Encoding of brain state changes in local field potentials modulated by motor behaviors

Local field potentials (LFPs) measure aggregate neural activity resulting from the coordinated firing of neurons within a local network. We hypothesized that state parameters associated with the underlying brain dynamics may be encoded in LFPs but may not be directly measurable in the signal temporal and spectral contents. Using the Kalman filter we estimated latent state changes in LFPs recorded in monkey motor cortical areas during the execution of a visually instructed reaching task, under different applied force conditions. Prior to the estimation, matched filtering was performed to decouple behavior-relevant signals (Stamoulis and Richardson, J Comput Neurosci, 2009) from unrelated background oscillations. State changes associated with baseline oscillations appeared insignificant. In contrast, state changes estimated from LFP components associated with the execution of movement were significant. Approximately direction-invariant state vectors were consistently observed. Their patterns appeared invariant also to force field conditions, with a peak in the first 200 ms of the movement interval, but exponentially decreasing to the zero state approximately 200 ms from movement onset, also the time at which movement velocity reached its peak. Thus, state appeared to be modulated by the dynamics of movement but neither by movement direction nor by the mechanical environment. Finally, we compared state vectors estimated using the Kalman filter to the basis functions obtained through Principal Component Analysis. The pattern of the estimated state vector was very similar to that of the first PCA component, further suggesting that LFPs may directly encode brain state fluctuations associated with the dynamics of behavior.     

Comparison of LFP-based and spike-based spectro-temporal receptive fields and cross-correlation in cat primary auditory cortex

Multi-electrode array recordings of spike and local field potential (LFP) activity were made from primary auditory cortex of 12 normal hearing, ketamine-anesthetized cats. We evaluated 259 spectro-temporal receptive fields (STRFs) and 492 frequency-tuning curves (FTCs) based on LFPs and spikes simultaneously recorded on the same electrode. We compared their characteristic frequency (CF) gradients and their cross-correlation distances. The CF gradient for spike-based FTCs was about twice that for 2-40 Hz-filtered LFP-based FTCs, indicating greatly reduced frequency selectivity for LFPs. We also present comparisons for LFPs band-pass filtered between 4-8 Hz, 8-16 Hz and 16-40 Hz, with spike-based STRFs, on the basis of their marginal frequency distributions. We find on average a significantly larger correlation between the spike based marginal frequency distributions and those based on the 16-40 Hz filtered LFP, compared to those based on the 4-8 Hz, 8-16 Hz and 2-40 Hz filtered LFP. This suggests greater frequency specificity for the 16-40 Hz LFPs compared to those of lower frequency content. For spontaneous LFP and spike activity we evaluated 1373 pair correlations for pairs with >200 spikes in 900 s per electrode. Peak correlation-coefficient space constants were similar for the 2-40 Hz filtered LFP (5.5 mm) and the 16-40 Hz LFP (7.4 mm), whereas for spike-pair correlations it was about half that, at 3.2 mm. Comparing spike-pairs with 2-40 Hz (and 16-40 Hz) LFP-pair correlations showed that about 16% (9%) of the variance in the spike-pair correlations could be explained from LFP-pair correlations recorded on the same electrodes within the same electrode array. This larger correlation distance combined with the reduced CF gradient and much broader frequency selectivity suggests that LFPs are not a substitute for spike activity in primary auditory cortex.     

Brain Activity at 70–80 Hz Changes during Olfactory Stimulation Protocols in Drosophila

Oscillatory and synchronized activities in the mammalian brain have been correlated with the execution of complex cognitive tasks. Similar oscillations have been observed in local field potentials (LFPs) in flies, in this case correlated with different attentional states. To further test the significance of these oscillations we recorded LFPs from the brain of Drosophila melanogaster as it responded to the presentation of olfactory stimuli. We find that responses in the 70–80 Hz range increase during olfactory stimulation. Recurrent stimulation specifically decreased the power of LFPs in this frequency range. Delivery of electric shocks before olfactory stimulation modulated LFPs in the 70–80 Hz range by evoking a transient increase. These results suggest that these signals are a simple neuronal correlate of higher-order olfactory processing in flies.     

