Application of matched filtering to identify behavioral modulation of brain oscillations

Brain oscillations modulated by motor behaviors are coupled to steady-state and other potentially unrelated to movement oscillations, with energy in the same frequency bands as the signals of interest. We applied matched filtering, a quasi-optimum signal detection technique, to decouple and extract movement-related signals from local field potentials (LFPs) recorded in monkey motor cortical areas during the execution of a visually instructed reach-out task. Using a matched-filterbank, we examined coupling and interference of pre-movement and initial steady-state oscillations with movement-induced signals. Once these signal contributions were eliminated, we were able to identify significant correlations of the residual signals with behavioral parameters, which appeared attenuated by pre-movement signal interference in the raw LFPs. Specifically, the maximum and minimum amplitudes of filtered LFPs were directly modulated by peak movement velocity and micro-movements, respectively, identified in recorded hand velocity profiles. In addition, we identified phase correlations between signals during the delay (when the instructional cue was presented) and movement intervals, as well as modulation of LFP phase by movement direction. For pairs of orthogonal movement directions, corresponding LFP signals were consistently out of phase. Finally, β-band energy which is typically reduced during movement execution, possibly partly due to destructive interference between the modulated by behavior signal and unrelated oscillations, appeared to be recovered in the filtered signals.     

A review on directional information in neural signals for brain-machine interfaces

Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.     

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.     

High accuracy decoding of movement target direction in non-human primates based on common spatial patterns of local field potentials

Background

The current development of brain-machine interface technology is limited, among other factors, by concerns about the long-term stability of single- and multi-unit neural signals. In addition, the understanding of the relation between potentially more stable neural signals, such as local field potentials, and motor behavior is still in its early stages.

Methodology/Principal Findings

We tested the hypothesis that spatial correlation patterns of neural data can be used to decode movement target direction. In particular, we examined local field potentials (LFP), which are thought to be more stable over time than single unit activity (SUA). Using LFP recordings from chronically implanted electrodes in the dorsal premotor and primary motor cortex of non-human primates trained to make arm movements in different directions, we made the following observations: (i) it is possible to decode movement target direction with high fidelity from the spatial correlation patterns of neural activity in both primary motor (M1) and dorsal premotor cortex (PMd); (ii) the decoding accuracy of LFP was similar to the decoding accuracy obtained with the set of SUA recorded simultaneously; (iii) directional information varied with the LFP frequency sub-band, being greater in low (0.3–4 Hz) and high (48–200 Hz) frequency bands than in intermediate bands; (iv) the amount of directional information was similar in M1 and PMd; (v) reliable decoding was achieved well in advance of movement onset; and (vi) LFP were relatively stable over a period of one week.

Conclusions/Significance

The results demonstrate that the spatial correlation patterns of LFP signals can be used to decode movement target direction. This finding suggests that parameters of movement, such as target direction, have a stable spatial distribution within primary motor and dorsal premotor cortex, which may be used for brain-machine interfaces.     

The relations between long-latency reflexes in hand muscles,somatosensory evoked potentials and transcranial stimulation of motor tracts

In 15 normal subjects the latency of electrically elicited long-latency reflexes (LLRs) of thenar muscles was compared with somatosensory evoked potentials (SEPs) after median nerve stimulation and with the latencies of thenar muscle potentials after transcranial stimulation (TCS) of the motor cortex. Assuming a transcortical reflex pathway the intracortical relay time for the LLR was calculated to be 10.4±1.9 msec (mean±S.D.) or 8.1 ± 1.6 msec depending on the experimental conditions. The duration of the cortical relay time is not correlated with the peripheral or central conduction times, with body size or arm length. If the LLRs of hand muscles are conducted transcortically the long duration of the cortical relay time suggests a polysynaptic pathway.     

Estimating causal interaction between prefrontal cortex and striatum by transfer entropy

Transfer entropy (TE) is an information-theoretic measure for the investigation of causal interaction between two systems without a requirement of pre-specific interaction model (such as: linear or nonlinear). We introduced an efficient algorithm to calculate TE values between two systems based on observed time signals. By this method, we demonstrated that the TE correctly estimated the coupling strength and the direction of information transmission of two nonlinearly coupled systems. We also calculated TE values of real local field potentials (LFPs) recorded simultaneously in the lateral prefrontal cortex (LPFC) and the striatum of the behavioral monkey, and observed that the TE value from the LPFC to the striatum was stronger than that from the striatum to the LPFC, consistent with anatomical structure between the two areas. Moreover, the TE value dynamically varied dependent on behavior stages of the monkey. These results from simulated and real LFPs data suggested that the TE was able to effectively estimate functional connectivity between different brain regions and characterized their dynamical properties.     

