How local is the local field potential?

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm² patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

Time evolution of interhemispheric coupling in a model of focal neocortical epilepsy in mice

Epilepsy is characterized by substantial network rearrangements leading to spontaneous seizures and little is known on how an epileptogenic focus impacts on neural activity in the contralateral hemisphere. Here, we used a model of unilateral epilepsy induced by injection of the synaptic blocker tetanus neurotoxin (TeNT) in the mouse primary visual cortex (V1). Local field potential (LFP) signals were simultaneously recorded from both hemispheres of each mouse in acute phase (peak of toxin action) and chronic condition (completion of TeNT effects). To characterize the neural electrical activities the corresponding LFP signals were analyzed with several methods of time series analysis. For the epileptic mice, the spectral analysis showed that TeNT determines a power redistribution among the different neurophysiological bands in both acute and chronic phases. Using linear and nonlinear interdependence measures in both time and frequency domains, it was found in the acute phase that TeNT injection promotes a reduction of the interhemispheric coupling for high frequencies (12-30 Hz) and small time lag (<20 ms), whereas an increase of the coupling is present for low frequencies (0.5-4 Hz) and long time lag (>40 ms). On the other hand, the chronic period is characterized by a partial or complete recovery of the interhemispheric interdependence level. Granger causality test and symbolic transfer entropy indicate a greater driving influence of the TeNT-injected side on activity in the contralateral hemisphere in the chronic phase. Lastly, based on experimental observations, we built a computational model of LFPs to investigate the role of the ipsilateral inhibition and exicitatory interhemispheric connections in the dampening of the interhemispheric coupling. The time evolution of the interhemispheric coupling in such a relevant model of epilepsy has been addressed here.

Short- and long-term dopamine depletion causes enhanced beta oscillations in the cortico-basal ganglia loop of parkinsonian rats

Abnormally enhanced beta oscillations have been found in deep brain recordings from human Parkinson's disease (PD) patients and in animal models of PD. Recent correlative evidence suggests that beta oscillations are related to disease-specific symptoms such as akinesia and rigidity. However, this hypothesis has also been repeatedly questioned by studies showing no changes in beta power in animal models using an acute pharmacologic dopamine blockade. To further investigate the temporal dynamics of exaggerated beta synchrony in PD, we investigated the reserpine model, which is characterized by an acute and stable disruption of dopamine transmission, and compared it to the chronic progressive 6-hydroxydopamine (6-OHDA) model. Using simultaneous electrophysiological recordings in urethane anesthetized rats from the primary motor cortex, the subthalamic nucleus and the reticulate part of the substantia, we found evidence for enhanced beta oscillations in the basal ganglia of both animal models during the activated network state. In contrast to 6-OHDA, reserpine treated animals showed no involvement of primary motor cortex. Notably, beta coherence levels between primary motor cortex and basal ganglia nuclei were elevated in both models. Although both models exhibited elevated beta power and coherence levels, they differed substantially in respect to their mean peak frequency: while the 6-OHDA peak was located in the low beta range (17Hz), the reserpine peak was centered at higher beta frequencies (27Hz). Our results further support the hypothesis of an important pathophysiological relation between enhanced beta activity and akinesia in parkinsonism.

Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography

OBJECTIVE

Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram.

APPROACH

Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient.

MAIN RESULTS

The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies.

SIGNIFICANCE

Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and across cortical areas during activity.

Metabolic demands of neural-hemodynamic associated and disassociated areas in brain

Interpretation of regional blood oxygenation level-dependent (BOLD) responses in functional magnetic resonance imaging (fMRI) is contingent on whether local field potential (LFP) and multi-unit activity (MUA) is either dissociated or associated. To examine whether neural-hemodynamic associated and dissociated areas have different metabolic demands, we recorded sensory-evoked responses of BOLD signal, blood flow (CBF), and blood volume (CBV), which with calibrated fMRI provided oxidative metabolism (CMRO2) from rat's ventral posterolateral thalamic nucleus (VPL) and somatosensory forelimb cortex (S1FL) and compared these neuroimaging signals to neurophysiological recordings. MUA faithfully recorded evoked latency differences between VPL and S1FL because evoked MUA in these regions were similar in magnitude. Since evoked LFP was significantly attenuated in VPL, we extracted the time courses of the weaker thalamic LFP to compare with the stronger cortical LFP using wavelet transform. BOLD and CBV responses were greater in S1FL than in VPL, similar to LFP regional differences. CBF and CMRO2 responses were both comparably larger in S1FL and VPL. Despite different levels of CBF-CMRO2 and LFP-MUA couplings in VPL and S1FL, the CMRO2 was well matched with MUA in both regions. These results suggest that neural-hemodynamic associated and dissociated areas in VPL and S1FL can have similar metabolic demands.

Two subdivisions of macaque LIP process visual-oculomotor information differently

Although the cerebral cortex is thought to be composed of functionally distinct areas, the actual parcellation of area and assignment of function are still highly controversial. An example is the much-studied lateral intraparietal cortex (LIP). Despite the general agreement that LIP plays an important role in visual-oculomotor transformation, it remains unclear whether the area is primary sensory- or motor-related (the attention-intention debate). Although LIP has been considered as a functionally unitary area, its dorsal (LIPd) and ventral (LIPv) parts differ in local morphology and long-distance connectivity. In particular, LIPv has much stronger connections with two oculomotor centers, the frontal eye field and the deep layers of the superior colliculus, than does LIPd. Such anatomical distinctions imply that compared with LIPd, LIPv might be more involved in oculomotor processing. We tested this hypothesis physiologically with a memory saccade task and a gap saccade task. We found that LIP neurons with persistent memory activities in memory saccade are primarily provoked either by visual stimulation (vision-related) or by both visual and saccadic events (vision-saccade-related) in gap saccade. The distribution changes from predominantly vision-related to predominantly vision-saccade-related as the recording depth increases along the dorsal-ventral dimension. Consistently, the simultaneously recorded local field potential also changes from visual evoked to saccade evoked. Finally, local injection of muscimol (GABA agonist) in LIPv, but not in LIPd, dramatically decreases the proportion of express saccades. With these results, we conclude that LIPd and LIPv are more involved in visual and visual-saccadic processing, respectively.

