Multichannel Local Field Potentials Research Articles

Functional connectivity in a rat model of Alzheimer's disease during a working memory task.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive loss of memory. Impairment of working memory was typically observed in AD. The concept of brain functional connectivity plays an important role in neuroscience as a useful tool to understand the organized behavior of brain. Hence, the purpose of this study is to investigate the possible mechanism of working memory deficits in AD from a new perspective of functional connectivity. Rats were randomly divided into 2 groups: Aβ injection group (Aβ₁₋₄₂-induced toxicity rat model) and control group. Multi-channel local field potentials (LFPs) were obtained from rat prefrontal cortex with implanted microelectrode arrays while the rats performed a Y-maze working memory task. The short-time Fourier transform was utilized to analyze the power changes in LFPs and sub-bands (in particular theta and low gamma bands) were extracted via band filtering. Then the Directed transfer function (DTF) method was applied to calculate the functional connections among LFPs. From the DTF calculation, the causal networks in the sub-bands were identified. DTFmean (mean of connectivity matrix elements) was used to quantify connection strength as well as global efficiency (Eglob) was calculated to quantitatively describe the efficient of information transfer in the network. Our results showed that both connection strength and efficient of information transfer increased during the working memory task in the control group; by contrast, there was no significantly change in the Aβ injection group. These findings could lead to improve the understanding of the mechanism of working memory deficits in AD.

Real-time estimation and biofeedback of single-neuron firing rates using local field potentials.

The long-term stability and low-frequency composition of local field potentials (LFPs) offer important advantages for robust and efficient neuroprostheses. However, cortical LFPs recorded by multi-electrode arrays are often assumed to contain only redundant information arising from the activity of large neuronal populations. Here we show that multichannel LFPs in monkey motor cortex each contain a slightly different mixture of distinctive slow potentials that accompany neuronal firing. As a result, the firing rates of individual neurons can be estimated with surprising accuracy. We implemented this method in a real-time biofeedback brain–machine interface, and found that monkeys could learn to modulate the activity of arbitrary neurons using feedback derived solely from LFPs. These findings provide a principled method for monitoring individual neurons without long-term recording of action potentials.

The utility of multichannel local field potentials for brain–machine interfaces

Objective. Local field potentials (LFPs) that carry information about the subject's motor intention have the potential to serve as a complement or alternative to spike signals for brain–machine interfaces (BMIs). The goal of this study is to assess the utility of LFPs for BMIs by characterizing the largely unknown information coding properties of multichannel LFPs. Approach. Two monkeys were implanted, each with a 16-channel electrode array, in the parietal reach region where both LFPs and spikes are known to encode the subject's intended reach target. We examined how multichannel LFPs recorded during a reach task jointly carry reach target information, and compared the LFP performance to simultaneously recorded multichannel spikes. Main Results. LFPs yielded a higher number of channels that were informative about reach targets than spikes. Single channel LFPs provided more accurate target information than single channel spikes. However, LFPs showed significantly larger signal and noise correlations across channels than spikes. Reach target decoders performed worse when using multichannel LFPs than multichannel spikes. The underperformance of multichannel LFPs was mostly due to their larger noise correlation because noise de-correlated multichannel LFPs produced a decoding accuracy comparable to multichannel spikes. Despite the high noise correlation, decoders using LFPs in addition to spikes outperformed decoders using only spikes. Significance. These results demonstrate that multichannel LFPs could effectively complement spikes for BMI applications by yielding more informative channels. The utility of multichannel LFPs may be further augmented if their high noise correlation can be taken into account by decoders.

Encoding of rat working memory by power of multi-channel local field potentials via sparse non-negative matrix factorization

Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multi-channel local field potentials (LFPs) based on sparse nonnegative matrix factorization (SNMF). SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four Sprague-Dawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the power-increased LFP components were selected as working memory-related features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time-frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.

Component analysis reveals sharp tuning of the local field potential in the guinea pig auditory cortex

Local field potentials (LFPs) recorded in the auditory cortex of mammals are known to reveal weakly selective and often multimodal spectrotemporal receptive fields in contrast to spiking activity. This may in part reflect the wider "listening sphere" of LFPs relative to spikes due to the greater current spread at low than high frequencies. We recorded LFPs and spikes from auditory cortex of guinea pigs using 16-channel electrode arrays. LFPs were processed by a component analysis technique that produces optimally tuned linear combinations of electrode signals. Linear combinations of LFPs were found to have sharply tuned responses, closer to spike-related tuning. The existence of a sharply tuned component implies that a cortical neuron (or group of neurons) capable of forming a linear combination of its inputs has access to that information. Linear combinations of signals from electrode arrays reveal information latent in the subspace spanned by multichannel LFP recordings and are justified by the fact that the observations themselves are linear combinations of neural sources.

Working Memory Event Encoding by Theta and Gamma Oscillation of Multi-Channel Local Field Potentials in Rat Based on Wigner-Ville Distribution

Working Memory Event Encoding by Theta and Gamma Oscillation of Multi-Channel Local Field Potentials in Rat Based on Wigner-Ville Distribution

Explicit-duration hidden Markov model inference of UP-DOWN states from continuous signals.

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics.

