Accepted Manuscript Title: Body representation in the brain Author: Eiichi Naito Jun Ota Akira Murata PII: DOI: Reference:
S0168-0102(15)00305-3 http://dx.doi.org/doi:10.1016/j.neures.2015.12.014 NSR 3915
To appear in:
Neuroscience Research
Received date: Revised date: Accepted date:
25-11-2015 4-12-2015 24-12-2015
Please cite this article as: Naito, E., Ota, J., Murata, A.,Body representation in the brain, Neuroscience Research (2016), http://dx.doi.org/10.1016/j.neures.2015.12.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Manuscript
Editorial Title: Body representation in the brain Authors: Eiichi Naito1,2, Jun Ota3, Akira Murata4
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Affiliations: 1 Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), 2A6 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan 2 Graduate School of Medicine and Graduate School of Frontier Biosciences, Osaka University, 2-15 Yamadaoka, Suita, Osaka 565-0871, Japan Tel: +81-80-9098-3256 Fax: +81-6-7174-8612 Email: [email protected] 3
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Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, 5-1-5 Kashiwa, Chiba, 277-8568, Japan Tel: +81-4-7136-4252 Fax: +81-4-7136-4242 Email: [email protected] 4
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Department of Physiology, Kinki University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, 589-8511, Japan Tel: +81-72-366-0221 Fax: +81-72-366-0206 Email: [email protected]
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As the Japanese society ages rapidly, we are experiencing a sharp increase in the number of patients with motor or cognitive dysfunctions resulting from brain stroke or neurodegenerative disease. Thus, developing effective rehabilitative techniques to overcome these problems is of paramount importance. To perform motor actions, the brain should have a model of the body (structure, size, weight, posture or motor properties) and monitor “online” the state of the body as this changes during movement so that the brain can further update the model. We call this model the “body representation” in the brain, and our hypothesis is that dysfunctions in the body representation may commonly underlie the motor and cognitive dysfunctions. Hence, to overcome such problems, it is essential to further advance our knowledge about the neural entity of the body representation and to develop effective intervention techniques targeting this internal representation. In this special issue, the term “body representation” includes both the body image and body schema (c.f. Gallagher 2005). In general, the body image is heavily associated with awareness and knowledge of one’s own body, whereas the body schema is thought to be a pre-conscious plastic schema of the body that is utilized in motor control. Thus, the body representation we defined in this special issue covers a broader range of internal body representations that are deeply related to body cognition and motor control. To enrich our understanding of body representations so that we may develop effective rehabilitative techniques, it is very important to cultivate interdisciplinary approaches by combining brain science, rehabilitation medicine and systems engineering. Prompted by this belief, we have recently launched the embodied-brain program, which is one of the interdisciplinary programs from the Japan Society for the Promotion Science, Grant-in-Aid for Scientific Research on Innovative Areas. We thereby intend to establish a new academic discipline that is known as embodied-brain systems science. In this program, we conduct experiments with humans and animals to understand the neural mechanisms of body representations that mediate body cognition (sense of agency, sense of ownership) and motor control (muscle synergy control, anticipatory postural control). In particular, we aim to reveal the neural substrates underlying the long-term changes in body representations and to develop rehabilitative interventions based on the modeling of these findings. In this special issue, we have accumulated what we have learned so far about body representations in the brain and what actions we must take in the future to better understand brain plasticity and body representations to promote their adaptive functions. This special issue covers a wide range of important topics. We first present papers related to body cognition and then introduce papers related to motor control.