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.

Spatial spread of local field potential is band-pass in the primary visual cortex

Local field potential (LFP) is a valuable tool in understanding brain function and in brain machine-interfacing applications. However, there is no consensus on the spatial extent of the cortex that contributes to the LFP (its "spatial spread"), with different studies reporting values between a few hundred micrometers and several millimeters. Furthermore, the dependency of the spatial spread on frequency, which could reflect properties of the network architecture and extracellular medium, is not well studied, with theory and models predicting either "all-pass" (frequency-independent) or "low-pass" behavior. Surprisingly, we found the LFP spread to be "band-pass" in the primate primary visual cortex, with the greatest spread in the high-gamma range (60-150 Hz). This was accompanied by an increase in phase coherency across neighboring sites in the same frequency range, consistent with the findings of a recent model that reconciles previous studies by suggesting that spatial spread depends on neuronal correlations.

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.

Shielded Coaxial Optrode Arrays for Neurophysiology

Recent progress in the study of the brain has been greatly facilitated by the development of new tools capable of minimally-invasive, robust coupling to neuronal assemblies. Two prominent examples are the microelectrode array (MEA), which enables electrical signals from large numbers of neurons to be detected and spatiotemporally correlated, and optogenetics, which enables the electrical activity of cells to be controlled with light. In the former case, high spatial density is desirable but, as electrode arrays evolve toward higher density and thus smaller pitch, electrical crosstalk increases. In the latter, finer control over light input is desirable, to enable improved studies of neuroelectronic pathways emanating from specific cell stimulation. Here, we introduce a coaxial electrode architecture that is uniquely suited to address these issues, as it can simultaneously be utilized as an optical waveguide and a shielded electrode in dense arrays. Using optogenetically-transfected cells on a coaxial MEA, we demonstrate the utility of the architecture by recording cellular currents evoked from optical stimulation. We also show the capability for network recording by radiating an area of seven individually-addressed coaxial electrode regions with cultured cells covering a section of the extent.

Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics

Brain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but very little is known about interregional differences of their intrinsic dynamics at a macro-scale in human. We developed here a new method aiming at mapping the dynamical properties of cortical microcircuits non-invasively using the coupling between robotized transcranial magnetic stimulation and electroencephalography. We recorded the responses evoked by the stimulation of 18 cortical targets largely covering the accessible neocortex in 22 healthy volunteers. Specific data processing methods were developed to map the local source activity of each cortical target, which showed inter-regional differences with very good interhemispheric reproducibility. Functional signatures of cortical microcircuits were further studied using spatio-temporal decomposition of local source activities in order to highlight principal brain modes. The identified brain modes revealed that cortical areas with similar intrinsic dynamical properties could be distributed either locally or not, with a spatial signature that was somewhat reminiscent of resting state networks. Our results provide the proof of concept of "functional cytoarchitectonics", that would guide the parcellation of the human cortex using not only its cytoarchitecture but also its intrinsic responses to local perturbations. This opens new avenues for brain modelling and physiopathology readouts.

Second spatial derivative analysis of cortical surface potentials recorded in cat primary auditory cortex using thin film surface arrays: Comparisons with multi-unit data

BACKGROUND

Current source density analysis of recordings from penetrating electrode arrays has traditionally been used to examine the layer- specific cortical activation and plastic changes associated with changed afferent input. We report on a related analysis, the second spatial derivative (SSD) of surface local field potentials (LFPs) recorded using custom designed thin-film polyimide substrate arrays.

RESULTS

SSD analysis of tone- evoked LFPs generated from the auditory cortex under the recording array demonstrated a stereotypical single local minimum, often flanked by maxima on both the caudal and rostral sides. In contrast, tone-pips at frequencies not represented in the region under the array, but known (on the basis of normal tonotopic organization) to be represented caudal to the recording array, had a more complex pattern of many sources and sinks.

COMPARISON WITH EXISTING METHODS

Compared to traditional analysis of LFPs, SSD analysis produced a tonotopic map that was more similar to that obtained with multi-unit recordings in a normal-hearing animal. Additionally, the statistically significant decrease in the number of acoustically responsive cortical locations in partially deafened cats following 6 months of cochlear implant use compared to unstimulated cases observed with multi-unit data (p=0.04) was also observed with SSD analysis (p=0.02), but was not apparent using traditional analysis of LFPs (p=0.6).

CONCLUSIONS

SSD analysis of surface LFPs from the thin-film array provides a rapid and robust method for examining the spatial distribution of cortical activity with improved spatial resolution compared to more traditional LFP recordings.

