Local Field Potential Measurements Research Articles

Assessing brain state and anesthesia level with two-photon calcium signals

Brain states, such as wake, sleep, or different depths of anesthesia are usually assessed using electrophysiological techniques, such as the local field potential (LFP) or the electroencephalogram (EEG), which are ideal signals for detecting activity patterns such as asynchronous or oscillatory activities. However, it is technically challenging to have these types of measures during calcium imaging recordings such as two-photon or wide-field techniques. Here, using simultaneous two-photon and LFP measurements, we demonstrate that despite the slower dynamics of the calcium signal, there is a high correlation between the LFP and two-photon signals taken from the neuropil outside neuronal somata. Moreover, we find the calcium signal to be systematically delayed from the LFP signal, and we use a model to show that the delay between the two signals is due to the physical distance between the recording sites. These results suggest that calcium signals alone can be used to detect activity patterns such as slow oscillations and ultimately assess the brain state and level of anesthesia.

Phase-Dependent Deep Brain Stimulation: A Review.

Neural oscillations are repetitive patterns of neural activity in the central nervous systems. Oscillations of the neurons in different frequency bands are evident in electroencephalograms and local field potential measurements. These oscillations are understood to be one of the key mechanisms for carrying out normal functioning of the brain. Abnormality in any of these frequency bands of oscillations can lead to impairments in different cognitive and memory functions leading to different pathological conditions of the nervous system. However, the exact role of these neural oscillations in establishing various brain functions is still under investigation. Closed loop deep brain stimulation paradigms with neural oscillations as biomarkers could be used as a mechanism to understand the function of these oscillations. For making use of the neural oscillations as biomarkers to manipulate the frequency band of the oscillation, phase of the oscillation, and stimulation signal are of importance. This paper reviews recent trends in deep brain stimulation systems and their non-invasive counterparts, in the use of phase specific stimulation to manipulate individual neural oscillations. In particular, the paper reviews the methods adopted in different brain stimulation systems and devices for stimulating at a definite phase to further optimize closed loop brain stimulation strategies.

Isoflurane affects brain functional connectivity in rats 1 month after exposure

Isoflurane, the most commonly used preclinical anesthetic, induces brain plasticity and long-term cellular and molecular changes leading to behavioral and/or cognitive consequences. These changes are most likely associated with network-level changes in brain function. To elucidate the mechanisms underlying long-term effects of isoflurane, we investigated the influence of a single isoflurane exposure on functional connectivity, brain electrical activity, and gene expression.Male Wistar rats (n = 22) were exposed to 1.8% isoflurane for 3 h. Control rats (n = 22) spent 3 h in the same room without exposure to anesthesia. After 1 month, functional connectivity was evaluated with resting-state functional magnetic resonance imaging (fMRI; n = 6 + 6) and local field potential measurements (n = 6 + 6) in anesthetized animals. A whole genome expression analysis (n = 10+10) was also conducted with mRNA-sequencing from cortical and hippocampal tissue samples.Isoflurane treatment strengthened thalamo-cortical and hippocampal-cortical functional connectivity. Cortical low-frequency fMRI power was also significantly increased in response to the isoflurane treatment. The local field potential results indicating strengthened hippocampal-cortical alpha and beta coherence were in good agreement with the fMRI findings. Furthermore, altered expression was found in 20 cortical genes, several of which are involved in neuronal signal transmission, but no gene expression changes were noted in the hippocampus.Isoflurane induced prolonged changes in thalamo-cortical and hippocampal-cortical function and expression of genes contributing to signal transmission in the cortex. Further studies are required to investigate whether these changes are associated with the postoperative behavioral and cognitive symptoms commonly observed in patients and animals.

