Low-frequency Local Field Potentials Research Articles

Modulation of neural firing through intracellular ATP dynamics governed by energy feedback from the vascular system

We propose a simple model for neuro-glio-vascular interactions to emphasize on the role of energy feedback from the vascular system in brain's computations [1,2]. In [1], we introduced a bidirectional communication within a detailed biophysical model of neuron-astrocyte-vessel. We now compress this model to just two modules: the neuron and the 'energy' module. The energy module is a lumped representation of the astrocyte-vessel system; it receives neural firing activity as input and controls intracellular neuronal energy (ATPi) levels as a feedback. The model comprises of a quadratic integrate and fire neuron with a dynamic threshold, Vth, which further depends on the ATPi dynamics. Vth is high during ATPi deficit, making the neuron least excitable and vice versa for high ATPi conditions. The underlying principle of modeling Vth as a function of ATPi is based on the experiments describing the role of KATP channels in governing neural excitability [3]. These channels are ATP-dependent potassium channels and are open when ATPi is low, resulting in a depolarized membrane potential of the neuron during metabolically compromised states such as hypoxia [3]. The neuron model parameters are adapted to that of mammalian cortical pyramidal neuron [4]. Furthermore, ATPi dynamics are also modeled similar to [4], where ATPi consumption directly depends on neural spiking activity. The production rate of ATPi, ep is a crucial model parameter representing the local vascular activity. A wide range of neural dynamic behaviors: phasic bursting, tonic bursting and continuous spiking are observed by varying ep and external input current Iext. Furthermore, simulation of a network consisting of such energy-dependent neural units (Figure ​(Figure1A.)1A.) depicts that ep could modulate the Local field potential (LFP) frequencies and amplitudes (Figure ​(Figure1B.).1B.). Interestingly, low frequency LFP dominates under low ep conditions and could represent seizure-like activity observed in epilepsy. Although conventional neuroscience considers unidirectional influences from neurons to small vessels, there have been proposals that highlight the reverse influence from the vessels to the neurons [1,2] . The proposed 'neuron-energy' unit may be treated as a building-block in large scale models of neurovascular networks. Figure 1 A. Implemented network configuration with n (=1000) neurons, i synapses with c (=20%) connectivity. B. Simulated LFP at both high (=10) and low (=3) ep. C. & D. depicts spectrograms for low and high ep, respectively.

Modulation of the Intracortical LFP during Action Execution and Observation

The activity of mirror neurons in macaque ventral premotor cortex (PMv) and primary motor cortex (M1) is modulated by the observation of another's movements. This modulation could underpin well documented changes in EEG/MEG activity indicating the existence of a mirror neuron system in humans. Because the local field potential (LFP) represents an important link between macaque single neuron and human noninvasive studies, we focused on mirror properties of intracortical LFPs recorded in the PMv and M1 hand regions in two macaques while they reached, grasped and held different objects, or observed the same actions performed by an experimenter. Upper limb EMGs were recorded to control for covert muscle activity during observation.The movement-related potential (MRP), investigated as intracortical low-frequency LFP activity (<9 Hz), was modulated in both M1 and PMv, not only during action execution but also during action observation. Moreover, the temporal LFP modulations during execution and observation were highly correlated in both cortical areas. Beta power in both PMv and M1 was clearly modulated in both conditions. Although the MRP was detected only during dynamic periods of the task (reach/grasp/release), beta decreased during dynamic and increased during static periods (hold).Comparison of LFPs for different grasps provided evidence for partially nonoverlapping networks being active during execution and observation, which might be related to different inputs to motor areas during these conditions. We found substantial information about grasp in the MRP corroborating its suitability for brain-machine interfaces, although information about grasp was generally low during action observation.

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

Objective. Brain–machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main results. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

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.

Low- but Not High-Frequency LFP Correlates with Spontaneous BOLD Fluctuations in Rat Whisker Barrel Cortex.