Serial recording of sensory,corticomotor, and brainstem-derived motor evoked potentials in the rat

A method is presented for serial recording of corticomotor evoked potentials (CMEPs), brainstem-derived motor evoked potentials (BMEPs), and somatosensory evoked potentials (SEPs) via permanently implanted cranial screws. One screw was positioned posterior to lambda (posterior screw), and two screws were positioned over the cortical hind limb areas (cortical screws). SEPs were elicited by stimulation of the hind paw and recorded from the contralateral cortex. BMEPs were stimulated via the posterior screw and recorded from both hind limbs, whereas CMEPs were elicited by repeated bipolar stimulation of the cortex and recorded from the contralateral hind limb. BMEPs and CMEPs differed in several points and can be considered as completely separate motor evoked potentials. While BMEPs consisted of a prominent negative peak with short latency (5–7.5 ms), CMEPs were represented by polyphasic signals with long latencies (17–22 ms). The cortical origin of the CMEPs was confirmed by transecting the corticospinal tracts, which abolished the CMEPs but spared the BMEPs. SEPs consisted of three consecutive peaks with mean latencies of the initial peak ranging between 15 and 17 ms. Dorsal column transection also abolished SEPs. In healthy rats, all three signals were recorded for six consecutive weeks. Signal parameters did not change significantly within this observation period. Rats tolerated the screws and the repeated measurements very well and no negative affect on animal behavior was noted. Thus, this method allows serial recording of SEPs, CMEPs, and BMEPs in chronic rat models.     

Serial recording of sensory, corticomotor, and brainstem-derived motor evoked potentials in the rat

A method is presented for serial recording of corticomotor evoked potentials (CMEPs), brainstem-derived motor evoked potentials (BMEPs), and somatosensory evoked potentials (SEPs) via permanently implanted cranial screws. One screw was positioned posterior to lambda (posterior screw), and two screws were positioned over the cortical hind limb areas (cortical screws). SEPs were elicited by stimulation of the hind paw and recorded from the contralateral cortex. BMEPs were stimulated via the posterior screw and recorded from both hind limbs, whereas CMEPs were elicited by repeated bipolar stimulation of the cortex and recorded from the contralateral hind limb. BMEPs and CMEPs differed in several points and can be considered as completely separate motor evoked potentials. While BMEPs consisted of a prominent negative peak with short latency (5-7.5 ms), CMEPs were represented by polyphasic signals with long latencies (17-22 ms). The cortical origin of the CMEPs was confirmed by transecting the corticospinal tracts, which abolished the CMEPs but spared the BMEPs. SEPs consisted of three consecutive peaks with mean latencies of the initial peak ranging between 15 and 17 ms. Dorsal column transection also abolished SEPs. In healthy rats, all three signals were recorded for six consecutive weeks. Signal parameters did not change significantly within this observation period. Rats tolerated the screws and the repeated measurements very well and no negative affect on animal behavior was noted. Thus, this method allows serial recording of SEPs, CMEPs, and BMEPs in chronic rat models.     

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.     

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.     

A role for the cerebellum in the control of limb movement velocity

Accepting, rejecting or modifying the many different theories of the cerebellum's role in the control of movement requires an understanding of the signals encoded in the discharge of cerebellar neurons and how those signals are transformed by the cerebellar circuitry. Particularly challenging is understanding the sensory and motor signals carried by the two types of action potentials generated by cerebellar Purkinje cells, the simple spikes and complex spikes. Advances have been made in understanding this signal processing in the context of voluntary arm movements. Recent evidence suggests that mossy fiber afferents to the cerebellar cortex are a source of kinematic signals, providing information about movement direction and speed. In turn, the simple spike discharge of Purkinje cells integrates this mossy fiber information to generate a movement velocity signal. Complex spikes may signal errors in movement velocity. It is proposed that the cerebellum uses the signals carried by the simple and complex spike discharges to control movement velocity for both step and tracking arm movements.     

Cross-population coupling of neural activity based on Gaussian process current source densities

Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this framework, the set of LFPs result from noise being added to the transform of the CSD by a biophysical forward model, while the CSD is considered to be the sum of a zero-mean, stationary, spatiotemporal Gaussian process, having fast and slow components, and a mean function, which is the sum of multiple time-varying functions distributed across space, each varying across trials. We derived biophysical forward models relevant to the data we analyzed. In simulation studies this approach improved identification of source signals compared to existing CSD estimation methods. Using data recorded from primate auditory cortex, we analyzed trial-to-trial fluctuations in both steady-state and task-evoked signals. We found cortical layer-specific phase coupling between two probes and showed that the same analysis applied directly to LFPs did not recover these patterns. We also found task-evoked CSDs to be correlated across probes, at specific cortical depths. Using data from Neuropixels probes in mouse visual areas, we again found evidence for depth-specific phase coupling of primary visual cortex and lateromedial area based on the CSDs.     

Uncovering interactions in the frequency domain

Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multi-electrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey.     

