Yasuo Nagasaka

Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys.

Brain–machine interfaces (BMIs) employ the electrical activity generated by cortical neurons directly for controlling external devices and have been conceived as a means for restoring human cognitive or sensory-motor functions. The dominant approach in BMI research has been to decode motor variables based on single-unit activity (SUA). Unfortunately, this approach suffers from poor long-term stability and daily recalibration is normally required to maintain reliable performance. A possible alternative is BMIs based on electrocorticograms (ECoGs), which measure population activity and may provide more durable and stable recording. However, the level of long-term stability that ECoG-based decoding can offer remains unclear. Here we propose a novel ECoG-based decoding paradigm and show that we have successfully decoded hand positions and arm joint angles during an asynchronous food-reaching task in monkeys when explicit cues prompting the onset of movement were not required. Performance using our ECoG-based decoder was comparable to existing SUA-based systems while evincing far superior stability and durability. In addition, the same decoder could be used for months without any drift in accuracy or recalibration. These results were achieved by incorporating the spatio-spectro-temporal integration of activity across multiple cortical areas to compensate for the lower fidelity of ECoG signals. These results show the feasibility of high-performance, chronic and versatile ECoG-based neuroprosthetic devices for real-life applications. This new method provides a stable platform for investigating cortical correlates for understanding motor control, sensory perception, and high-level cognitive processes.

Higher Order Partial Least Squares (HOPLS): A Generalized Multilinear Regression Method

A new generalized multilinear regression model, termed the higher order partial least squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) Y from a tensor X through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low-dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both X and Y. Instead of decomposing X and Y individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.

Multidimensional recording (MDR) and data sharing: an ecological open research and educational platform for neuroscience.

Primate neurophysiology has revealed various neural mechanisms at the single-cell level and population level. However, because recording techniques have not been updated for several decades, the types of experimental design that can be applied in the emerging field of social neuroscience are limited, in particular those involving interactions within a realistic social environment. To address these limitations and allow more freedom in experimental design to understand dynamic adaptive neural functions, multidimensional recording (MDR) was developed. MDR obtains behavioral, neural, eye position, and other biological data simultaneously by using integrated multiple recording systems. MDR gives a wide degree of freedom in experimental design because the level of behavioral restraint is adjustable depending on the experimental requirements while still maintaining the signal quality. The biggest advantage of MDR is that it can provide a stable neural signal at higher temporal resolution at the network level from multiple subjects for months, which no other method can provide. Conventional event-related analysis of MDR data shows results consistent with previous findings, whereas new methods of analysis can reveal network mechanisms that could not have been investigated previously. MDR data are now shared in the public server Neurotycho.org. These recording and sharing methods support an ecological system that is open to everyone and will be a valuable and powerful research/educational platform for understanding the dynamic mechanisms of neural networks.

Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques

Brain–machine interface (BMI) technology captures brain signals to enable control of prosthetic or communication devices with the goal of assisting patients who have limited or no ability to perform voluntary movements. Decoding of inherent information in brain signals to interpret the user’s intention is one of main approaches for developing BMI technology. Subdural electrocorticography (sECoG)-based decoding provides good accuracy, but surgical complications are one of the major concerns for this approach to be applied in BMIs. In contrast, epidural electrocorticography (eECoG) is less invasive, thus it is theoretically more suitable for long-term implementation, although it is unclear whether eECoG signals carry sufficient information for decoding natural movements. We successfully decoded continuous three-dimensional hand trajectories from eECoG signals in Japanese macaques. A steady quantity of information of continuous hand movements could be acquired from the decoding system for at least several months, and a decoding model could be used for ∼10 days without significant degradation in accuracy or recalibration. The correlation coefficients between observed and predicted trajectories were lower than those for sECoG-based decoding experiments we previously reported, owing to a greater degree of chewing artifacts in eECoG-based decoding than is found in sECoG-based decoding. As one of the safest invasive recording methods available, eECoG provides an acceptable level of performance. With the ease of replacement and upgrades, eECoG systems could become the first-choice interface for real-life BMI applications.

Social state representation in prefrontal cortex

Abstract One of the cardinal mental faculties of humans and other primates is social brain function, the collective name assigned to the distributed system of social cognitive processes that orchestrate our sophisticated adaptive social behavior. These must include processes for recognizing current social context and maintaining an internal representation of the current social state as a reference for decision-making. But how and where the brain processes such social-state information is unknown. To home in on the neural substrates of social-state representation, the activity of 196 prefrontal (PFC) neurons was recorded from two monkeys simultaneously during a food-grab task under varying social conditions. Of PFC neurons, 39% showed activity modulation during movement-free periods and seemed to be representing current social state. The direction of modulation was opposite between the dominant and submissive monkeys: During social engagement, PFC activity increased in the dominant monkey and was suppressed in the submissive monkey. The modulation was consistently observed in additional PFC neurons (27/72) in additional pairings with two other monkeys. Notably, PFC activity in one formerly submissive monkey switched to dominant modulation mode when he was paired with a new monkey of lower social status. These findings suggest that PFC, as part of a larger social brain network, maintains a multistate classification of social context for use as a behavioral reference for social decision-making.

