Faunal activity rhythms influencing early community succession of an implanted whale carcass offshore Sagami Bay, Japan.

Benthic community succession patterns at whale falls have been previously established by means of punctual submersible and ROV observations. The contribution of faunal activity rhythms in response to internal tides and photoperiod cues to that community succession dynamism has never been evaluated. Here, we present results from a high-frequency monitoring experiment of an implanted sperm whale carcass in the continental slope (500 m depth) offshore Sagami Bay, Japan. The benthic community succession was monitored at a high frequency in a prolonged fashion (i.e. 2-h intervals for 2.5 months) with a seafloor lander equipped with a time-lapse video camera and an acoustic Doppler profiler to concomitantly study current flow dynamics. We reported here for the first time, to the best of our knowledge, the occurrence of strong 24-h day-night driven behavioral rhythms of the most abundant species (Simenchelys parasitica; Macrocheira kaempferi, and Pterothrissus gissu). Those rhythms were detected in detriment of tidally-controlled ones. Evidence of a diel temporal niche portioning between scavengers and predators avoiding co-occurrence at the carcass, is also provided. The high-frequency photographic and oceanographic data acquisition also helped to precisely discriminate the transition timing between the successional stages previously described for whale falls' attendant communities.

Two is better than one: physical interactions improve motor performance in humans.

How do physical interactions with others change our own motor behavior? Utilizing a novel motor learning paradigm in which the hands of two - individuals are physically connected without their conscious awareness, we investigated how the interaction forces from a partner adapt the motor behavior in physically interacting humans. We observed the motor adaptations during physical interactions to be mutually beneficial such that both the worse and better of the interacting partners improve motor performance during and after interactive practice. We show that these benefits cannot be explained by multi-sensory integration by an individual, but require physical interaction with a reactive partner. Furthermore, the benefits are determined by both the interacting partner's performance and similarity of the partner's behavior to one's own. Our results demonstrate the fundamental neural processes underlying human physical interactions and suggest advantages of interactive paradigms for sport-training and physical rehabilitation.

Downregulation of Ral GTPase-activating protein promotes tumor invasion and metastasis of bladder cancer.

The small GTPase Ral is known to be highly activated in several human cancers, such as bladder, colon and pancreas cancers. It is reported that activated Ral is involved in cell proliferation, migration and metastasis of bladder cancer. This protein is activated by Ral guanine nucleotide exchange factors (RalGEFs) and inactivated by Ral GTPase-activating proteins (RalGAPs), the latter of which consist of heterodimers containing a catalytic α1 or α2 subunit and a common ß subunit. In Ras-driven cancers, such as pancreas and colon cancers, constitutively active Ras mutant activates Ral through interaction with RalGEFs, which contain the Ras association domain. However, little is known with regard to the mechanism that governs aberrant activation of Ral in bladder cancer, in which Ras mutations are relatively infrequent. Here, we show that Ral was highly activated in invasive bladder cancer cells due to reduced expression of RalGAPα2, the dominant catalytic subunit in bladder, rather than increased expression of RalGEFs. Exogenous expression of wild-type RalGAPα2 in KU7 bladder cancer cells with invasive phenotype, but not mutant RalGAPα2-N1742K lacking RalGAP activity, resulted in attenuated cell migration in vitro and lung metastasis in vivo. Furthermore, genetic ablation of Ralgapa2 promoted tumor invasion in a chemically-induced murine bladder cancer model. Importantly, immunohistochemical analysis of human bladder cancer specimens revealed that lower expression of RalGAPα2 was associated with advanced clinical stage and poor survival of patients. Collectively, these results are highly indicative that attenuated expression of RalGAPα2 leads to disease progression of bladder cancer through enhancement of Ral activity.

Model-based attenuation of movement artifacts in fMRI.