Joint analysis of spikes and local field potentials using copula

Recent technological advances, which allow for simultaneous recording of spikes and local field potentials (LFPs) at multiple sites in a given cortical area or across different areas, have greatly increased our understanding of signal processing in brain circuits. Joint analysis of simultaneously collected spike and LFP signals is an important step to explicate how the brain orchestrates information processing. In this contribution, we present a novel statistical framework based on Gaussian copula to jointly model spikes and LFP. In our approach, we use copula to link separate, marginal regression models to construct a joint regression model, in which the binary-valued spike train data are modeled using generalized linear model (GLM) and the continuous-valued LFP data are modeled using linear regression. Model parameters can be efficiently estimated via maximum-likelihood. In particular, we show that our model offers a means to statistically detect directional influence between spikes and LFP, akin to Granger causality measure, and that we are able to assess its statistical significance by conducting a Wald test. Through extensive simulations, we also show that our method is able to reliably recover the true model used to generate the data. To demonstrate the effectiveness of our approach in real setting, we further apply the method to a mixed neural dataset, consisting of spikes and LFP simultaneously recorded from the visual cortex of a monkey performing a contour detection task.

Conundrums of high-frequency oscillations (80-800 Hz) in the epileptic brain

Pathological high-frequency oscillations (HFOs) (80-800 Hz) are considered biomarkers of epileptogenic tissue, but the underlying complex neuronal events are not well understood. Here, we identify and discuss several outstanding issues or conundrums in regards to the recording, analysis, and interpretation of HFOs in the epileptic brain to critically highlight what is known and what is not about these enigmatic events. High-frequency oscillations reflect a range of neuronal processes contributing to overlapping frequencies from the lower 80 Hz to the very fast spectral frequency bands. Given their complex neuronal nature, HFOs are extremely sensitive to recording conditions and analytical approaches. We provide a list of recommendations that could help to obtain comparable HFO signals in clinical and basic epilepsy research. Adopting basic standards will facilitate data sharing and interpretation that collectively will aid in understanding the role of HFOs in health and disease for translational purpose.

Unmasking local activity within local field potentials (LFPs) by removing distal electrical signals using independent component analysis

Local field potentials (LFPs) are commonly thought to reflect the aggregate dynamics in local neural circuits around recording electrodes. However, we show that when LFPs are recorded in awake behaving animals against a distal reference on the skull as commonly practiced, LFPs are significantly contaminated by non-local and non-neural sources arising from the reference electrode and from movement-related noise. In a data set with simultaneously recorded LFPs and electroencephalograms (EEGs) across multiple brain regions while rats perform an auditory oddball task, we used independent component analysis (ICA) to identify signals arising from electrical reference and from volume-conducted noise based on their distributed spatial pattern across multiple electrodes and distinct power spectral features. These sources of distal electrical signals collectively accounted for 23–77% of total variance in unprocessed LFPs, as well as most of the gamma oscillation responses to the target stimulus in EEGs. Gamma oscillation power was concentrated in volume-conducted noise and was tightly coupled with the onset of licking behavior, suggesting a likely origin of muscle activity associated with body movement or orofacial movement. The removal of distal signal contamination also selectively reduced correlations of LFP/EEG signals between distant brain regions but not within the same region. Finally, the removal of contamination from distal electrical signals preserved an event-related potential (ERP) response to auditory stimuli in the frontal cortex and also increased the coupling between the frontal ERP amplitude and neuronal activity in the basal forebrain, supporting the conclusion that removing distal electrical signals unmasked local activity within LFPs. Together, these results highlight the significant contamination of LFPs by distal electrical signals and caution against the straightforward interpretation of unprocessed LFPs. Our results provide a principled approach to identify and remove such contamination to unmask local LFPs.

Spontaneous Neural Dynamics and Multi-scale Network Organization

Spontaneous neural activity has historically been viewed as task-irrelevant noise that should be controlled for via experimental design, and removed through data analysis. However, electrophysiology and functional MRI studies of spontaneous activity patterns, which have greatly increased in number over the past decade, have revealed a close correspondence between these intrinsic patterns and the structural network architecture of functional brain circuits. In particular, by analyzing the large-scale covariation of spontaneous hemodynamics, researchers are able to reliably identify functional networks in the human brain. Subsequent work has sought to identify the corresponding neural signatures via electrophysiological measurements, as this would elucidate the neural origin of spontaneous hemodynamics and would reveal the temporal dynamics of these processes across slower and faster timescales. Here we survey common approaches to quantifying spontaneous neural activity, reviewing their empirical success, and their correspondence with the findings of neuroimaging. We emphasize invasive electrophysiological measurements, which are amenable to amplitude- and phase-based analyses, and which can report variations in connectivity with high spatiotemporal precision. After summarizing key findings from the human brain, we survey work in animal models that display similar multi-scale properties. We highlight that, across many spatiotemporal scales, the covariance structure of spontaneous neural activity reflects structural properties of neural networks and dynamically tracks their functional repertoire.

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.

Post-Stroke Longitudinal Alterations of Inter-Hemispheric Correlation and Hemispheric Dominance in Mouse Pre-Motor Cortex

Purpose

Limited restoration of function is known to occur spontaneously after an ischemic injury to the primary motor cortex. Evidence suggests that Pre-Motor Areas (PMAs) may “take over” control of the disrupted functions. However, little is known about functional reorganizations in PMAs. Forelimb movements in mice can be driven by two cortical regions, Caudal and Rostral Forelimb Areas (CFA and RFA), generally accepted as primary motor and pre-motor cortex, respectively. Here, we examined longitudinal changes in functional coupling between the two RFAs following unilateral photothrombotic stroke in CFA (mm from Bregma: +0.5 anterior, +1.25 lateral).

Methods

Local field potentials (LFPs) were recorded from the RFAs of both hemispheres in freely moving injured and naïve mice. Neural signals were acquired at 9, 16 and 23 days after surgery (sub-acute period in stroke animals) through one bipolar electrode per hemisphere placed in the center of RFA, with a ground screw over the occipital bone. LFPs were pre-processed through an efficient method of artifact removal and analysed through: spectral,cross-correlation, mutual information and Granger causality analysis.