Subthalamic local field beta oscillations during ongoing deep brain stimulation in Parkinson's disease in hyperacute and chronic phases

In the past years, local field potential (LFP) signals recorded from the subthalamic nucleus (STN) in patients undergoing deep brain stimulation (DBS) for Parkinson's disease (PD) disclosed that DBS has a controversial effect on STN beta oscillations recorded 2-7 days after surgery for macroelectrode implantation. Nothing is known about these DBS-induced oscillatory changes 30 days after surgery. We recorded STN LFPs during ongoing DBS in 7 patients with PD, immediately (hyperacute phase) and 30 days (chronic phase) after surgery. STN LFP recordings showed stationary intranuclear STN beta LFP activity in hyperacute and chronic phases, confirming that beta peaks were also present in chronic recordings. Power spectra of nuclei with significant beta activity (54% of the sample) showed that it decreased significantly during DBS (p=0.021) under both recording conditions. The time course of beta activity showed more evident DBS-induced changes in the chronic than in the hyperacute phase (p=0.014). DBS-induced changes in STN beta LFPs in patients undergoing DBS in chronic phase provide useful information for developing a new neurosignal-controlled adaptive DBS system.     

Sensory information in local field potentials and spikes from visual and auditory cortices: time scales and frequency bands

Studies analyzing sensory cortical processing or trying to decode brain activity often rely on a combination of different electrophysiological signals, such as local field potentials (LFPs) and spiking activity. Understanding the relation between these signals and sensory stimuli and between different components of these signals is hence of great interest. We here provide an analysis of LFPs and spiking activity recorded from visual and auditory cortex during stimulation with natural stimuli. In particular, we focus on the time scales on which different components of these signals are informative about the stimulus, and on the dependencies between different components of these signals. Addressing the first question, we find that stimulus information in low frequency bands (<12 Hz) is high, regardless of whether their energy is computed at the scale of milliseconds or seconds. Stimulus information in higher bands (>50 Hz), in contrast, is scale dependent, and is larger when the energy is averaged over several hundreds of milliseconds. Indeed, combined analysis of signal reliability and information revealed that the energy of slow LFP fluctuations is well related to the stimulus even when considering individual or few cycles, while the energy of fast LFP oscillations carries information only when averaged over many cycles. Addressing the second question, we find that stimulus information in different LFP bands, and in different LFP bands and spiking activity, is largely independent regardless of time scale or sensory system. Taken together, these findings suggest that different LFP bands represent dynamic natural stimuli on distinct time scales and together provide a potentially rich source of information for sensory processing or decoding brain activity.     

Selecting the signals for a brain-machine interface

Brain-machine interfaces are being developed to assist paralyzed patients by enabling them to operate machines with recordings of their own neural activity. Recent studies show that motor parameters, such as hand trajectory, and cognitive parameters, such as the goal and predicted value of an action, can be decoded from the recorded activity to provide control signals. Neural prosthetics that use simultaneously a variety of cognitive and motor signals can maximize the ability of patients to communicate and interact with the outside world. Although most studies have recorded electroencephalograms or spike activity, recent research shows that local field potentials (LFPs) offer a promising additional signal. The decode performances of LFPs and spike signals are comparable and, because LFP recordings are more long lasting, they might help to increase the lifetime of the prosthetics.     

大鼠压杆行为的运动皮层局部场电位研究

目的：提取与大鼠右前肢运动相关联的初级运动皮层场电位的信号特征,并探讨依据局部场电位（LFP）识别前肢运动行为的可行性。方法：4只SD大鼠通过训练习得压杆取水操作,然后在左右脑初级运动皮层M1区分别植入多通道束状微电极,术后恢复后进行大鼠压杆行为实验,以2 kHz/s速率记录深部脑电信号及压杆状态信号,同步记录行为过程的视频信号。以通道间的差分信号作为局部场电位信号,分析局部场电位信号的时域特征,进行聚类分析。以压杆状态信号和视频分析为判定依据,对聚类结果进行分析。结果：局部场电位信号在大鼠压杆动作时明显增强,不同通道的局部场电位信号幅值、波形有差异,表明与前肢运动相关联的M1区局部场电位信号有空间分布特征;依据阈值准则从局部场电位信号检测压杆行为的检出率为80%。结论：依据局部场电位信号特征对大鼠前肢运动进行检测具有可行性。     

Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential

Despite its century-old use, the interpretation of local field potentials (LFPs), the low-frequency part of electrical signals recorded in the brain, is still debated. In cortex the LFP appears to mainly stem from transmembrane neuronal currents following synaptic input, and obvious questions regarding the ‘locality’ of the LFP are: What is the size of the signal-generating region, i.e., the spatial reach, around a recording contact? How far does the LFP signal extend outside a synaptically activated neuronal population? And how do the answers depend on the temporal frequency of the LFP signal? Experimental inquiries have given conflicting results, and we here pursue a modeling approach based on a well-established biophysical forward-modeling scheme incorporating detailed reconstructed neuronal morphologies in precise calculations of population LFPs including thousands of neurons. The two key factors determining the frequency dependence of LFP are the spatial decay of the single-neuron LFP contribution and the conversion of synaptic input correlations into correlations between single-neuron LFP contributions. Both factors are seen to give low-pass filtering of the LFP signal power. For uncorrelated input only the first factor is relevant, and here a modest reduction (<50%) in the spatial reach is observed for higher frequencies (>100 Hz) compared to the near-DC () value of about . Much larger frequency-dependent effects are seen when populations of pyramidal neurons receive correlated and spatially asymmetric inputs: the low-frequency () LFP power can here be an order of magnitude or more larger than at 60 Hz. Moreover, the low-frequency LFP components have larger spatial reach and extend further outside the active population than high-frequency components. Further, the spatial LFP profiles for such populations typically span the full vertical extent of the dendrites of neurons in the population. Our numerical findings are backed up by an intuitive simplified model for the generation of population LFP.     

Cortical local field potential encodes movement intentions in the posterior parietal cortex

The cortical local field potential (LFP) is a summation signal of excitatory and inhibitory dendritic potentials that has recently become of increasing interest. We report that LFP signals in the parietal reach region (PRR) of the posterior parietal cortex of macaque monkeys have temporal structure that varies with the type of planned or executed motor behavior. LFP signals from PRR provide better decode performance for reaches compared to saccades and have stronger coherency with simultaneously recorded spiking activity during the planning of reach movements than during saccade planning. LFP signals predict the animal's behavioral state (e.g., planning a reach or saccade) and the direction of the currently planned movement from single-trial information. This new evidence provides further support for a role of the parietal cortex in movement planning and the potential application of LFP signals for a brain-machine interface.     

The Primary Visual Cortex Is Differentially Modulated by Stimulus-Driven and Top-Down Attention

Selective attention can be focused either volitionally, by top-down signals derived from task demands, or automatically, by bottom-up signals from salient stimuli. Because the brain mechanisms that underlie these two attention processes are poorly understood, we recorded local field potentials (LFPs) from primary visual cortical areas of cats as they performed stimulus-driven and anticipatory discrimination tasks. Consistent with our previous observations, in both tasks, we found enhanced beta activity, which we have postulated may serve as an attention carrier. We characterized the functional organization of task-related beta activity by (i) cortical responses (EPs) evoked by electrical stimulation of the optic chiasm and (ii) intracortical LFP correlations. During the anticipatory task, peripheral stimulation that was preceded by high-amplitude beta oscillations evoked large-amplitude EPs compared with EPs that followed low-amplitude beta. In contrast, during the stimulus-driven task, cortical EPs preceded by high-amplitude beta oscillations were, on average, smaller than those preceded by low-amplitude beta. Analysis of the correlations between the different recording sites revealed that beta activation maps were heterogeneous during the bottom-up task and homogeneous for the top-down task. We conclude that bottom-up attention activates cortical visual areas in a mosaic-like pattern, whereas top-down attentional modulation results in spatially homogeneous excitation.     

How local is the local field potential?

Local field potentials (LFPs) are of growing importance in neurophysiological investigations. LFPs supplement action potential recordings by indexing activity relevant to EEG, magnetoencephalographic, and hemodynamic (fMRI) signals. Recent reports suggest that LFPs reflect activity within very small domains of several hundred micrometers. We examined this conclusion by comparing LFP, current source density (CSD), and multiunit activity (MUA) signals in macaque auditory cortex. Estimated by frequency tuning bandwidths, these signals' "listening areas" differ systematically with an order of MUA?< CSD?< LFP. Computational analyses confirm that observed LFPs receive local contributions. Direct measurements indicate passive spread of LFPs to sites more than a centimeter from their origins. These findings appear to be independent of the frequency content of the LFP. Our results challenge the idea that LFP recordings typically integrate over extremely circumscribed local domains. Rather, LFPs appear as a mixture of local potentials with "volume conducted" potentials from distant sites.     

Dysfunctional Behavioral Modulation of Corticostriatal Communication in the R6/2 Mouse Model of Huntington’s Disease

Background

In Huntington’s disease (HD), motor symptoms develop prior to the widespread loss of neurons in striatum and cerebral cortex. The aim of this study was to examine dysfunctional patterns of corticostriatal communication during spontaneously occurring behaviors in a transgenic mouse model of HD.