Resting-state magnetic resonance imaging (rsMRI) is thought to reflect ongoing spontaneous brain activity. However, the precise neurophysiological basis of rsMRI signal remains elusive. Converging evidence supports the notion that local field potential (LFP) signal in the high-frequency range correlates with fMRI response evoked by a task (e.g., visual stimulation). It remains uncertain whether this relationship extends to rsMRI. In this study, we systematically modulated LFP signal in the whisker barrel cortex (WBC) by unilateral deflection of rat whiskers. Results show that functional connectivity between bilateral WBC was significantly modulated at the 2 Hz, but not at the 4 or 6 Hz, stimulus condition. Electrophysiologically, only in the low-frequency range (<5 Hz) was the LFP power synchrony in bilateral WBC significantly modulated at 2 Hz, but not at 4- or 6-Hz whisker stimulation, thus distinguishing these 2 experimental conditions, and paralleling the findings in rsMRI. LFP power synchrony in other frequency ranges was modulated in a way that was neither unique to the specific stimulus conditions nor parallel to the fMRI results. Our results support the hypothesis that emphasizes the role of low-frequency LFP signal underlying rsMRI.

Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces.

The best-performing brain-machine interfaces (BMIs) to date decode movement intention from intracortically recorded spikes, but these signals may be lost over time. A way to increase the useful lifespan of BMIs is to make more comprehensive use of available neural signals. Recent studies have demonstrated that the local field potential (LFP), a potentially more robust signal, can also be used to control a BMI. However, LFP-driven performance has fallen short of the best spikes-driven performance. Here we report a biomimetic BMI driven by low-frequency LFP that enabled a rhesus monkey to acquire and hold randomly placed targets with 99% success rate. Although LFP-driven performance was still worse than when decoding spikes, to the best of our knowledge this represents the highest-performing LFP-based BMI. We also demonstrate a new hybrid BMI that decodes cursor velocity using both spikes and LFP. This hybrid decoder improved performance over spikes-only decoding. Our results suggest that LFP can complement spikes when spikes are available or provide an alternative control signal if spikes are absent.

Decoding of Visual Attention from LFP Signals of Macaque MT

The local field potential (LFP) has recently been widely used in brain computer interfaces (BCI). Here we used power of LFP recorded from area MT of a macaque monkey to decode where the animal covertly attended. Support vector machines (SVM) were used to learn the pattern of power at different frequencies for attention to two possible positions. We found that LFP power at both low (<9 Hz) and high (31–120 Hz) frequencies contains sufficient information to decode the focus of attention. Highest decoding performance was found for gamma frequencies (31–120 Hz) and reached 82%. In contrast low frequencies (<9 Hz) could help the classifier reach a higher decoding performance with a smaller amount of training data. Consequently, we suggest that low frequency LFP can provide fast but coarse information regarding the focus of attention, while higher frequencies of the LFP deliver more accurate but less timely information about the focus of attention.

Phase-amplitude coupling and infraslow (

Resting state functional magnetic resonance imaging (fMRI) can identify network alterations that occur in complex psychiatric diseases and behaviors, but its interpretation is difficult because the neural basis of the infraslow BOLD fluctuations is poorly understood. Previous results link dynamic activity during the resting state to both infraslow frequencies in local field potentials (LFP) (<1 Hz) and band-limited power in higher frequency LFP (>1 Hz). To investigate the relationship between these frequencies, LFPs were recorded from rats under two anesthetics: isoflurane and dexmedetomidine. Signal phases were calculated from low-frequency LFP and compared to signal amplitudes from high-frequency LFP to determine if modulation existed between the two frequency bands (phase-amplitude coupling). Isoflurane showed significant, consistent phase-amplitude coupling at nearly all pairs of frequencies, likely due to the burst-suppression pattern of activity that it induces. However, no consistent phase-amplitude coupling was observed in rats that were anesthetized with dexmedetomidine. fMRI-LFP correlations under isoflurane using high frequency LFP were reduced when the low frequency LFP's influence was accounted for, but not vice-versa, or in any condition under dexmedetomidine. The lack of consistent phase-amplitude coupling under dexmedetomidine and lack of shared variance between high frequency and low frequency LFP as it relates to fMRI suggests that high and low frequency neural electrical signals may contribute differently, possibly even independently, to resting state fMRI. This finding suggests that researchers take care in interpreting the neural basis of resting state fMRI, as multiple dynamic factors in the underlying electrophysiology could be driving any particular observation.

