Hidenori Watanabe

Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates

Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion.

Reconstruction of movement-related intracortical activity from micro-electrocorticogram array signals in monkey primary motor cortex

Subdural electrode arrays provide stable, less invasive electrocorticogram (ECoG) recordings of neural signals than multichannel needle electrodes. Accurate reconstruction of intracortical local field potentials (LFPs) from ECoG signals would provide a critical step for the development of a less invasive, high-performance brain-machine interface; however, neural signals from individual ECoG channels are generally coarse and have limitations in estimating deep layer LFPs. Here, we developed a high-density, 32-channel, micro-ECoG array and applied a sparse linear regression algorithm to reconstruct the LFPs at various depths of primary motor cortex (M1) in a monkey performing a reach-and-grasp task. At 0.2 mm beneath the cortical surface, the real and estimated LFPs were significantly correlated (correlation coefficient (r); 0.66 ± 0.11), and the r at 3.2 mm was still as high as 0.55 ± 0.04. A time-frequency analysis of the reconstructed LFP showed clear transition between resting and movements by the monkey. These methods would be a powerful tool with wide-ranging applicability in neuroscience studies.

Phase shift of subthreshold theta oscillation in hippocampal CA1 pyramidal cell membrane by excitatory synaptic inputs

Hippocampal CA1 neurons receive multiple rhythmical inputs with relatively independent phases during theta activity. It, however, remains to be determined how these multiple rhythmical inputs affect oscillation properties in membrane potential of the CA1 pyramidal cell. In order to investigate oscillation properties in the subthreshold membrane potential, we generated oscillations in the membrane potential of the CA1 pyramidal cells in rat hippocampal slices in vitro with a sinusoidal current injection into the pyramidal soma at theta band frequencies (4-7 Hz), and analyzed effect of rhythmically excitatory synaptic inputs. The Schaffer collaterals were stimulated with a cyclic Gaussian stimulation method, whose pulse intervals were distributed at 10 pulses/cycle (5 cycles/s). We found that the cyclic Gaussian stimulations induced membrane potential oscillations and their phase delays from the mean of the pulse distribution were dependent on membrane potential oscillation amplitude. We applied four pairs of cyclic Gaussian stimulations and somatic sinusoidal current stimulations at the same frequency (5 Hz) with varying phase differences (-pi/2, 0, pi/2, pi rad). The paired stimulations induced phase distributions of the oscillation in the membrane potential, which showed a dependency on an increasing membrane potential oscillation amplitude response to cyclic Gaussian stimulation. This membrane potential dynamic was exhibited by the mixture of the membrane potential oscillation-amplitude-dependent phase delay and the linear summation of the two sinusoidal waves. These suggest that phases of the membrane potential oscillation are modulated by excitatory synaptic inputs. This phase-modulation by excitatory synaptic inputs may play a crucial role for memory operation in the hippocampus.

Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex.

The relatively low invasiveness of electrocorticography (ECoG) has made it a promising candidate for the development of practical, high-performance neural prosthetics. Recent ECoG-based studies have shown success in decoding hand and finger movements and muscle activity in reaching and grasping tasks. However, decoding of force profiles is still lacking. Here, we demonstrate that lateral grasp force profile can be decoded using a sparse linear regression from 15 and 16 channel ECoG signals recorded from sensorimotor cortex in two non-human primates. The best average correlation coefficients of prediction after 10-fold cross validation were 0.82±0.09 and 0.79±0.15 for our monkeys A and B, respectively. These results show that grasp force profile was successfully decoded from ECoG signals in reaching and grasping tasks and may potentially contribute to the development of more natural control methods for grasping in neural prosthetics.

Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements

Brain–machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-off for the stability, however, the latency (the time gap between the monkeys actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation of a trigger event by decoding muscle activity in order to predict integrated electromyograms (iEMGs) from the ECoGs. We verified the achievability of our method by conducting a performance test of the proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.

Decoding of the spike timing of primary afferents during voluntary arm movements in monkeys

Understanding the mechanisms of encoding forelimb kinematics in the activity of peripheral afferents is essential for developing a somatosensory neuroprosthesis. To investigate whether the spike timing of dorsal root ganglion (DRG) neurons could be estimated from the forelimb kinematics of behaving monkeys, we implanted two multi-electrode arrays chronically in the DRGs at the level of the cervical segments in two monkeys. Neuronal activity during voluntary reach-to-grasp movements were recorded simultaneously with the trajectories of hand/arm movements, which were tracked in three-dimensional space using a motion capture system. Sixteen and 13 neurons, including muscle spindles, skin receptors, and tendon organ afferents, were recorded in the two monkeys, respectively. We were able to reconstruct forelimb joint kinematics from the temporal firing pattern of a subset of DRG neurons using sparse linear regression (SLiR) analysis, suggesting that DRG neuronal ensembles encoded information about joint kinematics. Furthermore, we estimated the spike timing of the DRG neuronal ensembles from joint kinematics using an integrate-and-fire model (IF) incorporating the SLiR algorithm. The temporal change of firing frequency of a subpopulation of neurons was reconstructed precisely from forelimb kinematics using the SLiR. The estimated firing pattern of the DRG neuronal ensembles encoded forelimb joint angles and velocities as precisely as the originally recorded neuronal activity. These results suggest that a simple model can be used to generate an accurate estimate of the spike timing of DRG neuronal ensembles from forelimb joint kinematics, and is useful for designing a proprioceptive decoder in a brain machine interface.