Biophysical characterization of local field potential recordings from directional deep brain stimulation electrodes

ObjectiveTwo major advances in clinical deep brain stimulation (DBS) technology have been the introduction of local field potential (LFP) recording capabilities, and the deployment of directional DBS electrodes. However, these two technologies are not operationally integrated within current clinical DBS devices. Therefore, we evaluated the theoretical advantages of using directional DBS electrodes for LFP recordings, with a focus on measuring beta-band activity in the subthalamic nucleus (STN). MethodsWe used a computational model of human STN neural activity to simulate LFP recordings. The model consisted of 235,280 anatomically and electrically detailed STN neurons surrounding the DBS electrode, which was previously optimized to mimic beta-band synchrony in the dorsolateral STN. We then used that model system to compare LFP recordings from cylindrical and directional DBS contacts, and evaluate how the selection of different contacts for bipolar recording affected the LFP measurements. ResultsThe model predicted two advantages of directional DBS electrodes over cylindrical DBS electrodes for STN LFP recording. First, recording from directional contacts could provide additional insight on the location of a synchronous volume of neurons within the STN. Second, directional contacts could detect a smaller volume of synchronous neurons than cylindrical contacts, which our simulations predicted to be a ~0.5 mm minimum radius. ConclusionsSTN LFP recordings from 8-contact directional DBS electrodes (28 possible bipolar pairs) can provide more information than 4-contact cylindrical DBS electrodes (6 possible bipolar pairs), but they also introduce additional complexity in analyzing the signals. SignificanceIntegration of directional electrodes with DBS systems that are capable of LFP recordings could improve localization of targeted volumes of synchronous neurons in PD patients.

Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions

The local field potential (LFP) is the low frequency part of the extracellular electrical potential in the brain and reflects synaptic activity onto thousands of neurons around each recording contact. Nowadays, LFPs can be measured at several hundred locations simultaneously. The measured LFP is in general a superposition of contributions from many underlying neural populations which makes interpretation of LFP measurements in terms of the underlying neural activity challenging. Classical statistical analyses of LFPs rely on matrix decomposition-based methods such as PCA (Principal Component Analysis) and ICA (Independent Component Analysis), which require additional constraints on spatial and/or temporal patterns of populations. In this work, we instead explore the multi-fold data structure of LFP recordings, e.g., multiple trials, multi-channel time series, arrange the signals as a higher-order tensor (i.e., multiway array), and study how a specific tensor decomposition approach, namely canonical polyadic (CP) decomposition, can be used to reveal the underlying neural populations. Essential for interpretation, the CP model provides uniqueness without imposing constraints on patterns of underlying populations. Here, we first define a neural network model and based on its dynamics, compute LFPs. We run multiple trials with this network, and LFPs are then analysed simultaneously using the CP model. More specifically, we design \Note{feed-forward population rate neuron models} to match the structure of state-of-the-art, large-scale LFP simulations, but downscale them to allow easy inspection and interpretation. We demonstrate that our feed-forward model matches the mathematical structure assumed in the CP model, and CP successfully reveals temporal and spatial patterns as well as variations over trials of underlying populations when compared with the ground truth from the model. We also discuss the use of diagnostic approaches for CP to guide the analysis when there is no ground truth information. In comparison with classical methods, we discuss the advantages of using tensor decompositions for analyzing LFP recordings as well as their limitations.

On the Relationship between MRI and Local Field Potential Measurements of Spatial and Temporal Variations in Functional Connectivity

Correlations between fluctuations in resting state BOLD fMRI signals are interpreted as measures of functional connectivity (FC), but the neural basis of their origins and their relationships to specific features of underlying electrophysiologic activity, have not been fully established. In particular, the dependence of FC metrics on different frequency bands of local field potentials (LFPs), and the relationship of dynamic changes in BOLD FC to underlying temporal variations of LFP correlations, are not known. We compared the spatial profiles of resting state coherences of different frequency bands of LFP signals, with high resolution resting state BOLD FC measurements. We also compared the probability distributions of temporal variations of connectivity in both modalities using a Markov chain model-based approach. We analyzed data obtained from the primary somatosensory (S1) cortex of monkeys. We found that in areas 3b and 1 of S1 cortex, low frequency LFP signal fluctuations were the main contributions to resting state LFP coherence. Additionally, the dynamic changes of BOLD FC behaved most similarly to the LFP low frequency signal coherence. These results indicate that, within the S1 cortex meso-scale circuit studied, resting state FC measures from BOLD fMRI mainly reflect contributions from low frequency LFP signals and their dynamic changes.