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Electrophysiological investigation in animals is a core technique for understanding neuronal representations in the brain, and it has been one of the primary techniques used in systems neuroscience. In this respect, Murata et al. (2016) provide an excellent review paper that examines the essential contributions of the dorsal-ventral streams of the parieto-premotor networks to forming internal body representations. In this paper, the authors revalidate the importance of the anterior intraparietal area (AIP) and ventral premotor cortex (F5) for the motor control of the hand and object manipulation. Furthermore, the authors provide new evidence that some classes of parietal neurons encode either one’s own actions or those of others, whereas other classes of parietal neurons encode both. The latter is consistent with the classical concept of mirror neurons in which a map of one’s own body parts can be used as a reference to perceive the body parts of others, whereas the former can be used for self-other distinctions and for distinguishing the agency of action. Because these types of neurons are found within the mirror neuron system, the parieto-premotor network that includes the mirror neuron system is key to controlling one’s own actions and to mapping one’s own body and those of others, allowing recognition of action contribution, i.e., sense of agency. The importance of the dorsal-ventral stream of the parieto-premotor network is corroborated in the human brain (Naito et al. 2016), and it is highly likely that the network is supported by the large-scale brain fibers of the inferior branch of superior longitudinal fasciculus (SLF III). In this review, the authors mainly focus on how proprioceptive (kinesthetic) information contributes to the formation of body representations in humans by introducing neuroimaging evidence obtained from their unique approach of vibration-induced proprioceptive illusions. They discuss the possibility that a motor network centered in the primary motor cortex (M1) processes kinesthetic information and constructs kinematic and dynamic postural models of a limb (body schema) that enables fast online feedback control. In addition, the authors show that kinesthetic illusory awareness is an attribute of the neural activity in the right SLF III network, and some of these neuronal substrates are shared for self-identification (body image). They also introduce the concept of functional lateralization in the human brain. Aymerich-Franch and Ganesh (2016) provide a novel framework to better understand the computational mechanisms of body representations for self-attribution (sense of ownership). The article summarizes previous experimental findings on the embodiment of artificial limbs (e.g., rubber hand illusion), the entire body, and a virtual avatar. The authors also provide further explanations for these results based on the “functional body model hypothesis.” They propose a new concept of functionality in the body model in which our brain attributes a perceived entity as our own limb
3 Page 3 of 8
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(body) if the physical properties of the entity are sufficient to afford certain actions the brain has associated with that limb (body). They also provide their view that the body model would be represented by the fronto-parietal network, specifically through interactions between the inferior parietal and occipital regions and between the premotor and somatosensory areas via intraparietal areas, which generally fits well with the views from Murata et al. (2016) and Naito et al. (2016). Developing effective rehabilitative techniques requires a better understanding of dysfunction in the human brain. In this respect, Oouchida et al. (2016) attempt to explain maladaptive neuronal processes underlying phantom limb experience in terms of use-dependent plasticity. Computational modeling greatly facilitates understanding and prediction of changes in the body representation in the brain. Izawa et al. (2016) focus on the impaired inference mechanism of self-agency in the brains of individuals with schizophrenia, in which the sensory outcome generated by self-initiated action is misattributed to the actions of another agent. The authors have developed a novel computational model of agency experience using a Bayesian decision-making framework, and they unite the computational mechanisms of agency and motor control via an internal model based on optimal feedback control using Kalman filtering. The model seems to be able to successfully predict several of the motor abnormalities observed in schizophrenia by assuming a deformed internal model. To discuss the plausibility of their model’s predictions, they review reports in the literature that could support their predictions and suggest experiments that would potentially examine the proposed model of schizophrenia. Recent studies in cognitive neuroscience have noted the importance of fast and slow neural processes in body cognition. Yano et al. (2016) review the mathematical basis for understanding fast-slow dynamical systems. They also review the basis of Bayesian statistics and propose a fast-slow perspective on Bayesian statistics. This review may be helpful for advancing the Bayesian perspectives in cognitive neuroscience. One of the higher brain functions that links cognition and motor control is motor imagery, wherein individuals mentally simulate body movements without overtly generating movement. This function results from a forward model generated by the brain that transforms a motor command into a prediction of the sensory consequences that would result from the movement. Motor imagery is also applicable for neurorehabilitation. Hanakawa (2016) reviews a wide range of reports and nicely characterizes motor imagery in terms of motor control, sensory modalities, agency and explicitness. In addition, he introduces cutting-edge research on neurofeedback training with motor imagery.
