The economic and humanistic costs of chronic lower back pain in Japan.

BACKGROUND: Few data are available that provide estimates of the economic impact of chronic lower back pain (CLBP) in Japan. The current study estimated the patient burden and the direct and indirect medical costs associated with CLBP in Japan using data from a large cross-sectional patient survey. CLBP was hypothesized to be associated with a considerable burden of illness and a large economic impact. METHODS: Study participants completed the Japan National Health and Wellness Survey in 2014, which included measures of health-related quality of life (HRQoL), work impairment, impairment to daily activities, and healthcare service use. Data from those reporting CLBP (N=392) were contrasted against those from matched controls without back pain, using age and sex-adjusted models. RESULTS: CLBP patients reported significantly lower HRQoL relative to matched controls. Age-and sex-adjusted models estimated mean annual per patient direct and indirect costs attributable to CLBP to be ¥1,820,297 ($15,239 or €12,551) and ¥1,479,899 ($12,389 or €10,203), respectively, with the majority of direct costs related to hospital expenses (¥1,584,759, which is equivalent to $13,267 and €10,927). In estimating the economic impact of CLBP on society, the CLBP respondents were estimated to include 1,508,524 individuals when extrapolated to the Japanese population (815,461 of them employed). Ultimately, this represented approximately ¥1.2 trillion ($10 billion and €8.3 billion) per year in lost productivity at the time of this study. CONCLUSION: This study of patients with CLBP in Japan has shown it to be associated with a significant burden on patients and to have a considerable negative impact on the Japanese economy primarily driven by lost productivity. Further research on the effectiveness of interventions to improve the outcomes of those with CLBP is warranted.

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.

Cortical network architecture for context processing in primate brain.

Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition.

Perception of chasing in squirrel monkeys (Saimiri sciureus).

Understanding the intentions of others is crucial in developing positive social relationships. Comparative human and non-human animal studies have addressed the phylogenetic origin of this ability. However, few studies have explored the importance of motion information in distinguishing others' intentions and goals in non-human primates. This study addressed whether squirrel monkeys (Saimiri sciureus) are able to perceive a goal-directed motion pattern-specifically, chasing-represented by two geometric objects. In Experiment 1, we trained squirrel monkeys to discriminate a "Chasing" sequence from a "Random" sequence. We then confirmed that this discrimination transferred to new stimuli ("Chasing" and "Random") in a probe test. To determine whether the monkeys used similarities of trajectory to discriminate chasing from random motion, we also presented a non-chasing "Clone" sequence in which the trajectories of the two figures were identical. Three of six monkeys were able to discriminate "Chasing" from the other sequences. In Experiment 2, we confirmed humans' recognition of chasing with the stimuli from Experiment 1. In Experiment 3, the three monkeys for which discrimination did not transfer to the new stimuli in Experiment 1 were trained to discriminate between "Chasing" and "Clone" sequences. At testing, all three monkeys had learned to discriminate chasing, and two transferred their learning to new stimuli. Our results suggest that squirrel monkeys use goal-directed motion patterns, rather than simply similarity of trajectory, to discriminate chasing. Further investigation is necessary to identify the motion characteristics that contribute to this discrimination.

Using the reassignment procedure to test object representation in pigeons and people.

In four experiments, we evaluated Lea's (1984) reassignment procedure for studying object representation in pigeons (Experiments 1-3) and humans (Experiment 4). In the initial phase of Experiment 1, pigeons were taught to make discriminative button responses to five views of each of four objects. Using the same set of buttons in the second phase, one view of each object was trained to a different button. In the final phase, the four views that had been withheld in the second stage were shown. In Experiment 2, pigeons were initially trained just like the birds in Experiment 1. Then, one view of each object was reassigned to a different button, now using a new set of four response buttons. In Experiment 3, the reassignment paradigm was again tested using the number of pecks to bind together different views of the same object. Across all three experiments, pigeons showed statistically significant generalization of the new response to the non-reassigned views, but such responding was well below that to the reassigned view. In Experiment 4, human participants were studied using the same stimuli and task as the pigeons in Experiment 1. People did strongly generalize the new response to the non-reassigned views. These results indicate that humans, but not pigeons, can employ a unified object representation that they can flexibly map to different responses under the reassignment procedure.