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.

FMRI correlates for low frequency local field potentials appear as a spatiotemporal dynamic under multiple anesthetic conditions

In the previous decade, interest in the “functional connectivity” of the brain has greatly increased, but the nature of the signal underlying derived predictive metrics remains poorly understood [1]. A typical study uses functional magnetic resonance imaging (fMRI) and calculates regions of correlated low-frequency activity or “functional networks” when no task is being performed, the “resting state”. However, unlike traditional block/event based fMRI, the spontaneous fluctuations that determine such networks may not relate to a standard “hemodynamic response” to neural activity [2] and may be task and brain region dependent [1]. Ten rats were anesthetized with either isoflurane (iso) or dexmedetomidine (med). Each rat had simultaneous local field potentials (LFP) [3] recorded from implanted electrodes in bilateral primary somatosensory cortex (SI) simultaneously with single-slice fMRI of SI [4]. After preprocessing, signals were filtered to regions of significant spectral coherence (0.04-0.18Hz iso, 0.05-0.3Hz med). Pearson correlation (rt) was calculated between LFP signals at time shifts -10s to 10s relative to fMRI, at every fMRI voxel (Figure ​(Figure1B).1B). Instead of a simple hemodynamic response, the LFP correlates appeared both to have a component of spatial propagation (Figure ​(Figure1B,1B, white arrows), and alternation between positive and negative correlation. This was observed using both anesthesias and suggests that LFPs in coherent frequencies do not simply reflect local activation, but may instead be part of a large scale dynamic process. Using an fMRI-based algorithm validated in both anesthetized rats and awake humans [5], a spatiotemporal dynamic was produced that was highly similar to rt (Figure ​(Figure1C).1C). Spatial correlation (rs) between the two types of pattern reached a maximum at approximately the same shift between patterns in all rats, mean rs = 0.25 (med) and mean rs = 0.23 (iso), with mean rs > 0.10 indicating significance at p < 0.05 when using boot-strapping and correcting for multiple comparisons [6]. These results suggest that the neural basis of functional networks may be more complex than a simple hemodynamic response and possibly contains contributions from large-scale neuromodulatory processes. Figure 1 A. A coronal image of a rat’s brain in the same plane as the fMRI images used in this study. B. (med) rt between LFP and fMRI at each voxel, times listed are the time shift of LFP prior to fMRI. C. (med) fMRI pattern from Majeed et al. algorithm ...

Activity recall in a visual cortical ensemble.

Cue-triggered recall of learned temporal sequences is an important cognitive function that has been attributed to higher brain areas. Here, recordings in both anesthetized and awake rats demonstrate that after repeated stimulation with a moving spot that evoked sequential firing of an ensemble of primary visual cortex (V1) neurons, just a brief flash at the starting point of the motion path was sufficient to evoke a sequential firing pattern that reproduced the activation order evoked by the moving spot. The speed of recalled spike sequences may reflect the internal dynamics of the network rather than the motion speed. In awake animals, such recall was observed during a synchronized (“quiet wakeful”) brain state with large-amplitude, low-frequency local field potential (LFP), but not in a desynchronized (“active”) state with low-amplitude, high-frequency LFP. Such conditioning-enhanced, cue-evoked sequential spiking of a V1 ensemble may contribute to experience-based perceptual inference in a brain state-dependent manner.

The laminar and temporal structure of stimulus information in the phase of field potentials of auditory cortex.