4 Page 4 of 8
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As described above, another important topic in this special issue is motor control. In the second half of this special issue, we introduce papers related to motor control, especially in terms of muscle synergy control and anticipatory postural control. It is well established that M1 is an executive locus of voluntary movement. However, exactly how M1 controls body movements remains an unresolved problem. Electrophysiological findings support both kinematic and dynamic coding hypotheses. Tanaka (2016) nicely summarizes several M1 models in terms of optimality, recurrent neural networks and spatial dynamics. In particular, he proposes a new M1 model that includes cross products, which can better explain the characteristics of neural responses and the experimental results of kinematical and dynamical adaptation. M1 forms closed loop circuits with the cerebellum, and the cerebro-cerebellar interactions, in general, likely play essential roles not only in motor control but also in non-motor higher brain functions. Ishikawa et al. (2016) provide physiological and morphologic evidences suggesting the existence of a forward model for limb movement in the cerebro-cerebellum, which transforms a motor command into a prediction of the sensory consequences of the movement. They also discuss how the characteristic input-output organization of the cerebro-cerebellum may contribute to forward models for non-motor higher brain functions. A central issue in motor systems neuroscience is how the central nervous system (CNS) coordinates the actions of redundant muscles. Hirashima et al. (2016) nicely review the optimization and muscle synergy hypotheses and note that both hypotheses are formulated mathematically without a clear concept of their neural implementation. Instead of these hypotheses, the authors propose a biologically plausible hypothesis (“the forgetting hypothesis”) for how optimization is realized by a population of neurons (at the synaptic level). Locomotive behavior and postural control are important aspects of motor control in which the CNS must control multiple muscles simultaneously based on multisensory inputs (e.g., visual, vestibular, proprioceptive and tactile). From this perspective, Aoi et al. (2016) examine locomotive behaviors and review simulation studies that explored adaptive motor control in locomotion via sensorimotor coordination using neuromusculoskeletal models based on the muscle synergy hypothesis. They discuss the neural mechanisms of motor control related to the muscle synergies for adaptive locomotion, and they stress the importance of neuromusculoskeletal modeling to capture the neural mechanisms underlying motor impairments in neurorehabilitation. Likewise, Chiba et al. (2016) examine postural control by summarizing the roles of multisensory inputs and demonstrate an example
5 Page 5 of 8
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of rapid postural alterations (fast dynamics) resulting from multisensory inputs in the regulation of upright posture maintenance. They also propose a posture control model and discuss the necessity for additional evidence associated with the long-term alterations in multisensory integration and postural control (slow dynamics). Finally, we introduce some examples of our attempts at using neuromodulation and model-based neurorehabilitation in the clinical setting. First, Yozu et al. (2016) introduce hereditary sensory and autonomic neuropathy (HSAN), which includes genetic disorders involving various sensory and autonomic dysfunctions. In particular, the authors focus on HSAN type 4 (HSAN-4) and type 5 (HSAN-5), which are characterized by insensitivity to pain and thermal sensation. Due to this somatic insensitivity, these patients show abnormal gait control that can cause the patients to hurt themselves. To resolve this serious problem, the authors propose a new model-based rehabilitation method. Through this method, they have built a system that can perform measurements and provide a real-time display of the kinematics and muscle synergies of gait. The authors provide their view that this visual feedback system may help patients to correct abnormalities in their gait. Furuya et al. (2016) examine focal task-specific dystonia (FTSD), which is a disorder that musicians, writers, painters, surgeons, and golfers have an especially high risk of developing. The behavioral features of this disorder include loss of independent movement control, muscular co-activation, awkward limb posture, and impairment of fine discrimination of tactile and proprioceptive sensations. Therefore, this disorder impairs not only motor dexterity but also somatosensory perception of well-trained behavioral tasks. In their paper, the authors summarize evidence that loss of inhibition and malplastic reorganization of body representations in the sensorimotor system (the primary motor and somatosensory areas, premotor area, cerebellum and basal ganglia) underlie FTSD. In addition, they introduce their promising approach of bi-hemispheric transcranial direct current stimulation (tDCS) over M1, which can improve motor control of pianists with FTSD. Overall, we found that the literature so far indicates the importance of the fronto-parietal network for body cognition and the essential contribution of the sensory-motor network for motor control. What is most lacking in the literature are detailed descriptions of the interactions between fronto-parietal and motor networks and evidence for the neural substrates underlying long-term changes in body representations. Investigators must work in parallel to evaluate the effectiveness of model-based interventions targeting the body representation. At present, we still have much work to do; however, we expect the effectiveness of model-based neurorehabilitation will improve as our understanding of brain plasticity as it relates to body cognition and motor control progresses through our embodied-brain program.