Microelectrode array electrical impedance tomography for fast functional imaging in the thalamus

Electrical Impedance Tomography (EIT) has the potential to be able to observe functional tomographic images of neural activity in the brain at millisecond time-scales. Prior modelling and experimental work has shown that EIT is capable of imaging impedance changes from neural depolarisation in rat somatosensory cortex. Here, we investigate the feasibility of EIT for imaging impedance changes using a stereotaxically implanted microelectrode array in the thalamus. Microelectrode array EIT was simulated using an anatomically accurate marmoset brain model. Impedance imaging was validated and detectability estimated using physiological noise recorded from the marmoset visual thalamus. The results suggest that visual-input-driven impedance changes in visual subcortical bodies within 300 μm of the implanted array could be reliably reconstructed and localised, comparable to local field potential measurements. Furthermore, we demonstrated that microelectrode array EIT could reconstruct concurrent activity in multiple subcortical bodies simultaneously.

Spectral distribution of local field potential responses to electrical stimulation of the retina

Objective. Different frequency bands of the local field potential (LFP) have been shown to reflect neuronal activity occurring at varying cortical scales. As such, recordings of the LFP may offer a novel way to test the efficacy of neural prostheses and allow improvement of stimulation strategies via neural feedback. Here we use LFP measurements from visual cortex to characterize neural responses to electrical stimulation of the retina. We aim to show that the LFP is a viable signal that contains sufficient information to optimize the performance of sensory neural prostheses. Approach. Clinically relevant electrode arrays were implanted in the suprachoroidal space of one eye in four felines. LFPs were simultaneously recorded in response to stimulation of individual electrodes using penetrating microelectrode arrays from the visual cortex. The frequency response of each electrode was extracted using multi-taper spectral analysis and the uniqueness of the responses was determined via a linear decoder. Main results. We found that cortical LFPs are reliably modulated by electrical stimulation of the retina and that the responses are spatially localized. We further characterized the spectral distribution of responses, with maximum information being contained in the low and high gamma bands. Finally, we found that LFP responses are unique to a large range of stimulus parameters (∼40) with a maximum conveyable information rate of 6.1 bits. Significance. These results show that the LFP can be used to validate responses to electrical stimulation of the retina and we provide the first steps towards using these responses to provide more efficacious stimulation strategies.

Identifying and exploiting the anatomical origin of population rate oscillations in multi-layered spiking networks

Fast oscillations of the population firing rate in the high gamma range (50-200 Hz), where individual neurons fire slowly and irregularly, are observed in the living brain and in network models of leaky integrate-and-fire (LIF) neurons, that have also been studied analytically [1]. However, a systematic approach identifying sub-circuits responsible for specific oscillations in a structured network of neural populations is currently unavailable. We consider a large-scale, neural network consisting of 4 layers each composed of an excitatory and inhibitory population of LIF-neurons with connectivity determined by electrophysiological and anatomical studies [2]. In simulations we observe a peak in the power spectrum around 83 Hz in all populations and low frequency oscillations with smaller power in a subset of the populations. Mapping the dynamics of the fluctuations to an effective linear rate model, using the recently derived transfer function for LIF-neurons with synaptic filtering [3], we derive the spectra of the population firing rates analytically. Decomposing the noise-driven fluctuations into eigenmodes of the effective connectivity, we identify the modes responsible for peaks in the spectra. Applying perturbation theory, we quantify the influence of individual anatomical connections on the spectrum at given frequencies and identify a sub-circuitry, localized in the supra-granular and granular layer, generating the oscillation. These findings are in agreement with layer-specific local field potential measurements in the Macaque primary visual cortex, where gamma-frequency oscillations were mostly pronounced in layer 2,3 and 4B [4]. We exploit this method i) to identify the connectivity loops responsible for the observed peaks and ii) to alter the circuitry in a targeted manner to control the position and amplitude of the peaks and the generation of slow frequency fluctuations. This requires removal and addition of only small numbers of synapses. The analytical framework moreover explains the suppression of higher frequencies by distributed delays and the amplification of population specific oscillatory input. Mapping the stimulus vector onto the eigenmodes of the system shows how the components of the input vector are processed in the network. Thus one can derive the sensitivity of the population rate dynamics to the direction and frequency of stimuli. Our method finds application in the identification of the connectivity loops that determine emergent and externally driven global measures of activity observable in experiments as well as in engineering circuits that exhibit desired correlations on the population level.