Recent studies have shown that the phase of low-frequency local field potentials (LFPs) in sensory cortices carries a significant amount of information about complex naturalistic stimuli, yet the laminar circuit mechanisms and the aspects of stimulus dynamics responsible for generating this phase information remain essentially unknown. Here we investigated these issues by means of an information theoretic analysis of LFPs and current source densities (CSDs) recorded with laminar multi-electrode arrays in the primary auditory area of anesthetized rats during complex acoustic stimulation (music and broadband 1/f stimuli). We found that most LFP phase information originated from discrete "CSD events" consisting of granular-superficial layer dipoles of short duration and large amplitude, which we hypothesize to be triggered by transient thalamocortical activation. These CSD events occurred at rates of 2-4 Hz during both stimulation with complex sounds and silence. During stimulation with complex sounds, these events reliably reset the LFP phases at specific times during the stimulation history. These facts suggest that the informativeness of LFP phase in rat auditory cortex is the result of transient, large-amplitude events, of the "evoked" or "driving" type, reflecting strong depolarization in thalamo-recipient layers of cortex. Finally, the CSD events were characterized by a small number of discrete types of infragranular activation. The extent to which infragranular regions were activated was stimulus dependent. These patterns of infragranular activations may reflect a categorical evaluation of stimulus episodes by the local circuit to determine whether to pass on stimulus information through the output layers.

Broadband Local Field Potentials Correlate with Spontaneous Fluctuations in Functional Magnetic Resonance Imaging Signals in the Rat Somatosensory Cortex Under Isoflurane Anesthesia

Resting-state functional magnetic resonance imaging (fMRI) is widely used for exploring spontaneous brain activity and large-scale networks; however, the neural processes underlying the observed resting-state fMRI signals are not fully understood. To investigate the neural correlates of spontaneous low-frequency fMRI fluctuations and functional connectivity, we developed a rat model of simultaneous fMRI and multiple-site intracortical neural recordings. This allowed a direct comparison to be made between the spontaneous signals and interhemispheric connectivity measured with the two modalities. Results show that low-frequency blood oxygen level-dependent (BOLD) fluctuations (<0.1 Hz) correlate significantly with slow power modulations (<0.1 Hz) of local field potentials (LFPs) in a broad frequency range (1-100 Hz) under isoflurane anesthesia (1%-1.8%). Peak correlation occurred between neural and hemodynamic activity when the BOLD signal was delayed by ~4 sec relative to the LFP signal. The spatial location and extent of correlation was highly reproducible across studies, with the maximum correlation localized to a small area surrounding the site of microelectrode recording and to the homologous area in the contralateral hemisphere for most rats. Interhemispheric connectivity was calculated using BOLD correlation and band-limited LFP (1-4, 4-8, 8-14, 14-25, 25-40, and 40-100 Hz) coherence. Significant coherence was observed for the slow power changes of all LFP frequency bands as well as in the low-frequency BOLD data. A preliminary investigation of the effect of anesthesia on interhemispheric connectivity indicates that coherence in the high-frequency LFP bands declines with increasing doses of isoflurane, whereas coherence in the low-frequency LFP bands and the BOLD signal increases. These findings suggest that resting-state fMRI signals might be a reflection of broadband LFP power modulation, at least in isoflurane-anesthetized rats.

Does the information in the phase of low frequency LFP reflect the low frequency envelope of local spike rates?

Recently, it has been shown that when the timing of spikes is measured relative to the phase of the cortical local field potentials (LFP), spikes can carry substantial more information about an external stimulus [1]. Experimental studies in sensory cortices of macaque have shown that the extra information obtained with such phase-of-firing codes above that in the firing rate alone ranges from 55% in primary visual cortex [1] to more than 100% in primary auditory cortex [2]. Here, we use a mathematical model that relates local spike trains and the resulting LFP, to explain the emergence of the phase-of-firing codes in cortex. The model is based on the one proposed in [3] and incorporates two types of integration over the spiking activity: i) a time convolution that results from the filtering properties of neural structures [4], which embeds history effects in LFP from past spiking activity, and ii) an integration step over the activity of neurons in the neighbourhood of the measuring electrode. When the spikes recorded from macaque primary visual cortex were used to synthesize the LFP, the model could reproduce the phase-of-firing information found using the real LFP, as shown in Figure ​Figure1.1. This suggests that an important component of phase-of-firing information originates from the surrounding neural population and past spiking activity. The next question that arises is what is the relative contribution of the neuron population size and the length of the firing rate history embedded in the LFP. We are currently investigating this question by parametrically varying both the population size and time integration ranges in generating the synthetic LFP. Figure 1 Comparison of the phase-of-firing information using synthetic and real LFP. The original data corresponds to LFP and spiking activity from 78 recordings channels in macaque V1, obtained while the animals were presented a movie [1]. A. Information in the ...