Spontaneous synchronization of arm motion between Japanese macaques

Humans show spontaneous synchronization of movements during social interactions; this coordination has been shown to facilitate smooth communication. Although human studies exploring spontaneous synchronization are increasing in number, little is known about this phenomenon in other species. In this study, we examined spontaneous behavioural synchronization between monkeys in a laboratory setting. Synchronization was quantified by changes in button-pressing behaviour while pairs of monkeys were facing one another. Synchronization between the monkeys was duly observed and it was participant-partner dependent. Further tests confirmed that the speed of button pressing changed to harmonic or sub-harmonic levels in relation to the partners speed. In addition, the visual information from the partner induced a higher degree of synchronization than auditory information. This study establishes advanced tasks for testing social coordination in monkeys, and illustrates ways in which monkeys coordinate their actions to establish synchronization.

Encoding of social state information by neuronal activities in the macaque caudate nucleus.

Social animals adjust their behavior according to social relationships and momentary circumstances. Dominant–submissive relationships modulate, but do not completely determine, their competitive behaviors. For example, a submissive monkeys decision to retrieve food depends not only on the presence of dominant partners but also on their observed behavior. Thus, behavioral expression requires a dynamic evaluation of reward outcome and momentary social states. The neural mechanisms underlying this evaluation remain elusive. The caudate nucleus (CN) plays a pivotal role in representing reward expectation and translating it into action selection. To investigate whether their activities encode social state information, we recorded from CN neurons in monkeys while they performed a competitive food-grab task against a dominant competitor. We found two groups of CN neurons: one primarily responded to reward outcome, while the other primarily tracked the monkeys social state. These social state-dependent neurons showed greater activity when the monkeys freely retrieved food without active challenges from the competitor and reduced activity when the monkeys were in a submissive state due to the competitors active behavior. These results indicate that different neuronal activities in the CN encode social state information and reward-related information, which may contribute to adjusting competitive behavior in dynamic social contexts.

Validating the virtual string task with the gap test

AbstractRelational concepts—such as connectedness—may be easy for human adults to appreciate; but, obtaining evidence of other species’ understanding of connectedness has been challenging. One key test of connectedness involves an organism’s responding to variants of the string task. Using a virtual string task, we gave pigeons a pair of strings from which to choose: one connected to a full dish of food and a second disconnected from another full dish of food. Our pigeons did not at first choose the connected string under conditions of non-differential reinforcement; later, the birds rapidly learned to choose the connected string under conditions of differential reinforcement. Our results replicate prior findings with real strings and food dishes, thereby demonstrating that pigeons can appreciate the connectedness between a string tether and a dish of food, and attesting to the utility and fidelity of the virtual string task.

A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal

Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform.

Social Suppressive Behavior Is Organized by the Spatiotemporal Integration of Multiple Cortical Regions in the Japanese Macaque

Under social conflict, monkeys develop hierarchical positions through social interactions. Once the hierarchy is established, the dominant monkey dominates the space around itself and the submissive monkey tries not to violate this space. Previous studies have shown the contributions of the frontal and parietal cortices in social suppression, but the contributions of other cortical areas to suppressive functions remain elusive. We recorded neural activity in large cortical areas using electrocorticographic (ECoG) arrays while monkeys performed a social food-grab task in which a target monkey was paired with either a dominant or a submissive monkey. If the paired monkey was dominant, the target monkey avoided taking food in the shared conflict space, but not in other areas. By contrast, when the paired monkey was submissive, the target monkey took the food freely without hesitation. We applied decoding analysis to the ECoG data to see when and which cortical areas contribute to social behavioral suppression. Neural information discriminating the social condition was more evident when the conflict space was set in the area contralateral to the recording hemisphere. We found that the information increased as the social pressure increased during the task. Before food presentation, when the pressure was relatively low, the parietal and somatosensory–motor cortices showed sustained discrimination of the social condition. After food presentation, when the monkey faced greater pressure to make a decision as to whether it should take the food, the prefrontal and visual cortices started to develop buildup responses. The social representation was found in a sustained form in the parietal and somatosensory–motor regions, followed by additional buildup form in the visual and prefrontal cortices. The representation was less influenced by reward expectation. These findings suggest that social adaptation is achieved by a higher-order self-regulation process (incorporating motor preparation/execution processes) in accordance with the embodied social contexts.