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

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.

Stimulus-related neuroimaging in task-engaged subjects is best predicted by concurrent spiking.

The implicit goal of functional magnetic resonance imaging is to infer local neural activity. There is considerable debate, however, as to whether imaging correlates most linearly with local spiking or some local field potential (LFP) measurement. Through simultaneous neuroimaging (intrinsic-signal optical imaging) and electrode recordings from alert, task-engaged macaque monkeys, we showed previously that local electrophysiology correlates with only a specific stimulus-related imaging component. Here we show that this stimulus-related component--obtained by subtracting a substantial task-related component--is particularly linear with local spiking over a comprehensive range of response strengths. Matches to concurrent LFP measurements are, to varying degrees, poorer. As a control, we also tried matching the full imaging signal to local electrophysiology without subtracting task-related components. These control matches were consistently worse; they were, however, slightly better for gamma LFP than spiking, potentially resolving discrepancies between our findings and earlier reports favoring LFP.

Modelling local field potential features during network gamma oscillations

While the physics of local field potential (LFP) generation are well-established, the complexity of neural network dynamics means that interpreting LFP measurements in terms of the underlying neural activity is very difficult [1]. Recent studies have therefore investigated forward models of the LFP: calculating the LFP due to known arrangements of neuronal current sources [1]. We use this approach to study the spatio-temporal features of the LFP recorded during persistent gamma-oscillations in vitro. The mechanism by which this activity regime is generated is well-known [2], and many multi-electrode array recordings are available that allow us to compare the spatio-temporal properties of the experimentally measured and simulated LFPs. For simplicity, we implemented a model of a neocortical slice containing only layer 2/3, using our VERTEX simulation tool. The model contained a gamma oscillation-generating region surrounded by regions that did not participate in the gamma oscillation (i.e. the neurons in these regions fired randomly). These enforced conditions allowed us to study the spatial profile of the LFP from differently sized gamma oscillation-generating patches, while minimising the number of variables we changed between simulations. Additionally, we could make observations about properties of the LFP during random activity in the network. We describe three key results from our simulations. Firstly, we found that perisomatic synaptic currents on pyramidal neurons resulting from basket interneuron firing dominate the LFP during gamma oscillations, in agreement with recent experimental results [3,4]. We predict that basket interneurons will also contribute the majority of the LFP signal during random, uncorrelated activity because of the location of their synapses at pyramidal neuron somas. This contribution is amplified during oscillatory activity because of increased basket cell synchrony relative to other neuron types. Secondly, we investigated how gamma-frequency power from a localised gammagenerating region in the model spread due to volume conduction. We found that the spatial spread of the oscillation in the LFP increased above and below the neurons’ somadepth, and depended on the level of the random surround activity. Finally, we investigated frequency scaling in the LFP power spectrum across space. We showed that LFPs at the level of the pyramidal cells’ apical dendrites exhibit a low-pass filtering effect (as predicted previously [5]), which is absent from LFPs recorded at the soma level because of the relative dominance and localisation of the basket cell input. We confirmed that this matched the frequency scaling in our comparison experimental data recorded in macaque neocortical slices, and, based on the frequency characteristics in our model, propose that the experimentally recorded LFP could contain contributions from spiking activity at frequencies down to, and perhaps below, 100 Hz.