6 Page 6 of 8
References Aoi, S., Funato, T., 2016. Neuromusculoskeletal models based on the muscle synergy hypothesis for the investigation of adaptive motor control in locomotion via sensory-motor coordination. Neurosci. Res.
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ip t
Aymerich-Franch, L., Ganesh, G., 2016. The role of functionality in the body model for self-attribution. Neurosci. Res.
us
Chiba, R., Takakusaki, K., Ota, J., Yozu, A., Haga, N., 2016. Human upright posture control models based on multisensory inputs; in fast and slow dynamics. Neurosci. Res.
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Furuya, S., Hanakawa, T., 2016. A curse of motor expertise: use-dependent focal dystonia as manifestation of maladaptive changes in body representation. Neurosci. Res. Gallagher, S., 2005. How the body shapes the mind. Oxford university press, New York.
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Hanakawa, T., 2016. Organizing motor imageries. Neurosci. Res.
ce pt
Hirashima, M., Oya, T., 2016. How does the brain solve muscle redundancy? Filling the gap between optimization and muscle synergy hypotheses. Neurosci. Res.
Ac
Ishikawa, T., Tomatsu, S., Izawa, J., Kakei, S., 2016. The cerebro-cerebellum: Could it be loci of forward models? Neurosci. Res. Izawa, J., Asai, T., Imamizu, H., 2016. Computational motor control as a window to understanding schizophrenia. Neurosci. Res. Murata, A., Wen, W., Asama, H., 2016. The body and objects represented in the ventral stream of the parieto-premotor network. Neurosci. Res. Naito, E., Morita, T., Amemiya, K., 2016. Body representations in the human brain revealed by kinesthetic illusions and their essential contributions to motor control and corporeal awareness. Neurosci. Res.
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Oouchida, Y., Sudo, T., Inamura, T., Tanaka, N., Ohki, Y., Izumi, S., 2016. Maladaptive change of body representation in the brain after damage to central or peripheral nervous system. Neurosci. Res. Tanaka, H., 2016. Modeling the motor cortex: Optimality, recurrent neural networks, and spatial dynamics. Neurosci. Res.
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Yano, S., Maeda, T., Kondo, T., 2016. Slow dynamics perspectives on the embodied-brain systems science. Neurosci. Res.
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Yozu, A., Haga, N., Funato, T., Owaki, D., Chiba, R., Ota, J., 2016. Hereditary sensory and autonomic neuropathy types 4 and 5: review and proposal of a new rehabilitation method. Neurosci. Res.