Methodology/Principal Findings

Local field potentials (LFPs) were recorded from two closely interconnected areas, motor cortex and dorsal striatum, in wild-type controls (WT, n = 14) and a widely used transgenic HD model (R6/2 mice, n = 12). All mice were between the ages of 7–9 weeks, a critical period of motor symptom development in R6/2s. Recordings were obtained while the mice were behaving freely in an open field. Specific LFP activity was extracted using timestamps for three increasingly demanding motor behaviors: 1) resting; 2) grooming; and 3) active exploration. Power spectral densities (PSD) were obtained for the cortical and striatal LFPs as well as coherence levels and relative phase across the frequency spectrum. In both brain regions, only R6/2s showed high frequency LFP oscillations during rest and grooming. As behavior increased from resting to exploring, corticostriatal synchrony at high frequencies declined in R6/2s, completely opposite to the WT pattern. R6/2s also exhibited nearly in-phase corticostriatal activity (cortex phase leads of ∼5°), while the WTs consistently showed cortical phase lags of ∼20° across all assessed behaviors, indicating a lead role for striatum.

Conclusions/Significance

Our results add to growing evidence for altered communication between cortex and striatum in HD and suggest more generally that increasingly demanding motor behaviors differentially modulate corticostriatal communication. Our data also suggest conduction delays in R6/2 corticostriatal transmission, leading to compensatory speeding of LFP activity, as evidenced by the presence of high frequency LFP oscillations.     

Cortical Neurovascular Coupling Driven by Stimulation of Channelrhodopsin-2

While functional imaging is widely used in studies of the brain, how well the hemodynamic signal represents the underlying neural activity is still unclear. And there is a debate on whether hemodynamic signal is more tightly related to synaptic activity or action potentials. This study intends to address these questions by examining neurovascular coupling driven by pyramidal cells in the motor cortex of rats. Pyramidal cells in the motor cortex of rats were selectively transduced with the light sensitive cation channel channelrhodopsin-2 (ChR2). Electrophysiological recordings and optical intrinsic signal imaging were performed simultaneously and synchronously to capture the neural activity and hemodynamics induced by optical stimulation of ChR2-expressing pyramidal cells. Our results indicate that both synaptic activity (local field potential, LFP) and action potentials (multi-unit activity, MUA) are tightly related to hemodynamic signals. While LFPs in γ band are better in predicting hemodynamic signals elicited by short stimuli, MUA has better predictions to hemodynamic signals elicited by long stimuli. Our results also indicate that strong nonlinearity exists in neurovascular coupling.     

Uncoupling of brain activity from movement defines arousal States in Drosophila

BACKGROUND: An animal's state of arousal is fundamental to all of its behavior. Arousal is generally ascertained by measures of movement complemented by brain activity recordings, which can provide signatures independently of movement activity. Here we examine the relationships among movement, arousal state, and local field potential (LFP) activity in the Drosophila brain.RESULTS: We have measured the correlation between local field potentials (LFPs) in the brain and overt movements of the fruit fly during different states of arousal, such as spontaneous daytime waking movement, visual arousal, spontaneous night-time movement, and stimulus-induced movement. We found that the correlation strength between brain LFP activity and movement was dependent on behavioral state and, to some extent, on LFP frequency range. Brain activity and movement were uncoupled during the presentation of visual stimuli and also in the course of overnight experiments in the dark. Epochs of low correlation or uncoupling were predictive of increased arousal thresholds even in moving flies and thus define a distinct state of arousal intermediate between sleep and waking in the fruit fly.CONCLUSIONS: These experiments indicate that the relationship between brain LFPs and movement in the fruit fly is dynamic and that the degree of coupling between these two measures of activity defines distinct states of arousal.     

The role of the cerebellum in modulating voluntary limb movement commands

We recorded the activity of cerebellar Purkinje cells (PCs), primary motor cortical (M1) neurons, and limb EMG signals while monkeys executed a sequential reaching and button pressing task. PC simple spike discharge generally correlated well with the activity of one or more forelimb muscles. Surprisingly, given the inhibitory projection of PCs, only about one quarter of the correlations were negative. The largest group of neurons burst during movement and were positively correlated with EMG signals, while another significant group burst and were negatively correlated. Among the PCs that paused during movement most were negatively correlated with EMG. The strength of these various correlations was somewhat weaker, on average, than equivalent correlations between M1 neurons and EMG signals. On the other hand, there were no significant differences in the timing of the onset of movement related discharge among these groups of PCs, or between the PCs and M1 neurons. PC discharge was modulated largely in phase, or directly out of phase, with muscle activity. The nearly synchronous activation of PCs and muscles yielded positive correlations, despite the fact that the synaptic effect of the PC discharge is inhibitory. The apparent function of this inhibition is to restrain activity in the limb premotor network, shaping it into a spatiotemporal pattern that is appropriate for controlling the many muscles that participate in this task. The observed timing suggests that the cerebellar cortex learns to modulate PC discharge predictively. Through the cerebellar nucleus, this PC signal is combined with an underlying cerebral cortical signal. In this manner the cerebellum refines the descending command as compared with the relatively crude version generated when the cerebellum is damaged.     

