in vStr, even when controlling for overt behavioral differences and slow changes in neural activity. These results imply that neural activity in vStr and mPFC is tightly coupled at the gamma timescale and raise the intriguing possibility that frequency-specific deviations from this coupling may signal transient leader-follower switches.

Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations

Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method leads to erroneous results.

Laminar Profile and Physiology of the α Rhythm in Primary Visual, Auditory, and Somatosensory Regions of Neocortex

The functional significance of the α rhythm is widely debated. It has been proposed that α reflects sensory inhibition and/or a temporal sampling or "parsing" mechanism. There is also continuing disagreement over the more fundamental questions of which cortical layers generate α rhythms and whether the generation of α is equivalent across sensory systems. To address these latter questions, we analyzed laminar profiles of local field potentials (LFPs) and concomitant multiunit activity (MUA) from macaque V1, S1, and A1 during both spontaneous activity and sensory stimulation. Current source density (CSD) analysis of laminar LFP profiles revealed α current generators in the supragranular, granular, and infragranular layers. MUA phase-locked to local current source/sink configurations confirmed that α rhythms index local neuronal excitability fluctuations. CSD-defined α generators were strongest in the supragranular layers, whereas LFP α power was greatest in the infragranular layers, consistent with some of the previous reports. The discrepancy between LFP and CSD findings appears to be attributable to contamination of the infragranular LFP signal by activity that is volume-conducted from the stronger supragranular α generators. The presence of α generators across cortical depth in V1, S1, and A1 suggests the involvement of α in feedforward as well as feedback processes and is consistent with the view that α rhythms, perhaps in addition to a role in sensory inhibition, may parse sensory input streams in a way that facilitates communication across cortical areas.

SIGNIFICANCE STATEMENT

The α rhythm is thought to reflect sensory inhibition and/or a temporal parsing mechanism. Here, we address two outstanding issues: (1) whether α is a general mechanism across sensory systems and (2) which cortical layers generate α oscillations. Using intracranial recordings from macaque V1, S1, and A1, we show α band activity with a similar spectral and laminar profile in each of these sensory areas. Furthermore, α generators were present in each of the cortical layers, with a strong source in superficial layers. We argue that previous findings, locating α generators exclusively in the deeper layers, were biased because of use of less locally specific local field potential measurements. The laminar distribution of α band activity appears more complex than generally assumed.

Theta-rhythmic drive between medial septum and hippocampus in slow-wave sleep and microarousal: a Granger causality analysis

Medial septum (MS) plays a critical role in controlling the electrical activity of the hippocampus (HIPP). In particular, theta-rhythmic burst firing of MS neurons is thought to drive lasting HIPP theta oscillations in rats during waking motor activity and REM sleep. Less is known about MS-HIPP interactions in nontheta states such as non-REM sleep, in which HIPP theta oscillations are absent but theta-rhythmic burst firing in subsets of MS neurons is preserved. The present study used Granger causality (GC) to examine the interaction patterns between MS and HIPP in slow-wave sleep (SWS, a nontheta state) and during its short interruptions called microarousals (a transient theta state). We found that during SWS, while GC revealed a unidirectional MS→HIPP influence over a wide frequency band (2-12 Hz, maximum: ∼8 Hz), there was no theta peak in the hippocampal power spectra, indicating a lack of theta activity in HIPP. In contrast, during microarousals, theta peaks were seen in both MS and HIPP power spectra and were accompanied by bidirectional GC with MS→HIPP and HIPP→MS theta drives being of equal magnitude. Thus GC in a nontheta state (SWS) vs. a theta state (microarousal) primarily differed in the level of HIPP→MS. The present findings suggest a modification of our understanding of the role of MS as the theta generator in two regards. First, a MS→HIPP theta drive does not necessarily induce theta field oscillations in the hippocampus, as found in SWS. Second, HIPP theta oscillations entail bidirectional theta-rhythmic interactions between MS and HIPP.

Auditory properties in the parabelt regions of the superior temporal gyrus in the awake macaque monkey: an initial survey

The superior temporal gyrus (STG) is on the inferior-lateral brain surface near the external ear. In macaques, 2/3 of the STG is occupied by an auditory cortical region, the "parabelt," which is part of a network of inferior temporal areas subserving communication and social cognition as well as object recognition and other functions. However, due to its location beneath the squamous temporal bone and temporalis muscle, the STG, like other inferior temporal regions, has been a challenging target for physiological studies in awake-behaving macaques. We designed a new procedure for implanting recording chambers to provide direct access to the STG, allowing us to evaluate neuronal properties and their topography across the full extent of the STG in awake-behaving macaques. Initial surveys of the STG have yielded several new findings. Unexpectedly, STG sites in monkeys that were listening passively responded to tones with magnitudes comparable to those of responses to 1/3 octave band-pass noise. Mapping results showed longer response latencies in more rostral sites and possible tonotopic patterns parallel to core and belt areas, suggesting the reversal of gradients between caudal and rostral parabelt areas. These results will help further exploration of parabelt areas.

Microcircuitry of agranular frontal cortex: contrasting laminar connectivity between occipital and frontal areas

Neocortex is striking in its laminar architecture. Tracer studies have uncovered anatomical connectivity among laminae, but the functional connectivity between laminar compartments is still largely unknown. Such functional connectivity can be discerned through spontaneous neural correlations during rest. Previous work demonstrated a robust pattern of mesoscopic resting-state connectivity in macaque primary visual cortex (V1) through interlaminar cross-frequency coupling. Here we investigated whether this pattern generalizes to other cortical areas by comparing resting-state laminar connectivity between V1 and the supplementary eye field (SEF), a frontal area lacking a granular layer 4 (L4). Local field potentials (LFPs) were recorded with linear microelectrode arrays from all laminae of granular V1 and agranular SEF while monkeys rested in darkness. We found substantial differences in the relationship between the amplitude of gamma-band (>30 Hz) LFP and the phase of alpha-band (7-14 Hz) LFP between these areas. In V1, gamma amplitudes in L2/3 and L5 were coupled with alpha-band LFP phase in L5, as previously described. In contrast, in SEF phase-amplitude coupling was prominent within L3 and much weaker across layers. These results suggest that laminar interactions in agranular SEF are unlike those in granular V1. Thus the intrinsic functional connectivity of the cortical microcircuit does not seem to generalize across cortical areas.