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S0168-0102(15)00305-3 http://dx.doi.org/doi:10.1016/j.neures.2015.12.014 NSR 3915
To appear in:
Neuroscience Research
Received date: Revised date: Accepted date:
25-11-2015 4-12-2015 24-12-2015
Please cite this article as: Naito, E., Ota, J., Murata, A.,Body representation in the brain, Neuroscience Research (2016), http://dx.doi.org/10.1016/j.neures.2015.12.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Manuscript
Editorial Title: Body representation in the brain Authors: Eiichi Naito1,2, Jun Ota3, Akira Murata4
an
us
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Affiliations: 1 Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), 2A6 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan 2 Graduate School of Medicine and Graduate School of Frontier Biosciences, Osaka University, 2-15 Yamadaoka, Suita, Osaka 565-0871, Japan Tel: +81-80-9098-3256 Fax: +81-6-7174-8612 Email: [email protected] 3
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Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, 5-1-5 Kashiwa, Chiba, 277-8568, Japan Tel: +81-4-7136-4252 Fax: +81-4-7136-4242 Email: [email protected] 4
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Department of Physiology, Kinki University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, 589-8511, Japan Tel: +81-72-366-0221 Fax: +81-72-366-0206 Email: [email protected]
1 Page 1 of 8
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As the Japanese society ages rapidly, we are experiencing a sharp increase in the number of patients with motor or cognitive dysfunctions resulting from brain stroke or neurodegenerative disease. Thus, developing effective rehabilitative techniques to overcome these problems is of paramount importance. To perform motor actions, the brain should have a model of the body (structure, size, weight, posture or motor properties) and monitor “online” the state of the body as this changes during movement so that the brain can further update the model. We call this model the “body representation” in the brain, and our hypothesis is that dysfunctions in the body representation may commonly underlie the motor and cognitive dysfunctions. Hence, to overcome such problems, it is essential to further advance our knowledge about the neural entity of the body representation and to develop effective intervention techniques targeting this internal representation. In this special issue, the term “body representation” includes both the body image and body schema (c.f. Gallagher 2005). In general, the body image is heavily associated with awareness and knowledge of one’s own body, whereas the body schema is thought to be a pre-conscious plastic schema of the body that is utilized in motor control. Thus, the body representation we defined in this special issue covers a broader range of internal body representations that are deeply related to body cognition and motor control. To enrich our understanding of body representations so that we may develop effective rehabilitative techniques, it is very important to cultivate interdisciplinary approaches by combining brain science, rehabilitation medicine and systems engineering. Prompted by this belief, we have recently launched the embodied-brain program, which is one of the interdisciplinary programs from the Japan Society for the Promotion Science, Grant-in-Aid for Scientific Research on Innovative Areas. We thereby intend to establish a new academic discipline that is known as embodied-brain systems science. In this program, we conduct experiments with humans and animals to understand the neural mechanisms of body representations that mediate body cognition (sense of agency, sense of ownership) and motor control (muscle synergy control, anticipatory postural control). In particular, we aim to reveal the neural substrates underlying the long-term changes in body representations and to develop rehabilitative interventions based on the modeling of these findings. In this special issue, we have accumulated what we have learned so far about body representations in the brain and what actions we must take in the future to better understand brain plasticity and body representations to promote their adaptive functions. This special issue covers a wide range of important topics. We first present papers related to body cognition and then introduce papers related to motor control.
2 Page 2 of 8
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Electrophysiological investigation in animals is a core technique for understanding neuronal representations in the brain, and it has been one of the primary techniques used in systems neuroscience. In this respect, Murata et al. (2016) provide an excellent review paper that examines the essential contributions of the dorsal-ventral streams of the parieto-premotor networks to forming internal body representations. In this paper, the authors revalidate the importance of the anterior intraparietal area (AIP) and ventral premotor cortex (F5) for the motor control of the hand and object manipulation. Furthermore, the authors provide new evidence that some classes of parietal neurons encode either one’s own actions or those of others, whereas other classes of parietal neurons