Results

Spectral analysis demonstrated an early decrease (day 9) in the alpha band power in both the RFAs. In the late sub-acute period (days 16 and 23), inter-hemispheric functional coupling was reduced in ischemic animals, as shown by a decrease in the cross-correlation and mutual information measures. Within the gamma and delta bands, correlation measures were already reduced at day 9. Granger analysis, used as a measure of the symmetry of the inter-hemispheric causal connectivity, showed a less balanced activity in the two RFAs after stroke, with more frequent oscillations of hemispheric dominance.

Conclusions

These results indicate robust electrophysiological changes in PMAs after stroke. Specifically, we found alterations in transcallosal connectivity, with reduced inter-hemispheric functional coupling and a fluctuating dominance pattern. These reorganizations may underlie vicariation of lost functions following stroke.

A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls

Oscillatory neuronal activity may provide a mechanism for dynamic network coordination. Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own advantages and disadvantages. This tutorial will review and summarize current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations. First, we review metrics for functional connectivity, including coherence, phase synchronization, phase-slope index, and Granger causality, with the specific aim to provide an intuition for how these metrics work, as well as their quantitative definition. Next, we highlight a number of interpretational caveats and common pitfalls that can arise when performing functional connectivity analysis, including the common reference problem, the signal to noise ratio problem, the volume conduction problem, the common input problem, and the sample size bias problem. These pitfalls will be illustrated by presenting a set of MATLAB-scripts, which can be executed by the reader to simulate each of these potential problems. We discuss how these issues can be addressed using current methods.

Neural Activity Patterns Underlying Spatial Coding in the Hippocampus

The hippocampus is well known as a central site for memory processing-critical for storing and later retrieving the experiences events of daily life so they can be used to shape future behavior. Much of what we know about the physiology underlying hippocampal function comes from spatial navigation studies in rodents, which have allowed great strides in understanding how the hippocampus represents experience at the cellular level. However, it remains a challenge to reconcile our knowledge of spatial encoding in the hippocampus with its demonstrated role in memory-dependent tasks in both humans and other animals. Moreover, our understanding of how networks of neurons coordinate their activity within and across hippocampal subregions to enable the encoding, consolidation, and retrieval of memories is incomplete. In this chapter, we explore how information may be represented at the cellular level and processed via coordinated patterns of activity throughout the subregions of the hippocampal network.

On the photovoltaic effect in local field potential recordings

Optogenetics allows light activation of genetically defined cell populations and the study of their link to specific brain functions. While it is a powerful method that has revolutionized neuroscience in the last decade, the shortcomings of directly stimulating electrodes and living tissue with light have been poorly characterized. Here, we assessed the photovoltaic effects in local field potential (LFP) recordings of the mouse hippocampus. We found that light leads to several artifacts that resemble genuine LFP features in animals with no opsin expression, such as stereotyped peaks at the power spectrum, phase shifts across different recording channels, coupling between low and high oscillation frequencies, and sharp signal deflections that are detected as spikes. Further, we tested how light stimulation affected hippocampal LFP recordings in mice expressing channelrhodopsin 2 in parvalbumin neurons (PV/ChR2 mice). Genuine oscillatory activity at the frequency of light stimulation could not be separated from light-induced artifacts. In addition, light stimulation in PV/ChR2 mice led to an overall decrease in LFP power. Thus, genuine LFP changes caused by the stimulation of specific cell populations may be intermingled with spurious changes caused by photovoltaic effects. Our data suggest that care should be taken in the interpretation of electrophysiology experiments involving light stimulation.

Laminar Distribution of Phase-Amplitude Coupling of Spontaneous Current Sources and Sinks

Although resting-state functional connectivity is a commonly used neuroimaging paradigm, the underlying mechanisms remain unknown. Thalamo-cortical and cortico-cortical circuits generate oscillations at different frequencies during spontaneous activity. However, it remains unclear how the various rhythms interact and whether their interactions are lamina-specific. Here we investigated intra- and inter-laminar spontaneous phase-amplitude coupling (PAC). We recorded local-field potentials using laminar probes inserted in the forelimb representation of rat area S1. We then computed time-series of frequency-band- and lamina-specific current source density (CSD), and PACs of CSD for all possible pairs of the classical frequency bands in the range of 1–150 Hz. We observed both intra- and inter-laminar spontaneous PAC. Of 18 possible combinations, 12 showed PAC, with the highest measures of interaction obtained for the pairs of the theta/gamma and delta/gamma bands. Intra- and inter-laminar PACs involving layers 2/3–5a were higher than those involving layer 6. Current sinks (sources) in the delta band were associated with increased (decreased) amplitudes of high-frequency signals in the beta to fast gamma bands throughout layers 2/3–6. Spontaneous sinks (sources) of the theta and alpha bands in layers 2/3–4 were on average linked to dipoles completed by sources (sinks) in layer 6, associated with high (low) amplitudes of the beta to fast-gamma bands in the entire cortical column. Our findings show that during spontaneous activity, delta, theta, and alpha oscillations are associated with periodic excitability, which for the theta and alpha bands is lamina-dependent. They further emphasize the differences between the function of layer 6 and that of the superficial layers, and the role of layer 6 in controlling activity in those layers. Our study links theories on the involvement of PAC in resting-state functional connectivity with previous work that revealed lamina-specific anatomical thalamo-cortico-cortical connections.