BOLD responses to different temporal frequency stimuli in the lateral geniculate nucleus and visual cortex: insights into the neural basis of fMRI.

The neural basis of the blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) remains largely unknown after decades of research. To investigate this issue, the unique property of the temporal frequency tuning that could separate neural input and output in the primary visual cortex was used as a model. During moving grating stimuli of 1, 2, 10 and 20Hz temporal frequencies, we measured 9.4-T BOLD fMRI responses simultaneously in the primary visual cortex of area 17 (A17) and area 18 (A18), and the lateral geniculate nucleus (LGN) of isoflurane-anesthetized cat. Our results showed that preferred temporal frequencies of the BOLD responses for A17, A18 and LGN were 3.1Hz, 4.5Hz and 6.0Hz, respectively, which were comparable to the previously reported electrophysiological data. Additionally, the difference of BOLD response onset time between LGN and A17 was 0.5s, which is 18 times larger than the difference of neural activity onset time between these areas. We then compared the frequency-dependent BOLD fMRI response of A17 with tissue partial pressure of oxygen (pO(2)) and electrophysiological data of the same animal model reported by Viswanathan and Freeman (Nature Neuroscience, 2007). The BOLD tuning curve resembled the low frequency band (<12Hz) of local field potential (LFP) tuning curve rather than spiking activity, gamma band (25-90Hz) of LFP, and tissue pO(2) tuning curves, suggesting that the BOLD fMRI signal relates closer to low frequency LFP.

Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices

A prominent feature of motor cortex field potentials during movement is a distinctive low-frequency local field potential (lf-LFP) (<4 Hz), referred to as the movement event-related potential (mEP). The lf-LFP appears to be a global signal related to regional synaptic input, but its relationship to nearby output signaled by single unit spiking activity (SUA) or to movement remains to be established. Previous studies comparing information in primary motor cortex (MI) lf-LFPs and SUA in the context of planar reaching tasks concluded that lf-LFPs have more information than spikes about movement. However, the relative performance of these signals was based on a small number of simultaneously recorded channels and units, or for data averaged across sessions, which could miss information of larger-scale spiking populations. Here, we simultaneously recorded LFPs and SUA from two 96-microelectrode arrays implanted in two major motor cortical areas, MI and ventral premotor (PMv), while monkeys freely reached for and grasped objects swinging in front of them. We compared arm end point and grip aperture kinematics' decoding accuracy for lf-LFP and SUA ensembles. The results show that lf-LFPs provide enough information to reconstruct kinematics in both areas with little difference in decoding performance between MI and PMv. Individual lf-LFP channels often provided more accurate decoding of single kinematic variables than any one single unit. However, the decoding performance of the best single unit among the large population usually exceeded that of the best single lf-LFP channel. Furthermore, ensembles of SUA outperformed the pool of lf-LFP channels, in disagreement with the previously reported superiority of lf-LFP decoding. Decoding results suggest that information in lf-LFPs recorded from intracortical arrays may allow the reconstruction of reach and grasp for real-time neuroprosthetic applications, thus potentially supplementing the ability to decode these same features from spiking populations.