encode both. The latter is consistent with the classical concept of mirror neurons in which a map of one’s own body parts can be used as a reference to perceive the body parts of others, whereas the former can be used for self-other distinctions and for distinguishing the agency of action. Because these types of neurons are found within the mirror neuron system, the parieto-premotor network that includes the mirror neuron system is key to controlling one’s own actions and to mapping one’s own body and those of others, allowing recognition of action contribution, i.e., sense of agency. The importance of the dorsal-ventral stream of the parieto-premotor network is corroborated in the human brain (Naito et al. 2016), and it is highly likely that the network is supported by the large-scale brain fibers of the inferior branch of superior longitudinal fasciculus (SLF III). In this review, the authors mainly focus on how proprioceptive (kinesthetic) information contributes to the formation of body representations in humans by introducing neuroimaging evidence obtained from their unique approach of vibration-induced proprioceptive illusions. They discuss the possibility that a motor network centered in the primary motor cortex (M1) processes kinesthetic information and constructs kinematic and dynamic postural models of a limb (body schema) that enables fast online feedback control. In addition, the authors show that kinesthetic illusory awareness is an attribute of the neural activity in the right SLF III network, and some of these neuronal substrates are shared for self-identification (body image). They also introduce the concept of functional lateralization in the human brain. Aymerich-Franch and Ganesh (2016) provide a novel framework to better understand the computational mechanisms of body representations for self-attribution (sense of ownership). The article summarizes previous experimental findings on the embodiment of artificial limbs (e.g., rubber hand illusion), the entire body, and a virtual avatar. The authors also provide further explanations for these results based on the “functional body model hypothesis.” They propose a new concept of functionality in the body model in which our brain attributes a perceived entity as our own limb
3 Page 3 of 8
Ac
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ed
M
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(body) if the physical properties of the entity are sufficient to afford certain actions the brain has associated with that limb (body). They also provide their view that the body model would be represented by the fronto-parietal network, specifically through interactions between the inferior parietal and occipital regions and between the premotor and somatosensory areas via intraparietal areas, which generally fits well with the views from Murata et al. (2016) and Naito et al. (2016). Developing effective rehabilitative techniques requires a better understanding of dysfunction in the human brain. In this respect, Oouchida et al. (2016) attempt to explain maladaptive neuronal processes underlying phantom limb experience in terms of use-dependent plasticity. Computational modeling greatly facilitates understanding and prediction of changes in the body representation in the brain. Izawa et al. (2016) focus on the impaired inference mechanism of self-agency in the brains of individuals with schizophrenia, in which the sensory outcome generated by self-initiated action is misattributed to the actions of another agent. The authors have developed a novel computational model of agency experience using a Bayesian decision-making framework, and they unite the computational mechanisms of agency and motor control via an internal model based on optimal feedback control using Kalman filtering. The model seems to be able to successfully predict several of the motor abnormalities observed in schizophrenia by assuming a deformed internal model. To discuss the plausibility of their model’s predictions, they review reports in the literature that could support their predictions and suggest experiments that would potentially examine the proposed model of schizophrenia. Recent studies in cognitive neuroscience have noted the importance of fast and slow neural processes in body cognition. Yano et al. (2016) review the mathematical basis for understanding fast-slow dynamical systems. They also review the basis of Bayesian statistics and propose a fast-slow perspective on Bayesian statistics. This review may be helpful for advancing the Bayesian perspectives in cognitive neuroscience. One of the higher brain functions that links cognition and motor control is motor imagery, wherein individuals mentally simulate body movements without overtly generating movement. This function results from a forward model generated by the brain that transforms a motor command into a prediction of the sensory consequences that would result from the movement. Motor imagery is also applicable for neurorehabilitation. Hanakawa (2016) reviews a wide range of reports and nicely characterizes motor imagery in terms of motor control, sensory modalities, agency and explicitness. In addition, he introduces cutting-edge research on neurofeedback training with motor imagery.