Covariation between Spike and LFP Modulations Revealed with Focal and Asynchronous Stimulation of Receptive Field Surround in Monkey Primary Visual Cortex

A focal visual stimulus outside the classical receptive field (RF) of a V1 neuron does not evoke a spike response by itself, and yet evokes robust changes in the local field potential (LFP). This subthreshold LFP provides a unique opportunity to investigate how changes induced by surround stimulation leads to modulation of spike activity. In the current study, two identical Gabor stimuli were sequentially presented with a variable stimulus onset asynchrony (SOA) ranging from 0 to 100 ms: the first (S1) outside the RF and the second (S2) over the RF of primary visual cortex neurons, while trained monkeys performed a fixation task. This focal and asynchronous stimulation of the RF surround enabled us to analyze the modulation of S2-evoked spike activity and covariation between spike and LFP modulation across SOA. In this condition, the modulation of S2-evoked spike response was dominantly facilitative and was correlated with the change in LFP amplitude, which was pronounced for the cells recorded in the upper cortical layers. The time course of covariation between the SOA-dependent spike modulation and LFP amplitude suggested that the subthreshold LFP evoked by the S1 can predict the magnitude of upcoming spike modulation.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes

Direct electrical recording and stimulation of neural activity using micro-fabricated silicon and metal micro-wire probes have contributed extensively to basic neuroscience and therapeutic applications; however, the dimensional and mechanical mismatch of these probes with the brain tissue limits their stability in chronic implants and decreases the neuron-device contact. Here, we demonstrate the realization of a three-dimensional macroporous nanoelectronic brain probe that combines ultra-flexibility and subcellular feature sizes to overcome these limitations. Built-in strains controlling the local geometry of the macroporous devices are designed to optimize the neuron/probe interface and to promote integration with the brain tissue while introducing minimal mechanical perturbation. The ultra-flexible probes were implanted frozen into rodent brains and used to record multiplexed local field potentials and single-unit action potentials from the somatosensory cortex. Significantly, histology analysis revealed filling-in of neural tissue through the macroporous network and attractive neuron-probe interactions, consistent with long-term biocompatibility of the device.

Local Field Potentials Encode Place Cell Ensemble Activation during Hippocampal Sharp Wave Ripples

Whether the activation of spiking cell ensembles can be encoded in the local field potential (LFP) remains unclear. We address this question by combining in vivo electrophysiological recordings in the rat hippocampus with realistic biophysical modeling, and explore the LFP of place cell sequence spiking ("replays") during sharp wave ripples. We show that multi-site perisomatic LFP amplitudes, in the ∼150-200 Hz frequency band, reliably reflect spatial constellations of spiking cells, embedded within non-spiking populations, and encode activation of local place cell ensembles during in vivo replays. We find spatiotemporal patterns in the LFP, which remain consistent between sequence replays, in conjunction with the ordered activation of place cell ensembles. Clustering such patterns provides an efficient segregation of replay events from non-replay-associated ripples. This work demonstrates how spatiotemporal ensemble spiking is encoded extracellularly, providing a window for efficient, LFP-based detection and monitoring of structured population activity in vivo.

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.

Quantification of mid and late evoked sinks in laminar current source density profiles of columns in the primary auditory cortex

Current source density (CSD) analysis assesses spatiotemporal synaptic activations at somatic and/or dendritic levels in the form of depolarizing current sinks. Whereas many studies have focused on the short (<50 ms) latency sinks, associated with thalamocortical projections, sinks with longer latencies have received less attention. Here, we analyzed laminar CSD patterns for the first 600 ms after stimulus onset in the primary auditory cortex of Mongolian gerbils. By applying an algorithm for contour calculation, three distinct mid and four late evoked sinks were identified in layers I, III, Va, VIa, and VIb. Our results further showed that the patterns of intracortical information-flow remained qualitatively similar for low and for high sound pressure level stimuli at the characteristic frequency (CF) as well as for stimuli ± 1 octave from CF. There were, however, differences associated with the strength, vertical extent, onset latency, and duration of the sinks for the four stimulation paradigms used. Stimuli one octave above the most sensitive frequency evoked a new, and quite reliable, sink in layer Va whereas low level stimulation led to the disappearance of the layer VIb sink. These data indicate the presence of input sources specifically activated in response to level and/or frequency parameters. Furthermore, spectral integration above vs. below the CF of neurons is asymmetric as illustrated by CSD profiles. These results are important because synaptic feedback associated with mid and late sinks—beginning at 50 ms post stimulus latency—is likely crucial for response modulation resulting from higher order processes like memory, learning or cognitive control.

Evaluation of local field potential signals in decoding of visual attention

In the field of brain research, attention as one of the main issues in cognitive neuroscience is an important mechanism to be studied. The complicated structure of the brain cannot process all the information it receives at any moment. Attention, in fact, is considered as a possible useful mechanism in which brain concentrates on the processing of important information which is required at any certain moment. The main goal of this study is decoding the location of visual attention from local field potential signals recorded from medial temporal (MT) area of a macaque monkey. To this end, feature extraction and feature selection are applied in both the time and the frequency domains. After applying feature extraction methods such as the short time Fourier transform, continuous wavelet transform (CWT), and wavelet energy (scalogram), feature selection methods are evaluated. Feature selection methods used here are T-test, Entropy, receiver operating characteristic, and Bhattacharyya. Subsequently, different classifiers are utilized in order to decode the location of visual attention. At last, the performances of the employed classifiers are compared. The results show that the maximum information about the visual attention in area MT exists in the low frequency features. Interestingly, low frequency features over all the time-axis and all of the frequency features at the initial time interval in the spectrogram domain contain the most valuable information related to the decoding of spatial attention. In the CWT and scalogram domains, this information exists in the low frequency features at the initial time interval. Furthermore, high performances are obtained for these features in both the time and the frequency domains. Among different employed classifiers, the best achieved performance which is about 84.5 % belongs to the K-nearest neighbor classifier combined with the T-test method for feature selection in the time domain. Additionally, the best achieved result (82.9 %) is related to the spectrogram with the least number of selected features as large as 200 features using the T-test method and SVM classifier in the time-frequency domain.