Cortical dynamics during naturalistic sensory stimulations: Experiments and models

We report the results of our experimental and theoretical investigations of the neural response dynamics in primary visual cortex (V1) during naturalistic visual stimulation. We recorded Local Field Potentials (LFPs) and spiking activity from V1 of anaesthetized macaques during binocular presentation of Hollywood color movies. We analyzed these recordings with information theoretic methods, and found that visual information was encoded mainly by two bands of LFP responses: the network fluctuations measured by the phase and power of low-frequency (less than 12 Hz) LFPs; and fast gamma-range (50–100 Hz) oscillations. Both the power and phase of low frequency LFPs carried information largely complementary to that carried by spikes, whereas gamma range oscillations carried information largely redundant to that of spikes. To interpret these results within a quantitative theoretical framework, we then simulated a sparsely connected recurrent network of excitatory and inhibitory neurons receiving slowly varying naturalistic inputs, and we determined how the LFPs generated by the network encoded information about the inputs. We found that this simulated recurrent network reproduced well the experimentally observed dependency of LFP information upon frequency. This network encoded the overall strength of the input into the power of gamma-range oscillations generated by inhibitory–excitatory neural interactions, and encoded slow variations in the input by entraining the network LFP at the corresponding frequency. This dynamical behavior accounted quantitatively for the independent information carried by high and low frequency LFPs, and for the experimentally observed cross-frequency coupling between phase of slow LFPs and the power of gamma LFPs. We also present new results showing that the model’s dynamics also accounted for the extra visual information that the low-frequency LFP phase of spike firing carries beyond that carried by spike rates. Overall, our results suggest biological mechanisms by which cortex can multiplex information about naturalistic sensory environments.

Decoding 3-D Reach and Grasp Kinematics From High-Frequency Local Field Potentials in Primate Primary Motor Cortex

Intracortical microelectrode array recordings generate a variety of neural signals with potential application as control signals in neural interface systems. Previous studies have focused on single and multiunit activity (MUA), as well as low-frequency local field potentials (LFPs), but have not explored higher frequency (>200 Hz) LFPs. In addition, the potential to decode 3-D reach and grasp kinematics based on LFPs has not been demonstrated. Here, we use mutual information and decoding analyses to probe the information content about 3-D reaching and grasping of seven different LFP frequency bands in the range of 0.3-400 Hz. LFPs were recorded via 96-microelectrode arrays in primary motor cortex (M1) of two monkeys performing free reaching to grasp moving objects. Mutual information analyses revealed that higher frequency bands (e.g., 100-200 and 200-400 Hz) carried the most information about the examined kinematics. Furthermore, Kalman filter decoding revealed that broad-band high frequency LFPs, likely reflecting MUA, provided the best decoding performance as well as substantial accuracy in reconstructing reach kinematics, grasp aperture, and aperture velocity. These results indicate that LFPs, especially high frequency bands, could be useful signals for neural interfaces controlling 3-D reach and grasp kinematics.

Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA

Functional magnetic resonance imaging (fMRI) based on the so-called blood oxygen level-dependent (BOLD) contrast is a powerful tool for studying brain function not only locally but also on the large scale. Most studies assume a simple relationship between neural and BOLD activity, in spite of the fact that it is important to elucidate how the “when” and “what” components of neural activity are correlated to the “where” of fMRI data. Here we conducted simultaneous recordings of neural and BOLD signal fluctuations in primary visual (V1) cortex of anesthetized monkeys. We explored the neurovascular relationship during periods of spontaneous activity by using temporal kernel canonical correlation analysis (tkCCA). tkCCA is a multivariate method that can take into account any features in the signals that univariate analysis cannot. The method detects filters in voxel space (for fMRI data) and in frequency–time space (for neural data) that maximize the neurovascular correlation without any assumption of a hemodynamic response function (HRF). Our results showed a positive neurovascular coupling with a lag of 4–5 s and a larger contribution from local field potentials (LFPs) in the γ range than from low-frequency LFPs or spiking activity. The method also detected a higher correlation around the recording site in the concurrent spatial map, even though the pattern covered most of the occipital part of V1. These results are consistent with those of previous studies and represent the first multivariate analysis of intracranial electrophysiology and high-resolution fMRI.

Cortical low-frequency local field potentials and action potentials act as largely independent information channels

Cortical low-frequency local field potentials and action potentials act as largely independent information channels