Distinct cortical circuit mechanisms for complex forelimb movement and motor map topography

Cortical motor maps are the basis of voluntary movement, but they have proven difficult to understand in the context of their underlying neuronal circuits. We applied light-based motor mapping of Channelrhodopsin-2 mice to reveal a functional subdivision of the forelimb motor cortex based on the direction of movement evoked by brief (10?ms) pulses. Prolonged trains of electrical or optogenetic stimulation (100-500?ms) targeted to anterior or posterior subregions of motor cortex evoked reproducible complex movements of the forelimb to distinct positions in space. Blocking excitatory cortical synaptic transmission did not abolish basic motor map topography, but the site-specific expression of complex movements was lost. Our data suggest that the topography of?movement maps arises from their segregated output projections, whereas complex movements evoked by prolonged stimulation require intracortical synaptic transmission.     

The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal

Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygen-level-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single- and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.     

In vivo measurement of cortical impedance spectrum in monkeys: implications for signal propagation

To combine insights obtained from electric field potentials (LFPs) and neuronal spiking activity (MUA) we need a better understanding of the relative spatial summation of these indices of neuronal activity. Compared to MUA, the LFP has greater spatial coherence, resulting in lower spatial specificity and lower stimulus selectivity. A differential propagation of low- and high-frequency electric signals supposedly underlies this phenomenon, which could result from cortical tissue specifically attenuating higher frequencies, i.e., from a frequency-dependent impedance spectrum. Here we directly measure the cortical impedance spectrum in vivo in monkey primary visual cortex. Our results show that impedance is independent of frequency, is homogeneous and tangentially isotropic within gray matter, and can be theoretically predicted assuming a pure-resistive conductor. We propose that the spatial summation of LFP and MUA is determined by the size of these signals' generators and the nature of neural events underlying them, rather than by biophysical properties of gray matter.     

Electrophysiological correlates of rest and activity in Drosophila melanogaster

Extended periods of rest in Drosophila melanogaster resemble mammalian sleep states in that they are characterized by heightened arousal thresholds and specific alterations in gene expression. Defined as inactivity periods spanning 5 or more min, amounts of this sleep-like state are, as in mammals, sensitive to prior amounts of waking activity, time of day, and pharmacological intervention. Clearly recognizable changes in the pattern and amount of brain electrical activity accompany changes in motor activity and arousal thresholds originally used to identify mammalian sleeping behavior. Electroencephalograms (EEGs) and/or local field potentials (LFPs) are now widely used to quantify sleep state amounts and define types of sleep. Thus, slow-wave sleep (SWS) is characterized by EEG spindles and large-amplitude delta-frequency (0-3.5 Hz) waves. Rapid-eye movement (REM) sleep is characterized by irregular gamma-frequency cortical EEG patterns and rhythmic theta-frequency (5-9 Hz) hippocampal EEG activity. It is unknown whether rest and activity in Drosophila are associated with distinct electrophysiological correlates. To address this issue, we monitored motor activity levels and recorded LFPs in the medial brain between the mushroom bodies, structures implicated in the modulation of locomotor activity, of Drosophila. The results indicate that LFPs can be reliably recorded from the brains of awake, moving fruit flies, that targeted genetic manipulations can be used to localize sources of LFP activity, and that brain electrical activity of Drosophila is reliably correlated with activity state.     

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.     

Neuronal activity in the primate supplementary motor area and the primary motor cortex in relation to spatio-temporal bimanual coordination

Single neuronal activity was recorded from the supplementary motor area (SMA-proper and pre-SMA) and primary motor cortex (M1) in two Macaca fascicularis trained to perform a delayed conditional sequence of coordinated bimanual pull and grasp movements. The behavioural paradigm was designed to distinguish neuronal activity associated with bimanual coordination from that related to a comparable motor sequence but executed unimanually (left or right arm only). The bimanual and unimanual trials were instructed in a random order by a visual cue. Following the cue, there was a waiting period until presentation of a "go-signal", signalling the monkey to perform the instructed movement. A total of 143 task-related neurons were recorded from the SMA (SMA-proper, 62; pre-SMA, 81). Most SMA units (87%) were active in both unimanual contralateral and unimanual ipsilateral trials (bilateral neurons), whereas 9% of units were active only in unimanual contralateral trials and 3% were active only in unimanual ipsilateral trials. Forty-eight per cent of SMA task-related units were classified as bimanual, defined as neurons in which the activity observed in bimanual trials could not be predicted from that associated with unimanual trials when comparing the same events related to the same arm. For direct comparison, 527 neurons were recorded from M1 in the same monkeys performing the same tasks. The comparison showed that M1 contains significantly less bilateral neurons (75%) than the SMA, whereas the reverse was observed for contralateral neurons (22% in M1). The proportion of M1 bimanual cells (53%) was not statistically different from that observed in the SMA. The results suggest that both the SMA and M1 may contribute to the control of sequential bimanual coordinated movements. Interlimb coordination may then take place in a distributed network including at least the SMA and M1, but the contribution of other cortical and subcortical areas such as cingulate motor cortex and basal ganglia remains to be investigated.