Frequency-dependent spatiotemporal profiles of visual responses recorded with subdural ECoG electrodes in awake monkeys: Differences between high- and low-frequency activity

Electrocorticography (ECoG) constitutes a powerful and promising neural recording modality in humans and animals. ECoG signals are often decomposed into several frequency bands, among which the so-called high-gamma band (80-250Hz) has been proposed to reflect local cortical functions near the cortical surface below the ECoG electrodes. It is typically assumed that the lower the frequency bands, the lower the spatial resolution of the signals; thus, there is not much to gain by analyzing the event-related changes of the ECoG signals in the lower-frequency bands. However, differences across frequency bands have not been systematically investigated. To address this issue, we recorded ECoG activity from two awake monkeys performing a retinotopic mapping task. We characterized the spatiotemporal profiles of the visual responses in the time-frequency domain. We defined the preferred spatial position, receptive field (RF), and response latencies of band-limited power (BLP) (i.e., alpha [3.9-11.7Hz], beta [15.6-23.4Hz], low [30-80Hz] and high [80-250Hz] gamma) for each electrode and compared them across bands and time-domain visual evoked potentials (VEPs). At the population level, we found that the spatial preferences were comparable across bands and VEPs. The high-gamma power showed a smaller RF than the other bands and VEPs. The response latencies for the alpha band were always longer than the latencies for the other bands and fastest in VEPs. Comparing the response profiles in both space and time for each cortical region (V1, V4+, and TEO/TE) revealed regional idiosyncrasies. Although the latencies of visual responses in the beta, low-, and high-gamma bands were almost identical in V1 and V4+, beta and low-gamma BLP occurred about 17ms earlier than high-gamma power in TEO/TE. Furthermore, TEO/TE exhibited a unique pattern in the spatial response profile: the alpha and high-gamma responses tended to prefer the foveal regions, whereas the beta and low-gamma responses preferred the peripheral visual fields with larger RFs. This suggests that neurons in TEO/TE first receive less selective spatial information via beta and low-gamma BLP but later receive more fine-tuned spatial foveal information via high-gamma power. This result is consistent with a hypothesis previously proposed by Nakamura et al. (1993) that states that visual processing in TEO/TE starts with coarse-grained information, which primes subsequent fine-grained information. Collectively, our results demonstrate that ECoG can be a potent tool for investigating the nature of the neural computations in each cortical region that cannot be fully understood by measuring only the spiking activity, through the incorporation of the knowledge of the spatiotemporal characteristics across all frequency bands.

Imaging fast electrical activity in the brain with electrical impedance tomography

Imaging of neuronal depolarization in the brain is a major goal in neuroscience, but no technique currently exists that could image neural activity over milliseconds throughout the whole brain. Electrical impedance tomography (EIT) is an emerging medical imaging technique which can produce tomographic images of impedance changes with non-invasive surface electrodes. We report EIT imaging of impedance changes in rat somatosensory cerebral cortex with a resolution of 2ms and <200μm during evoked potentials using epicortical arrays with 30 electrodes. Images were validated with local field potential recordings and current source-sink density analysis. Our results demonstrate that EIT can image neural activity in a volume 7×5×2mm in somatosensory cerebral cortex with reduced invasiveness, greater resolution and imaging volume than other methods. Modeling indicates similar resolutions are feasible throughout the entire brain so this technique, uniquely, has the potential to image functional connectivity of cortical and subcortical structures.

Sound identification in human auditory cortex: Differential contribution of local field potentials and high gamma power as revealed by direct intracranial recordings

High gamma power has become the principal means of assessing auditory cortical activation in human intracranial studies, albeit at the expense of low frequency local field potentials (LFPs). It is unclear whether limiting analyses to high gamma impedes ability of clarifying auditory cortical organization. We compared the two measures obtained from posterolateral superior temporal gyrus (PLST) and evaluated their relative utility in sound categorization. Subjects were neurosurgical patients undergoing invasive monitoring for medically refractory epilepsy. Stimuli (consonant-vowel syllables varying in voicing and place of articulation and control tones) elicited robust evoked potentials and high gamma activity on PLST. LFPs had greater across-subject variability, yet yielded higher classification accuracy, relative to high gamma power. Classification was enhanced by including temporal detail of LFPs and combining LFP and high gamma. We conclude that future studies should consider utilizing both LFP and high gamma when investigating the functional organization of human auditory cortex.

Local field potentials are local events in the mouse auditory cortex

Local field potentials (LFPs) and spikes (SPKs) sampled at the thalamocortical recipient layers represent the inputs from the thalamus and outputs to other layers. Previous studies have shown that SPK‐constructed receptive fields (RF_SPK) of cortical neurons are much smaller than LFP‐constructed RFs (RF_LFP). The difference in cortical RF_LFP and RF_SPK is therefore a plausible indication of local networking. The presence of a boarder RF_LFP appears due to contamination, to some degree, from remote sites. Our studies of the mouse primary auditory cortex show that the best frequencies and minimum thresholds of RF_SPK and RF_LFP were similar. We also observed that the RF_LFP area was only slightly larger than the RF_SPK area, a very different finding from previous reports. The bandwidth of RF_LFP was slightly broader than that of RF_SPK at all levels. These data do not support the explanation that bioelectrical signals from distant sites impact on cortical LFP through volume conduction. That the cortical LFP represents a local event is further supported by comparisons of RF_SPK and RF_LFP after cortical inhibition by muscimol and cortical disinhibition by bicuculine. We conclude that the difference between RF_SPK (output of cortical neurons) and RF_LFP (input of cortical neurons) results from intracortical processing, including cortical lateral inhibition and excitation.