Study on coding working memory event via independent components energies of multichannel local field potentials

Objective To investigate how the independent components(ICs)energies of multichannel local field potentials(LFPs) code event base on the analysis of ICA of the cortical LFPs of rats. Methods Taking the event point as the zero point, 15-channel LFPs between the span of ±500ms recorded from the prefrontal cortex of rats were decomposed into 15 ICs. The energies of the ICs were computed in a 50-ms window. By sliding the window with step of 25 ms, a dynamic distribution mapping of the 15 ICs' energies was established. ICs with distinctly increased energies during the span of ±200 ms, which indicating that these ICs energies coded event,were selected as the targets. The corresponding channels of these ICs were determined consequently via the inverse transformation of ICA. Results Considering each trail of the repetitious analysis for the same segment of data, the spatial localization of the dominate function region(s) turned out to be relatively stable in spite of the uncertainty of the number and sequence of the target IC(s) due to the ambiguities of the decomposition of ICA.Meanwhile, the analysis results of a series of data segments showed satisfactory correspondence between data segments and dominate function regions. Conclusion The ICs' energies of multichannel LFPs are able to code events in working memories; It is valid for ICA to identify the coding patterns of multichannel LFPs to events; ICA is capable to localize the dominate function regions of event coding with satisfactory robustness. Key words: Local field potentials; Independent components; Energy; Coding; Working memory event

Synchronous pattern of multi-channel local field potentials in rats anesthesized by propofol

Objective To study the phase coherence mode of multi-channel local field potentials (LFPs) of rats cortex under waking, propofol anesthetized and recovery of righting reflex situations and to apply it to estimate the anesthetic degree. Methods Five male Sprague-Dawley rats were selected and 16-channel micro-electrode matrix were implanted in the prefrontal cortex of the rats. Ten days later, 16-channel electric signals were recorded from waking rats using Cerebus 128 Data Acquisition System. Then the rats were anesthetized with propofol intravenously through the tail vein, with the induction dose of propofol of 10 mg/kg and anesthesia maintenance dose of put and the rat regained consciousness, 16-channel electric signals from anaesthetic rats were recorded. After preprocessing, phase coherence of LFPs was calculated among the data from these three situations. Phase coherences were calculated in each window between each signal channel LFP and reference channel LFP with 200-ms multitaper window sliding from the beginning. Time-varying phase coherence dynamic distribution was available between each channel LFP and reference channel LFP with 25-ms overlapping. Results The results shows that the phase coherence average number in waking situation is less than the number in anesthesia (P<0.05); the phase coherence average number in recovery situation is less than that of anesthesia (P<0.05), it is larger than the number in anesthetic situation on the contrary (P<0.05). Conclusion The result indicates that the no synchronization of the LFPs during the waking-up time was found. High synchronization of the LFPs during the anesthetic period was found, while the degree of synchronization of the LFPs during the recovery time was between that of waking-up time period and anesthetic period. It can be applied to identify anesthetic degree of propofol. Key words: Prefrontal cortex of rats; Anesthetic degree of propofol; Local field potentials; Synchronization

Granger causality relationships between local field potentials in an animal model of temporal lobe epilepsy

An understanding of the in vivo spatial emergence of abnormal brain activity during spontaneous seizure onset is critical to future early seizure detection and closed-loop seizure prevention therapies. In this study, we use Granger causality (GC) to determine the strength and direction of relationships between local field potentials (LFPs) recorded from bilateral microelectrode arrays in an intermittent spontaneous seizure model of chronic temporal lobe epilepsy before, during, and after Racine grade partial onset generalized seizures. Our results indicate distinct patterns of directional GC relationships within the hippocampus, specifically from the CA1 subfield to the dentate gyrus, prior to and during seizure onset. Our results suggest sequential and hierarchical temporal relationships between the CA1 and dentate gyrus within and across hippocampal hemispheres during seizure. Additionally, our analysis suggests a reversal in the direction of GC relationships during seizure, from an abnormal pattern to more anatomically expected pattern. This reversal correlates well with the observed behavioral transition from tonic to clonic seizure in time-locked video. These findings highlight the utility of GC to reveal dynamic directional temporal relationships between multichannel LFP recordings from multiple brain regions during unprovoked spontaneous seizures.

Relaxation-Based Feature Selection for Single-Trial Decoding of Bistable Perception

Bistable perception refers to the phenomenon of spontaneously alternating percepts while viewing the same stimulus continuously. Bistable stimuli allow dissociation between stimuli and perception, and thus, provide a unique opportunity for understanding the neural basis of visual perception. In this paper, we focus on a relaxation (RELAX) based algorithm to select features from the multitaper spectral estimates of the multichannel intracortical local field potential (LFP), simultaneously collected from the middle temporal visual cortex of a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. We demonstrate that RELAX surpasses the conventional sequential forward selection (SFS) by offering the flexibility of modifying selected features. We propose a redundancy reduction preprocessing technique to significantly reduce the computational load for both SFS and RELAX. We exploit the support vector machines classifier based on the selected features for single-trial decoding the reported perception. Our results demonstrate the excellent performance of the RELAX feature selection algorithm. Furthermore, we find that the features in the gamma frequency band (30-100 Hz) of LFP are most relevant to bistable SFM perception. This finding is novel in awake monkey studies and suggests that gamma oscillations carry the most discriminative information for bistable perception of SFM stimuli.

Temporal dynamics of information flow in the cerebral cortex

The nature of information flow from one area of the cerebral cortex to another is poorly understood. Frequency-dependent measures of information flow, based on multivariate autoregressive modeling of field potential time series, have shown promise for understanding information transactions between cortical areas (Liang et al., Neuro Report, 11 (2000) 2875–2880). In the present contribution, a time domain measure of information flow between two areas, called the directed transinformation (DTI), is described and applied to investigate causal influences directly from the field potential time series. We show that the DTI, as a generalization of mutual information, can be measured in a rather natural way, such that the interdependence of two time series is the sum of flow from X to Y, flow from Y to X, and instantaneous flow. We demonstrate the usefulness of this technique on both simulated data and multichannel local field potentials from macaque monkeys. Comparison with the frequency-dependent measure is also made.