Behavioral analysis of multi-joint arm reaching has allowed important advances in understanding the control of voluntary movements. Complementing this analysis with functional magnetic resonance imaging (fMRI) would give insight into the neural mechanisms behind this control. However, fMRI is very sensitive to artifacts created by head motion and magnetic field deformation caused by the moving limbs. It is thus necessary to attenuate these motion artifacts in order to obtain correct activation patterns. Most algorithms in literature were designed for slow changes of head position over several brain scans and are not very effective on data when the movement is of duration below the resolution of a brain scan. This paper introduces a simple model-based method to remove motion artifacts during short duration movements. The proposed algorithm can account for head movement and field deformations due to movements within and outside of the scanner's field of view. It uses information from the experimental design and subject kinematics to focus the artifact attenuation in time and space and minimize the loss of uncorrupted data. Applications of the algorithm on arm reaching experimental data obtained with blocked and event-related designs demonstrate attenuation of motion artifacts with minimal effect on the brain activations.

Motor memory and local minimization of error and effort, not global optimization, determine motor behavior.

Many real life tasks that require impedance control to minimize motion error are characterized by multiple solutions where the task can be performed either by co-contracting muscle groups, which requires a large effort, or, conversely, by relaxing muscles. However, human motor optimization studies have focused on tasks that are always satisfied by increasing impedance and that are characterized by a single error-effort optimum. To investigate motor optimization in the presence of multiple solutions and hence optima, we introduce a novel paradigm that enables us to let subjects repetitively (but inconspicuously) use different solutions and observe how exploration of multiple solutions affect their motor behavior. The results show that the behavior is largely influenced by motor memory with subjects tending to involuntarily repeat a recent suboptimal task-satisfying solution even after sufficient experience of the optimal solution. This suggests that the CNS does not optimize co-activation tasks globally but determines the motor behavior in a tradeoff of motor memory, error, and effort minimization.

Sparse linear regression for reconstructing muscle activity from human cortical fMRI.

In humans, it is generally not possible to use invasive techniques in order to identify brain activity corresponding to activity of individual muscles. Further, it is believed that the spatial resolution of non-invasive brain imaging modalities is not sufficient to isolate neural activity related to individual muscles. However, this study shows that it is possible to reconstruct muscle activity from functional magnetic resonance imaging (fMRI). We simultaneously recorded surface electromyography (EMG) from two antagonist muscles and motor cortices activity using fMRI, during an isometric task requiring both reciprocal activation and co-activation of the wrist muscles. Bayesian sparse regression was used to identify the parameters of a linear mapping from the fMRI activity in areas 4 (M1) and 6 (pre-motor, SMA) to EMG, and to reconstruct muscle activity in an independent test data set. The mapping obtained by the sparse regression algorithm showed significantly better generalization than those obtained from algorithms commonly used in decoding, i.e., support vector machine and least square regression. The two voxel sets corresponding to the activity of the antagonist muscles were intermingled but disjoint. They were distributed over a wide area of pre-motor cortex and M1 and not limited to regions generally associated with wrist control. These results show that brain activity measured by fMRI in humans can be used to predict individual muscle activity through Bayesian linear models, and that our algorithm provides a novel and non-invasive tool to investigate the brain mechanisms involved in motor control and learning in humans.

Impedance control is tuned to multiple directions of movement.

Humans are able to learn tool-handling tasks, such as carving, demonstrating their competency to make and vary the direction of movements in unstable environments. It has been shown that when a single reaching movement is repeated in unstable dynamics, the central nervous system (CNS) learns an impedance internal model to compensate for the environment instability. However, there is still no explanation for how humans can learn to move in various directions in such environments. In this study, we investigated whether and how humans compensate for instability while learning two different reaching movements simultaneously. Results show that when performing movements in two different directions, separated by a 35 degrees angle, the CNS was able to compensate for the unstable dynamics. After adaptation, the force was found to be similar to the free movement condition, but stiffness increased in the direction of instability, specifically for each direction of movement. Our findings suggest that the CNS either learned an internal model generalizing over different movements, or alternatively that it was able to switch between specific models acquired simultaneously.