Differential contributions of the globus pallidus and ventral thalamus to stimulus-response learning in humans

The ability to learn associations between stimuli, responses and rewards is a prerequisite for survival. Models of reinforcement learning suggest that the striatum, a basal ganglia input nucleus, vitally contributes to these learning processes. Our recently presented computational model predicts, first, that not only the striatum, but also the globus pallidus contributes to the learning (i.e., exploration) of stimulus-response associations based on rewards. Secondly, it predicts that the stable execution (i.e., exploitation) of well-learned associations involves further learning in the thalamus. To test these predictions, we postoperatively recorded local field potentials (LFPs) from patients that had undergone surgery for deep brain stimulation to treat severe movement disorders. Macroelectrodes were placed either in the globus pallidus or in the ventral thalamus. During recordings, patients performed a reward-based stimulus-response learning task that comprised periods of exploration and exploitation. We analyzed correlations between patients' LFP amplitudes and model-based estimates of their reward expectations and reward prediction errors. In line with our first prediction, pallidal LFP amplitudes during the presentation of rewards and reward omissions correlated with patients' reward prediction errors, suggesting pallidal access to reward-based teaching signals. Unexpectedly, the same was true for the thalamus. In further support of this prediction, pallidal LFP amplitudes during stimulus presentation correlated with patients' reward expectations during phases of low reward certainty - suggesting pallidal participation in the learning of stimulus-response associations. In line with our second prediction, correlations between thalamic stimulus-related LFP amplitudes and patients' reward expectations were significant within phases of already high reward certainty, suggesting thalamic participation in exploitation.

Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (<100 ms) spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters.

Mapping human brain networks with cortico-cortical evoked potentials

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.

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.

Studying brain functions with mesoscopic measurements: Advances in electrocorticography for non-human primates

Our brain is organized in a modular structure. Information in different modalities is processed within distinct cortical areas. However, individual cortical areas cannot enable complex cognitive functions without interacting with other cortical areas. Electrocorticography (ECoG) has recently become an important tool for studying global network activity across cortical areas in animal models. With stable recordings of electrical field potentials from multiple cortical areas, ECoG provides an opportunity to systematically study large-scale cortical activity at a mesoscopic spatiotemporal resolution under various experimental conditions. Recent developments in thin, flexible ECoG electrodes permit recording field potentials from not only gyral but intrasulcal cortical surfaces. Our review here focuses on the recent advances of ECoG applications to non-human primates.

HCN channels enhance spike phase coherence and regulate the phase of spikes and LFPs in the theta-frequency range

What are the implications for the existence of subthreshold ion channels, their localization profiles, and plasticity on local field potentials (LFPs)? Here, we assessed the role of hyperpolarization-activated cyclic-nucleotide-gated (HCN) channels in altering hippocampal theta-frequency LFPs and the associated spike phase. We presented spatiotemporally randomized, balanced theta-modulated excitatory and inhibitory inputs to somatically aligned, morphologically realistic pyramidal neuron models spread across a cylindrical neuropil. We computed LFPs from seven electrode sites and found that the insertion of an experimentally constrained HCN-conductance gradient into these neurons introduced a location-dependent lead in the LFP phase without significantly altering its amplitude. Further, neurons fired action potentials at a specific theta phase of the LFP, and the insertion of HCN channels introduced large lags in this spike phase and a striking enhancement in neuronal spike-phase coherence. Importantly, graded changes in either HCN conductance or its half-maximal activation voltage resulted in graded changes in LFP and spike phases. Our conclusions on the impact of HCN channels on LFPs and spike phase were invariant to changes in neuropil size, to morphological heterogeneity, to excitatory or inhibitory synaptic scaling, and to shifts in the onset phase of inhibitory inputs. Finally, we selectively abolished the inductive lead in the impedance phase introduced by HCN channels without altering neuronal excitability and found that this inductive phase lead contributed significantly to changes in LFP and spike phase. Our results uncover specific roles for HCN channels and their plasticity in phase-coding schemas and in the formation and dynamic reconfiguration of neuronal cell assemblies.

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.

Informative features of local field potential signals in primary visual cortex during natural image stimulation

The local field potential (LFP) is of growing importance in neurophysiology as a metric of network activity and as a readout signal for use in brain-machine interfaces. However, there are uncertainties regarding the kind and visual field extent of information carried by LFP signals, as well as the specific features of the LFP signal conveying such information, especially under naturalistic conditions. To address these questions, we recorded LFP responses to natural images in V1 of awake and anesthetized macaques using Utah multielectrode arrays. First, we have shown that it is possible to identify presented natural images from the LFP responses they evoke using trained Gabor wavelet (GW) models. Because GW models were devised to explain the spiking responses of V1 cells, this finding suggests that local spiking activity and LFPs (thought to reflect primarily local synaptic activity) carry similar visual information. Second, models trained on scalar metrics, such as the evoked LFP response range, provide robust image identification, supporting the informative nature of even simple LFP features. Third, image identification is robust only for the first 300 ms following image presentation, and image information is not restricted to any of the spectral bands. This suggests that the short-latency broadband LFP response carries most information during natural scene viewing. Finally, best image identification was achieved by GW models incorporating information at the scale of ∼ 0.5° in size and trained using four different orientations. This suggests that during natural image viewing, LFPs carry stimulus-specific information at spatial scales corresponding to few orientation columns in macaque V1.