Microscale impedance measurements suggest that ionic diffusion is implicated in generating extracellular potentials

The genesis of the Local Field Potential (LFP) highly depends on the electric properties of the extracellular medium, but such properties are still subject to a controversy because of contradictory measurements. One possibility is that the use of metal electrodes as current sources in previous studies provides non-physiological results. We tested this possibility by performing impedance measurements in conditions as close as possible to physiological conditions. We generated single-cell LFPs by injecting subthreshold inputs in single neurons using patch-clamp recordings, combined with extracellular recordings with micropipettes. Various measurement configurations show that (1) the extracellular medium has strong low-pass filtering properties and cannot be accounted by a resistive medium; (2) the frequency scaling of the filtering, as well as its phase, show that the system seems intermediate between resistive and capacitive. The extracellular impedance was also measured from in vivo experiments in rats under anesthesia. In this case, recording with intracellular (whole-cell) electrodes, together with extracellular LFP, showed results consistent with the in vitro experiments. Finally, we developed a theoretical model based on Maxwell equations, which shows that all measurements can be explained if the extracellular medium is of diffusive type (Warburg impedance). This model predicts that the phase difference between intracellular and extracellular signals should provide a signature of the physical nature of the impedance, with 45 degrees phase difference for purely diffusive type. The experiments show that indeed, the phase is that of a RC soma in series with a diffusive impedance (between 0 and -45 degrees), therefore confirming the diffusive nature of the extracellular impedance. These findings have potentially important consequences for interpreting LFP measurements and source estimation such as CSD analysis.

Spatially Distributed Local Fields in the Hippocampus Encode Rat Position

Although neuronal spikes can be readily detected from extracellular recordings, synaptic and subthreshold activity remains undifferentiated within the local field potential (LFP). In the hippocampus, neurons discharge selectively when the rat is at certain locations, while LFPs at single anatomical sites exhibit no such place-tuning. Nonetheless, because the representation of position is sparse and distributed, we hypothesized that spatial information can be recovered from multiple-site LFP recordings. Using high-density sampling of LFP and computational methods, we show that the spatiotemporal structure of the theta rhythm can encode position as robustly as neuronal spiking populations. Because our approach exploits the rhythmicity and sparse structure of neural activity, features found in many brain regions, it is useful as a general tool for discovering distributed LFP codes.

Effective Connectivity Maps in the Swine Somatosensory Cortex Estimated from Electrocorticography and Validated with Intracortical Local Field Potential Measurements

Macroscopic techniques are increasingly being used to estimate functional connectivity in the brain, which provides valuable information about brain networks. In any such endeavors it is important to understand capabilities and limitations of each technique through direct validation, which is often lacking. This study evaluated a multiple dipole source analysis technique based on electrocorticography (ECOG) data in estimating effective connectivity maps and validated the technique with intracortical local field potential (LFP) recordings. The study was carried out in an animal model (swine) with a large brain to avoid complications caused by spreading of the volume current. The evaluation was carried out for the cortical projections from the trigeminal nerve and corticocortical connectivity from the first rostrum area (R1) in the primary somatosensory cortex. Stimulation of the snout and layer IV of the R1 did not activate all projection areas in each animal, although whenever an area was activated in a given animal, its location was consistent with the intracortical LFP. The two types of connectivity maps based on ECOG analysis were consistent with each other and also with those estimated from the intracortical LFP, although there were small discrepancies. The discrepancies in mean latency based on ECOG and LFP were all very small and nonsignificant: snout stimulation, -1.1-2.0 msec (contralateral hemisphere) and 3.9-8.5 msec (ipsilateral hemisphere); R1 stimulation, -1.4-2.2 msec for the ipsilateral and 0.6-1.4 msec for the contralateral hemisphere. Dipole source analysis based on ECOG appears to be quite useful for estimating effective connectivity maps in the brain.

Monitoring functional impairment and recovery after traumatic brain injury in rats by FMRI.

The present study was designed to test a hypothesis that functional magnetic resonance imaging (fMRI) can be used to monitor functional impairment and recovery after moderate experimental traumatic brain injury (TBI). Moderate TBI was induced by lateral fluid percussion injury in adult rats. The severity of brain damage and functional recovery in the primary somatosensory cortex (S1) was monitored for up to 56 days using fMRI, cerebral blood flow (CBF) by arterial spin labeling, local field potential measurements (LFP), behavioral assessment, and histology. All the rats had reduced blood-oxygen-level-dependent (BOLD) responses during the 1st week after trauma in the ipsilateral S1. Forty percent of these animals showed recovery of the BOLD response during the 56 day follow-up. Unexpectedly, no association was found between the recovery in BOLD response and the volume of the cortical lesion or thalamic neurodegeneration. Instead, the functional recovery occurred in rats with preserved myelinated fibers in layer VI of S1. This is, to our knowledge, the first study demonstrating that fMRI can be used to monitor post-TBI functional impairment and consequent spontaneous recovery. Moreover, the BOLD response was associated with the density of myelinated fibers in the S1, rather than with neurodegeneration. The present findings encourage exploration of the usefulness of fMRI as a noninvasive prognostic biomarker for human post-TBI outcomes and therapy responses.