Accurate real-time feedback of surface EMG during fMRI.

Real-time acquisition of EMG during functional MRI (fMRI) provides a novel method of controlling motor experiments in the scanner using feedback of EMG. Because of the redundancy in the human muscle system, this is not possible from recordings of joint torque and kinematics alone, because these provide no information about individual muscle activation. This is particularly critical during brain imaging because brain activations are not only related to joint torques and kinematics but are also related to individual muscle activation. However, EMG collected during imaging is corrupted by large artifacts induced by the varying magnetic fields and radio frequency (RF) pulses in the scanner. Methods proposed in literature for artifact removal are complex, computationally expensive, and difficult to implement for real-time noise removal. We describe an acquisition system and algorithm that enables real-time acquisition for the first time. The algorithm removes particular frequencies from the EMG spectrum in which the noise is concentrated. Although this decreases the power content of the EMG, this method provides excellent estimates of EMG with good resolution. Comparisons show that the cleaned EMG obtained with the algorithm is, like actual EMG, very well correlated with joint torque and can thus be used for real-time visual feedback during functional studies.

Stability and motor adaptation in human arm movements.

In control, stability captures the reproducibility of motions and the robustness to environmental and internal perturbations. This paper examines how stability can be evaluated in human movements, and possible mechanisms by which humans ensure stability. First, a measure of stability is introduced, which is simple to apply to human movements and corresponds to Lyapunov exponents. Its application to real data shows that it is able to distinguish effectively between stable and unstable dynamics. A computational model is then used to investigate stability in human arm movements, which takes into account motor output variability and computes the force to perform a task according to an inverse dynamics model. Simulation results suggest that even a large time delay does not affect movement stability as long as the reflex feedback is small relative to muscle elasticity. Simulations are also used to demonstrate that existing learning schemes, using a monotonic antisymmetric update law, cannot compensate for unstable dynamics. An impedance compensation algorithm is introduced to learn unstable dynamics, which produces similar adaptation responses to those found in experiments.

How are internal models of unstable tasks formed?

The results of recent studies suggest that humans can form internal models that they use in a feedforward manner to compensate for both stable and unstable dynamics. To examine how internal models are formed, we performed adaptation experiments in novel dynamics, and measured the endpoint force, trajectory and EMG during learning. Analysis of reflex feedback and change of feedforward commands between consecutive trials suggested a unified model of motor learning, which can coherently unify the learning processes observed in stable and unstable dynamics and reproduce available data on motor learning. To our knowledge, this algorithm, based on the concurrent minimization of (reflex) feedback and muscle activation, is also the first nonlinear adaptive controller able to stabilize unstable dynamics.

The central nervous system stabilizes unstable dynamics by learning optimal impedance.

To manipulate objects or to use tools we must compensate for any forces arising from interaction with the physical environment. Recent studies indicate that this compensation is achieved by learning an internal model of the dynamics, that is, a neural representation of the relation between motor command and movement. In these studies interaction with the physical environment was stable, but many common tasks are intrinsically unstable. For example, keeping a screwdriver in the slot of a screw is unstable because excessive force parallel to the slot can cause the screwdriver to slip and because misdirected force can cause loss of contact between the screwdriver and the screw. Stability may be dependent on the control of mechanical impedance in the human arm because mechanical impedance can generate forces which resist destabilizing motion. Here we examined arm movements in an unstable dynamic environment created by a robotic interface. Our results show that humans learn to stabilize unstable dynamics using the skillful and energy-efficient strategy of selective control of impedance geometry.

Change in neuronal firing patterns in the process of motor command generation for the ocular following response.