Importance of EEG in validating the chronic effects of drugs: suggestions from animal models of epilepsy treated with rapamycin

PURPOSE

The development of new drugs for the treatment of epilepsy is a major challenge for modern neurology and its first steps demand basic research. Preclinical studies on animal models of epilepsy are mainly based on the analysis of brain electrical activity to detect seizures, when they are not just limited to behavioral tests like the Racine scale.

METHODS

In the present review, we discuss the importance of using time-locked video and EEG recordings (Video-EEG) coupled with behavioral tests as tools to monitor and analyze the effects of anti-epileptic drugs in pre-clinical research. Particularly, we focus on the utility of a multimodal approach based on EEG/behavioral analysis to study the beneficial effects of chronic rapamycin treatment as a potential anti-epileptogenic therapy for a broad spectrum of epilepsy, including both genetic (as in tuberous sclerosis complex) and acquired diseases.

RESULTS

Changes and synchronization of neuronal activity of different areas have been correlated with specific behavior in both physiological and pathological conditions. In the epileptic brain, during a seizure there is an abnormal activation of many cells all at once, altering different networks.

CONCLUSION

A multimodal approach based on video, EEG analysis and behavioral tests would be the best option in preclinical studies of epilepsy.

Maladaptive neural synchrony in tinnitus: origin and restoration

Tinnitus is the conscious perception of sound heard in the absence of physical sound sources external or internal to the body, reflected in aberrant neural synchrony of spontaneous or resting-state brain activity. Neural synchrony is generated by the nearly simultaneous firing of individual neurons, of the synchronization of membrane-potential changes in local neural groups as reflected in the local field potentials, resulting in the presence of oscillatory brain waves in the EEG. Noise-induced hearing loss, often resulting in tinnitus, causes a reorganization of the tonotopic map in auditory cortex and increased spontaneous firing rates and neural synchrony. Spontaneous brain rhythms rely on neural synchrony. Abnormal neural synchrony in tinnitus appears to be confined to specific frequency bands of brain rhythms. Increases in delta-band activity are generated by deafferented/deprived neuronal networks resulting from hearing loss. Coordinated reset (CR) stimulation was developed in order to specifically counteract such abnormal neuronal synchrony by desynchronization. The goal of acoustic CR neuromodulation is to desynchronize tinnitus-related abnormal delta-band oscillations. CR neuromodulation does not require permanent stimulus delivery in order to achieve long-lasting desynchronization or even a full-blown anti-kindling but may have cumulative effects, i.e., the effect of different CR epochs separated by pauses may accumulate. Unlike other approaches, acoustic CR neuromodulation does not intend to reduce tinnitus-related neuronal activity by employing lateral inhibition. The potential efficacy of acoustic CR modulation was shown in a clinical proof of concept trial, where effects achieved in 12 weeks of treatment delivered 4–6 h/day persisted through a preplanned 4-week therapy pause and showed sustained long-term effects after 10 months of therapy, leading to 75% responders.

Directional Recording of Subthalamic Spectral Power Densities in Parkinson's Disease and the Effect of Steering Deep Brain Stimulation

BACKGROUND

A new 32-contacts deep brain stimulation (DBS) lead, capable of directionally steering stimulation, was tested intraoperatively.

OBJECTIVE

The aim of this pilot study was to perform recordings from the multidirectional contacts and to investigate the effect of directional current steering on the local field potentials (LFPs).

METHODS

In eight patients with Parkinson's disease, after standard microelectrode recording and clinical testing, the new lead was temporarily implanted. The 32-channel LFP recordings were measured simultaneously at different depths and directions before and after directional stimulation.

RESULTS

The spatial distribution of LFPs power spectral densities across the contact array at baseline marked the borders of the subthalamic nucleus (STN) with a significant increase in beta power and with a mean accuracy of approximately 0.6 mm in four patients.The power in the 18.5-30 Hz frequency band varied across different directions in all patients. In the three cases that showed improvement of rigidity, this was higher when current was steered toward the direction with the highest LFP power in the beta band. Subthalamic LFPs in six patients showed a differential frequency-dependent suppression/enhancement of the oscillatory activity in the 10-45 Hz frequency band after four different 'steering' modes as compared to ring mode, suggesting a higher specificity.

CONCLUSIONS

Through a new 32-contact DBS lead it is possible to record simultaneous subthalamic LFPs at different depths and directions, providing confirmation of adequate lead placement and multidirectional spatial-temporal information potentially related to pathological subthalamic electrical activity and to the effect of stimulation. Although further research is needed, this may improve the efficiency of steering stimulation.

Alterations in neuronal activity in basal ganglia-thalamocortical circuits in the parkinsonian state

In patients with Parkinson’s disease and in animal models of this disorder, neurons in the basal ganglia and related regions in thalamus and cortex show changes that can be recorded by using electrophysiologic single-cell recording techniques, including altered firing rates and patterns, pathologic oscillatory activity and increased inter-neuronal synchronization. In addition, changes in synaptic potentials or in the joint spiking activities of populations of neurons can be monitored as alterations in local field potentials (LFPs), electroencephalograms (EEGs) or electrocorticograms (ECoGs). Most of the mentioned electrophysiologic changes are probably related to the degeneration of diencephalic dopaminergic neurons, leading to dopamine loss in the striatum and other basal ganglia nuclei, although degeneration of non-dopaminergic cell groups may also have a role. The altered electrical activity of the basal ganglia and associated nuclei may contribute to some of the motor signs of the disease. We here review the current knowledge of the electrophysiologic changes at the single cell level, the level of local populations of neural elements, and the level of the entire basal ganglia-thalamocortical network in parkinsonism, and discuss the possible use of this information to optimize treatment approaches to Parkinson’s disease, such as deep brain stimulation (DBS) therapy.