Simultaneous BOLD fMRI and local field potential measurements during kainic acid–induced seizures

To investigate how kainic acid-induced epileptiform activity is related to hemodynamic changes probed by blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI). Epileptiform activity was induced with kainic acid (KA) (10 mg/kg, i.p.), and simultaneous fMRI at 7 Tesla, and deep electrode local field potential (LFP) recordings were performed from the right hippocampus in awake and medetomidine-sedated adult Wistar rats. Recurrent seizure activity induced by KA was detected in LFP both in medetomidine-sedated and awake rats, even though medetomidine sedation reduced the mean duration of individual seizures as compared to awake rats (33 ± 24 and 46 ± 34 s, respectively, mean ± SD p < 0.01). KA administration also triggered robust positive BOLD responses bilaterally in the hippocampus both in awake and medetomidine-sedated rats; however, in both animal groups some of the seizures detected in LFP recording did not cause detectable BOLD signal change. Our data suggest that medetomidine sedation can be used for simultaneous fMRI and electrophysiologic studies of normal and epileptic brain function, even though seizure duration after medetomidine administration was shorter than that in awake animals. The results also indicate that neuronal activity and BOLD response can become decoupled during recurrent kainic acid-induced seizures, which may have implications to interpretation of fMRI data obtained during prolonged epileptiform activity.

Simultaneous fMRI and local field potential measurements during epileptic seizures in medetomidine-sedated rats using raser pulse sequence.

Simultaneous electrophysiological and functional magnetic resonance imaging measurements of animal models of epilepsy are methodologically challenging, but essential to better understand abnormal brain activity and hemodynamics during seizures. In this study, functional magnetic resonance imaging of medetomidine-sedated rats was performed using novel rapid acquisition by sequential excitation and refocusing (RASER) fast imaging pulse sequence and simultaneous local field potential measurements during kainic acid-induced seizures. The image distortion caused by the hippocampal-measuring electrode was clearly seen in echo planar imaging images, whereas no artifact was seen in RASER images. Robust blood oxygenation level-dependent responses were observed in the hippocampus during kainic acid-induced seizures. The recurrent epileptic seizures were detected in the local field potential signal after kainic acid injection. The presented combination of deep electrode local field potential measurements and functional magnetic resonance imaging under medetomidine anesthesia, which does not significantly suppress kainic acid-induced seizures, provides a unique tool for studying abnormal brain activity in rats.

On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings.

To investigate the directionality of neural interactions as assessed by electrophysiology, we adapted methods of structural analysis from the field of econometrics. In particular, within the framework of autoregressive modelling of the data, we considered quantitative measures of linear relationship between multiple time series adopting the Wiener-Granger concept of causality. The techniques were evaluated with local field potential measurements from the cat visual system. Here, several issues had to be addressed. First, out of several statistical tests of the stationarity of local field potentials considered, those based on the Kolmogorov-Smirnov and on the reverse arrangement statistics proved to be most powerful. The application of those tests to the experimental data showed that the large part of the local field potentials can be considered stationary on a time scale of 1 s. Second, out of the several investigated methods for the determination of an optimal order of the autoregressive model, the Akaike Information Criterion had the most suitable properties. The identified order of the model, across different repetitions of the trials, was consistently 5-8. Third, although the individual segments of field potentials used for the analysis were relatively short, the methods of structural analysis applied produced reliable results, confirming findings of simulations of data with similar properties. Furthermore the features of the estimated models were consistent among trials, so that the analysis of average measures of interaction appears to be a viable approach to investigate the relationship between the recording sites. In summary, the statistical methods considered have proved to be suitable for the study of the directionality of neuronal interactions.