To explore the process of motor command generation for the ocular following response, we recorded the activity of single neurons in the medial superior temporal (MST) area of the cortex, the dorsolateral pontine nucleus (DLPN), and the ventral paraflocculus (VPFL) of the cerebellum of alert monkeys during ocular following elicited by sudden movements of a large-field pattern. Using second-order linear-regression models, we analyzed the quantitative relationships between neuronal firing frequency patterns and eye movements or retinal errors specified by three parameters (position, velocity, and acceleration). We first attempted to reconstruct the temporal waveform of each neuronal response to each visual stimulus and computed the coefficients for each parameter using the least-square error method for each stimulus condition. The temporal firing patterns were generally well reconstructed [coefficient of determination index (CD) > 0.7] from either the retinal error or the associated ocular following response. In the MST and DLPN datasets, however, the fit with the retinal error model was generally better than with the eye-movement model, and the estimated coefficients of acceleration and velocity ranged widely, indicating that temporal patterns in these regions showed considerable diversity. The acceleration component is greater in MST and DLPN than in VPFL, suggesting that an integration occurs in this pathway. When we determined how well the temporal patterns of the neuronal responses of a given cell could be reconstructed for all visual stimuli using a single set of coefficients, good fits were found only for Purkinje cells (P- cells) in the VPFL using the eye-movement model. In these cases, the coefficients of acceleration and velocity for each cell were similar, and the mean ratio of the acceleration and velocity coefficients was close to that of motor neurons. These results indicate that individual MST and DLPN neurons are each encoding some selective aspects of the sensory stimulus (visual motion), whereas the P-cells in VPFL are encoding the complete dynamic command signals for the associated motor response (ocular following). We conclude that the sensory-to-motor transformation for the ocular following response occurs at the P-cells in VPFL.

Mosaic model for sensorimotor learning and control.

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

Exploration of signal transduction pathways in cerebellar long-term depression by kinetic simulation.

Because multiple molecular signal transduction pathways regulate cerebellar long-term depression (LTD), which is thought to be a possible molecular and cellular basis of cerebellar learning, the systematic relationship between cerebellar LTD and the currently known signal transduction pathways remains obscure. To address this issue, we built a new diagram of signal transduction pathways and developed a computational model of kinetic simulation for the phosphorylation of AMPA receptors, known as a key step for expressing cerebellar LTD. The phosphorylation of AMPA receptors in this model consists of an initial phase and an intermediate phase. We show that the initial phase is mediated by the activation of linear cascades of protein kinase C (PKC), whereas the intermediate phase is mediated by a mitogen-activated protein (MAP) kinase-dependent positive feedback loop pathway that is responsible for the transition from the transient phosphorylation of the AMPA receptors to the stable phosphorylation of the AMPA receptors. These phases are dually regulated by the PKC and protein phosphatase pathways. Both phases also require nitric oxide (NO), although NO per se does not show any ability to induce LTD; this is consistent with a permissive role as reported experimentally (Lev-Ram et al., 1997). Therefore, the kinetic simulation is a powerful tool for understanding and exploring the behaviors of complex signal transduction pathways involved in cerebellar LTD.

Quantitative examinations for multi joint arm trajectory planning--using a robust calculation algorithm of the minimum commanded torque change trajectory.