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.     

Phase-of-firing coding of natural visual stimuli in primary visual cortex

We investigated the hypothesis that neurons encode rich naturalistic stimuli in terms of their spike times relative to the phase of ongoing network fluctuations rather than only in terms of their spike count. We recorded local field potentials (LFPs) and multiunit spikes from the primary visual cortex of anaesthetized macaques while binocularly presenting a color movie. We found that both the spike counts and the low-frequency LFP phase were reliably modulated by the movie and thus conveyed information about it. Moreover, movie periods eliciting higher firing rates also elicited a higher reliability of LFP phase across trials. To establish whether the LFP phase at which spikes were emitted conveyed visual information that could not be extracted by spike rates alone, we compared the Shannon information about the movie carried by spike counts to that carried by the phase of firing. We found that at low LFP frequencies, the phase of firing conveyed 54% additional information beyond that conveyed by spike counts. The extra information available in the phase of firing was crucial for the disambiguation between stimuli eliciting high spike rates of similar magnitude. Thus, phase coding may allow primary cortical neurons to represent several effective stimuli in an easily decodable format.     

Electrical signals propagate unbiased in cortex

The greater spatial coherence of local field potentials (LFPs) compared with that of spiking activity has been attributed to frequency-dependent propagation of signals through the cortical medium. However, in this issue of Neuron, Logothetis and colleagues show that signal propagation within cortex is largely unbiased across different frequencies, thus suggesting a more functional and interpretable basis of LFP coherence.     

Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex.

Cortical field potentials have been used for decades in neurophysiological studies to probe spatio-temporal activity patterns of local populations of neurons. Recently, however, interest in these signals was spurred as they were proposed as potential control signals for neuronal motor prostheses, i.e., for devices fit to record and decode brain activity to restore motor functions in paralyzed patients. Little is known, however, about the functional significance of these cortical field potentials. Here we compared information about arm movement direction in two types of movement related cortical field potentials, obtained during a four direction center-out arm reaching paradigm: local field potentials (LFPs) recorded with intracortical micro-electrodes from monkey motor cortex, and epicortical field potentials (EFPs) recorded with macro-electrode arrays subdurally implanted on the surface of the human cerebral cortex. While monkey LFPs showed a typical sequence of positive and negative potential peaks, an initial negative peak was the most salient feature of human EFPs. Individual contralateral LFPs from the monkey motor cortex carried approximately twice as much decoded information (DI) about arm movement direction (median 0.27 bit) as did individual EFPs from the contralateral hand/arm area of primary motor cortex in humans (median 0.12 bit). This relation was similar to the relation between median peak signal-to-noise ratios for directional modulation of movement related potentials (MRPs) of both types of signals. We discuss possible reasons for the observed differences, amongst them epi- vs. intracortical recording and the different electrode dimensions used to measure EFPs and LFPs.     

Neurophysiology of the BOLD fMRI signal in awake monkeys

BACKGROUND: Simultaneous intracortical recordings of neural activity and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in primary visual cortex of anesthetized monkeys demonstrated varying degrees of correlation between fMRI signals and the different types of neural activity, such as local field potentials (LFPs), multiple-unit activity (MUA), and single-unit activity (SUA). One important question raised by the aforementioned investigation is whether the reported correlations also apply to alert subjects. RESULTS: Monkeys were trained to perform a fixation task while stimuli within the receptive field of each recording site were used to elicit neural responses followed by a BOLD response. We show -- also in alert behaving monkeys -- that although both LFP and MUA make significant contributions to the BOLD response, LFPs are better and more reliable predictors of the BOLD signal. Moreover, when MUA responses adapt but LFP remains unaffected, the BOLD signal remains unaltered. CONCLUSIONS: The persistent coupling of the BOLD signal to the field potential when LFP and MUA have different time evolutions suggests that BOLD is primarily determined by the local processing of inputs in a given cortical area. In the alert animal the largest portion of the BOLD signal's variance is explained by an LFP range (20-60 Hz) that is most likely related to neuromodulation. Finally, the similarity of the results in alert and anesthetized subjects indicates that at least in V1 anesthesia is not a confounding factor. This enables the comparison of human fMRI results with a plethora of electrophysiological results obtained in alert or anesthetized animals.