FREQUENCY-DEPENDENT SIGNAL PROCESSING IN APICAL DENDRITES OF HIPPOCAMPAL CA1 PYRAMIDAL CELLS

Depending on an animals behavioral state, hippocampal CA1 pyramidal cells receive distinct patterns of excitatory and inhibitory synaptic inputs. The time-dependent changes in the frequencies of these inputs and the nonuniform distribution of voltage-gated channels lead to dynamic fluctuations in membrane conductance. In this study, using a whole-cell patch-clamp method, we attempted to record and analyze the frequency dependencies of membrane responsiveness in Wistar rat hippocampal CA1 pyramidal cells following noise current injection directly into dendrites and somata under pharmacological blockade of all synaptic inputs. To estimate the frequency-dependent properties of membrane potential, membrane impedance was determined from the voltage response divided by the input current in the frequency domain. The cell membrane of most neurons showed low-pass filtering properties in all regions. In particular, the properties were strongly expressed in the somata or proximal dendrites. Moreover, the data revealed nonuniform distribution of dendritic impedance, which was high in the intermediate segment of the apical dendritic shaft (∼220-260μm from the soma). The low-pass filtering properties in the apical dendrites were more enhanced by membrane depolarization than those in the somata. Coherence spectral analysis revealed high coherence between the input signal and the output voltage response in the theta-gamma frequency range, and large lags emerged in the distal dendrites in the gamma frequency range. Our results suggest that apical dendrites of hippocampal CA1 pyramidal cells integrate synaptic inputs according to the frequency components of the input signal along the dendritic segments receiving the inputs.

Phase locking of β oscillation in electrocorticography (ECoG) in the monkey motor cortex at the onset of EMGs and 3D reaching movements

β oscillations in local field potentials, electrocorticography (ECoG), and electroencephalograms (EEG) are ubiquitous in the motor cortex of monkeys and humans. However, relations between their dynamical properties and behavior have not been well studied. Here, we used ECoG grids to cover large areas of motor cortex and some somatosensory cortex in monkeys while they performed an unconstrained reaching and a lever pulling task in three dimensional space with or without go cue. We used percentage of phase locking (PPL) as a dynamical property of β oscillations aligned to behaviorally relevant events, reaching onsets (RO), lever onset (LO), and holding onset (HO) as well as multiple peaks of electromyography (EMG) recorded from various upper limb muscles. We showed that relative strength of PPL alinged to RO and LO were reserved with or without the external go cues. Furthermore, among the muscles that we recorded EMGs from, β oscillations were not closely phase locked to any of EMG onsets. Therefore, phase locking of β oscillations is related more to the attentive state and external cues as opposed to detailed muscle activities.

Engineering Approach for Functional Recovery Based on Body Image Adjustment by Using Biofeedback of Electrical Stimulation

This chapter reports on a biomedical robotic collaborative approach for neuroprosthesis based on body image adjustment. The body image and homunculus show a stable relationship between the brain and a sensor and the motor allocation of the human body. The body schema conceptually explains the relationship between the brain and the body movement. In recent times, a novel concept of functional recovery of motion based on biofeedback to connect the intentions of motion and the sensory input has attracted considerable attention. This chapter describes adaptable EMG prosthetic hand experiments that show that the sensory motor cortex indicates the human intentions of motion through synchronized proprioceptive sensor inputs. This illusion induces strange activities in the sensory motor area according to the synchronous biofeedback. Biofeedback using an interference-driven electrical stimulation (ES) device is proposed, and the experimental results show that the somatic reflex stimulation realizes muscular control and neural rehabilitation in patients with sensor–motor coordination disruption. Furthermore, the proposed device can be applied for the rehabilitation of paralysis due to stroke; it has functions for changing the stimulation parameters and controlling many channels in order to adapt to various types of paralysis and to support complex movements such as grasping, standing, and walking. For neuroprosthesis applications, the desired relationship between the stimulation and intention of motion is synchronous and can be controlled by using an electrical switch to control the ES.

Reconstruction of intracortical whisker-evoked local field potential from electrocorticogram using a model trained for spontaneous activity in the rat barrel cortex

Electrocorticogram (ECoG) has provided neural information from the cortical surfaces, is widely used in clinical applications, and expected to be useful for brain-machine interfaces. Recent studies have defined the relationship between neural activity in deep layers of the cerebral cortex and ECoG. However, it is still unclear whether this relationship is shared across different brain states. In this study, spontaneous activity and whisker-evoked responses in the barrel cortex of anesthetized rats were recorded with a 32-channel ECoG electrode array and 32-channel linear silicon probe electrodes, respectively. Spontaneous local field potentials (LFPs) at various depths could be reconstructed with high accuracy (R>0.9) by a linear weighted summation of spontaneous ECoG. Current source density analysis revealed that the reconstructed LFPs correctly represented laminar profiles of current sinks and sources as well as the raw LFP. Moreover, when we applied the spontaneous activity model to reconstruction of LFP from the whisker-related ECoG, high accuracy of reconstruction could be obtained (R>0.9). Our results suggest that the ECoG carried rich information about synaptic currents in the deep layers of the cortex, and the same reconstruction model can be applied to estimate both spontaneous activity and whisker-evoked responses.