In previous research, criteria based on optimal theories were examined to explain trajectory features in time and space in multi joint arm movement. Four criteria have been proposed. They were the minimum hand jerk criterion (by which a trajectory is planned in an extrinsic-kinematic space), the minimum angle jerk criterion (which is planned in an intrinsic-kinematic space), the minimum torque change criterion (where control objects are joint links; it is planned in an intrinsic-dynamic-mechanical space), and the minimum commanded torque change criterion (which is planned in an intrinsic space considering the arm and muscle dynamics). Which of these is proper as a criterion for trajectory planning in the central nervous system has been investigated by comparing predicted trajectories based on these criteria with previously measured trajectories. Optimal trajectories based on the two former criteria can be calculated analytically. In contrast, optimal trajectories based on the minimum commanded torque change criterion are difficult to be calculated, even with numerical methods. In some cases, they can be computed by a Newton-like method or a steepest descent method combined with a penalty method. However, for a realistic physical parameter range, the former becomes unstable quite often and the latter is unreliable about the optimality of the obtained solution. In this paper, we propose a new method to stably calculate optimal trajectories based on the minimum commanded torque change criterion. The method can obtain trajectories satisfying Euler-Poisson equations with a sufficiently high accuracy. In the method, a joint angle trajectory, which satisfies the boundary conditions strictly, is expressed by using orthogonal polynomials. The coefficients of the orthogonal polynomials are estimated by using a linear iterative calculation so as to satisfy the Euler-Poisson equations with a sufficiently high accuracy. In numerical experiments, we show that the optimal solution can be computed in a wide work space and can also be obtained in a short time compared with the previous methods. Finally, we perform supplementary examinations of the experiments by Nakano, Imamizu, Osu, Uno, Gomi, Yoshioka et al. (1999). Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biological Cybernetics, 73, 291-300. Their experiments showed that the measured trajectory is the closest to the minimum commanded torque change trajectory by statistical examination of many point-to-point trajectories over a wide range in a horizontal and sagittal work space. We recalculated the minimum commanded torque change trajectory using the proposed method, and performed the same examinations as previous investigations. As a result, it could be reconfirmed that the measured trajectory is closest to the minimum commanded torque change trajectory previously reported.

Purkinje cell activity in the middle zone of the cerebellar flocculus during optokinetic and vestibular eye movement in cats.

Based on the inverse dynamics theory, a previous paper reconstructed simple-spike (SS) firing rates of Purkinje cells in the cat's flocculus middle-zone by a linear-weighted summation of eye acceleration, velocity, and position during optokinetic response (OKR). The present study investigated the SS rates during combined optokinetic and vestibular stimuli of the cells recorded in the previous paper. During the sinusoidal vestibuloocular reflex (VOR) in the light (VORL) and in the dark (VORD) the firing modulation was small. During VOR suppression (VORS) by head and visual-pattern rotation in the same direction, the modulation was deep, with the peak coinciding roughly with peak ipsiversive head velocity. During VOR enhancement (VORE), the modulation was deep, with the peak coinciding roughly with peak contraversive head velocity. If we interpret these data in relation to eye and head movements, the cells in the cat were comparable to the horizontal-gaze-velocity Purkinje cells in the monkey that encode a linear summation of eye and head velocity signals. Alternatively, if we interpret the data on the basis of the inverse dynamics theory, the SS rates during VORL, VORS, and VORE were well-fitted by the OKR components of the movements (subtraction of VORD from VORL, VORS, and VORE eye movements, respectively), but not by the whole movements, using the coefficients calculated during OKR. It is concluded that the data are interpretable by both theories when the VOR gain (eye movement/head movement) is close to 1 and the firing is dominated by eye velocity information.

A method for measuring endpoint stiffness during multi-joint arm movements.

Current methods for measuring stiffness during human arm movements are either limited to one-joint motions, or lead to systematic errors. The technique presented here enables a simple, accurate and unbiased measurement of endpoint stiffness during multi-joint movements. Using a computer-controlled mechanical interface, the hand is displaced relative to a prediction of the undisturbed trajectory. Stiffness is then computed as the ratio of restoring force to displacement amplitude. Because of the accuracy of the prediction (< 1 cm error after 200 ms) and the quality of the implementation, the movement is not disrupted by the perturbation. This technique requires only 13 as many trials to identify stiffness as the method of Gomi and Kawato (1997, Biological Cybernetics 76, 163-171) and may, therefore, be used to investigate the evolution of stiffness during motor adaptation.

Motor dynamics encoding in the rostral zone of the cat cerebellar flocculus during vertical optokinetic eye movements.