Study of the neural dynamics for understanding communication in terms of complex hetero systems.

The purpose of the research project was to establish a new research area named "neural information science for communication" by elucidating its neural mechanism. The research was performed in collaboration with applied mathematicians in complex-systems science and experimental researchers in neuroscience. The project included measurements of brain activity during communication with or without languages and analyses performed with the help of extended theories for dynamical systems and stochastic systems. The communication paradigm was extended to the interactions between human and human, human and animal, human and robot, human and materials, and even animal and animal.

Validating the virtual string task with the gap test.

Relational 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.

An artificial network model for estimating the network structure underlying partially observed neuronal signals.

Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed.

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.

Pigeons learn virtual patterned-string problems in a computerized touch screen environment.

For many decades, developmental and comparative psychologists have used a variety of string tasks to assess the perceptual and cognitive capabilities of human children of different ages and different species of nonhuman animals. The most important and widely used of these problems are patterned-string tasks, in which the organism is shown two or more strings, only one of which is connected to a reward. The organism must determine which string is attached to the reward and pull it. We report a new way to implement patterned-string tasks via a computerized touch screen apparatus. Pigeons successfully learned such virtual patterned-string tasks and exhibited the same general performance profile as animals given conventional patterned-string tasks. In addition, variations in the length, separation, and alignment of the strings reliably affected the pigeons' virtual string-pulling behavior. These results not only testify to the power and versatility of our computerized string task, but they also demonstrate that pigeons can concurrently contend with a broad range of demanding patterned-string problems, thereby eliminating many alternative interpretations of their behavior. The virtual patterned-string task may thus permit expanded exploration of other species and variables which would be unlikely to be undertaken either because of inadequacies of conventional methodology or sensorimotor limitations of the studied organisms.

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 partner's 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.

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

Brain­machine 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.

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 monkey's 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 monkey's 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 competitor's 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.

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.

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.

Social state representation in prefrontal cortex.

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.

Amodal completion of moving objects by pigeons.

In a series of four experiments, we explored whether pigeons complete partially occluded moving shapes. Four pigeons were trained to discriminate between a complete moving shape and an incomplete moving shape in a two-alternative forced-choice task. In testing, the birds were presented with a partially occluded moving shape. In experiment 1, none of the pigeons appeared to complete the testing stimulus; instead, they appeared to perceive the testing stimulus as incomplete fragments. However, in experiments 2, 3, and 4, three of the birds appeared to complete the partially occluded moving shapes. These rare positive results suggest that motion may facilitate amodal completion by pigeons, perhaps by enhancing the figure - ground segregation process.

Prior experience affects amodal completion in pigeons.

In a three-alternative forced-choice task, 4 pigeons were trained to discriminate a target stimulus consisting of two colored shapes, one of which partially occluded the other, from two foil stimuli that portrayed either a complete or an incomplete version of the occluded shape. The dependent measure was the percentage of total errors that the birds committed to the complete foil. At the outset of training, the pigeons committed approximately 50% of total errors to the complete foil, but as training progressed, the percentage of errors to the complete foil rose. When the pigeons were given a second exposure to the initial set of stimuli, they committed 70% of total errors to the complete foil, suggesting that they now saw the complete foil as more similar to the occluded target than the incomplete foil. These results suggest that experience with 2-D images may facilitate amodal completion in pigeons, perhaps via perceptual learning.

Perceptual grouping in pigeons.

Animal studies reveal that many species perceive partially occluded objects in the same way as do humans. Pigeons have been a notable exception. We re-investigated this anomaly of pigeon perception using a different approach from previous studies. With our method, we show that pigeons perceive occluded objects in the same manner as do other species. In addition, we report that pigeons can recognize perceptually transparent surfaces when the effect is induced by the same perceptual mechanisms as occlusion. These results give behavioral evidence that the perception of both occlusion and transparency is a common visual function shared by pigeons and humans, despite the structural differences between their visual systems.