The complex spike (CS) and simple spike (SS) activities of Purkinje cells in the rostral zone of the cerebellar flocculus were recorded in alert cats during optokinetic responses (OKR) elicited by a stimulus sequence consisting of a constant-speed visual pattern movement in one direction for 1 s and then in the opposite direction for 1 s. The quick-phase-free trials were selected. Ninety-eight cells were identified as rostral zone cells by the direction-selective CS activity that was modulated during vertical but not horizontal stimuli. In most of the majority population (88 cells), with an increasing CS firing rate during upward OKR and an increasing SS rate during downward OKR, the inverse dynamics approach was successful and the time course of the SS rate was reconstructed (mean coefficient of determination, 0.70 and 0.72 during upward and downward stimuli, respectively) by a linear weighted superposition of the eye acceleration, velocity, position, and constant terms, at a given time delay (mean 10 ms) from the unit response to the eye-movement response. Standard regression coefficient (SRC) analysis revealed that the contribution of the velocity term (mean SRC 0.98 for upward and 0.80 for downward) to regression was dominant over acceleration (mean SRC 0.018 and 0.058) and position (-0.14 and -0.12) terms. The velocity coefficient during upward stimuli (6.6 spikes/s per degree/s) was significantly (P<0.01) larger than that during downward stimuli (4.9 spikes/s per degree/s). In most of the minority population (10 cells), with both CS and SS firing rates increasing during upward OKR, the inverse dynamics approach was not successful. It is concluded that 1) in the cat rostral zone Purkinje cells, in which the preferred direction is upward for CS and downward for SS, eye velocity and acceleration information is encoded in SS firing to counteract the viscosity and inertia forces, respectively, on the eye during vertical OKR; 2) the eye position information encoded in SS firing is inappropriate for counteracting the elastic force; 3) encoding of eye velocity information during upward OKR is quantitatively different from that during downward OKR: SS firing modulation is larger for upward than for downward OKR of the same amplitude; and 4) encoding of motor dynamics is obscure in cells in which the preferred direction is upward for both CS and SS.

Human cerebellar activity reflecting an acquired internal model of a new tool.

Theories of motor control postulate that the brain uses internal models of the body to control movements accurately. Internal models are neural representations of how, for instance, the arm would respond to a neural command, given its current position and velocity. Previous studies have shown that the cerebellar cortex can acquire internal models through motor learning. Because the human cerebellum is involved in higher cognitive function as well as in motor control, we propose a coherent computational theory in which the phylogenetically newer part of the cerebellum similarly acquires internal models of objects in the external world. While human subjects learned to use a new tool (a computer mouse with a novel rotational transformation), cerebellar activity was measured by functional magnetic resonance imaging. As predicted by our theory, two types of activity were observed. One was spread over wide areas of the cerebellum and was precisely proportional to the error signal that guides the acquisition of internal models during learning. The other was confined to the area near the posterior superior fissure and remained even after learning, when the error levels had been equalized, thus probably reflecting an acquired internal model of the new tool.

A mathematical analysis of the characteristics of the system connecting the cerebellar ventral paraflocculus and extraoculomotor nucleus of alert monkeys during upward ocular following responses.

Movements of the visual scene evoke short-latency ocular-following-responses (OFR). Many studies suggest that a neural pathway containing the cerebellar-ventral-paraflocculus (VPFL) mediates OFR. The relationship between eye movement and simple-spike firing in the VPFL during OFR has been studied in detail using an inverse dynamics approach. The relationship between eye movement and cell firing in the extraoculomotor nucleus (MN) has already been reported. However, no studies have examined the information transformation that occurs between the VPFL and the MN during OFR. In this paper, using an inverse dynamics approach, we derive a transfer function that represents the characteristics of the structure connecting the VPFL and the MN during upward OFR. This structure appears to contain a kind of neural integrator, which constructs eye-velocity-and-position information from eye-acceleration-and-velocity information. We propose a diagram for the neural integration commonly at work during all types of upward eye movement. This is a closed-loop circuit containing a low-pass filter. The low-pass filter can construct eye-velocity-and-position information from an eye-acceleration-velocity-position command similar to the final motor command used commonly for all upward eye movements. Anatomical and electrophysiological data suggest that the vestibular nuclei-interstitial nucleus of Cajal-vestibular nuclei loop might perform such neural integration.