4 Page 4 of 8
Ac
ce pt
ed
M
an
us
cr
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As described above, another important topic in this special issue is motor control. In the second half of this special issue, we introduce papers related to motor control, especially in terms of muscle synergy control and anticipatory postural control. It is well established that M1 is an executive locus of voluntary movement. However, exactly how M1 controls body movements remains an unresolved problem. Electrophysiological findings support both kinematic and dynamic coding hypotheses. Tanaka (2016) nicely summarizes several M1 models in terms of optimality, recurrent neural networks and spatial dynamics. In particular, he proposes a new M1 model that includes cross products, which can better explain the characteristics of neural responses and the experimental results of kinematical and dynamical adaptation. M1 forms closed loop circuits with the cerebellum, and the cerebro-cerebellar interactions, in general, likely play essential roles not only in motor control but also in non-motor higher brain functions. Ishikawa et al. (2016) provide physiological and morphologic evidences suggesting the existence of a forward model for limb movement in the cerebro-cerebellum, which transforms a motor command into a prediction of the sensory consequences of the movement. They also discuss how the characteristic input-output organization of the cerebro-cerebellum may contribute to forward models for non-motor higher brain functions. A central issue in motor systems neuroscience is how the central nervous system (CNS) coordinates the actions of redundant muscles. Hirashima et al. (2016) nicely review the optimization and muscle synergy hypotheses and note that both hypotheses are formulated mathematically without a clear concept of their neural implementation. Instead of these hypotheses, the authors propose a biologically plausible hypothesis (“the forgetting hypothesis”) for how optimization is realized by a population of neurons (at the synaptic level). Locomotive behavior and postural control are important aspects of motor control in which the CNS must control multiple muscles simultaneously based on multisensory inputs (e.g., visual, vestibular, proprioceptive and tactile). From this perspective, Aoi et al. (2016) examine locomotive behaviors and review simulation studies that explored adaptive motor control in locomotion via sensorimotor coordination using neuromusculoskeletal models based on the muscle synergy hypothesis. They discuss the neural mechanisms of motor control related to the muscle synergies for adaptive locomotion, and they stress the importance of neuromusculoskeletal modeling to capture the neural mechanisms underlying motor impairments in neurorehabilitation. Likewise, Chiba et al. (2016) examine postural control by summarizing the roles of multisensory inputs and demonstrate an example
5 Page 5 of 8
Ac
ce pt
ed
M
an
us
cr
ip t
of rapid postural alterations (fast dynamics) resulting from multisensory inputs in the regulation of upright posture maintenance. They also propose a posture control model and discuss the necessity for additional evidence associated with the long-term alterations in multisensory integration and postural control (slow dynamics). Finally, we introduce some examples of our attempts at using neuromodulation and model-based neurorehabilitation in the clinical setting. First, Yozu et al. (2016) introduce hereditary sensory and autonomic neuropathy (HSAN), which includes genetic disorders involving various sensory and autonomic dysfunctions. In particular, the authors focus on HSAN type 4 (HSAN-4) and type 5 (HSAN-5), which are characterized by insensitivity to pain and thermal sensation. Due to this somatic insensitivity, these patients show abnormal gait control that can cause the patients to hurt themselves. To resolve this serious problem, the authors propose a new model-based rehabilitation method. Through this method, they have built a system that can perform measurements and provide a real-time display of the kinematics and muscle synergies of gait. The authors provide their view that this visual feedback system may help patients to correct abnormalities in their gait. Furuya et al. (2016) examine focal task-specific dystonia (FTSD), which is a disorder that musicians, writers, painters, surgeons, and golfers have an especially high risk of developing. The behavioral features of this disorder include loss of independent movement control, muscular co-activation, awkward limb posture, and impairment of fine discrimination of tactile and proprioceptive sensations. Therefore, this disorder impairs not only motor dexterity but also somatosensory perception of well-trained behavioral tasks. In their paper, the authors summarize evidence that loss of inhibition and malplastic reorganization of body representations in the sensorimotor system (the primary motor and somatosensory areas, premotor area, cerebellum and basal ganglia) underlie FTSD. In addition, they introduce their promising approach of bi-hemispheric transcranial direct current stimulation (tDCS) over M1, which can improve motor control of pianists with FTSD. Overall, we found that the literature so far indicates the importance of the fronto-parietal network for body cognition and the essential contribution of the sensory-motor network for motor control. What is most lacking in the literature are detailed descriptions of the interactions between fronto-parietal and motor networks and evidence for the neural substrates underlying long-term changes in body representations. Investigators must work in parallel to evaluate the effectiveness of model-based interventions targeting the body representation. At present, we still have much work to do; however, we expect the effectiveness of model-based neurorehabilitation will improve as our understanding of brain plasticity as it relates to body cognition and motor control progresses through our embodied-brain program.
6 Page 6 of 8
References Aoi, S., Funato, T., 2016. Neuromusculoskeletal models based on the muscle synergy hypothesis for the investigation of adaptive motor control in locomotion via sensory-motor coordination. Neurosci. Res.
cr
ip t
Aymerich-Franch, L., Ganesh, G., 2016. The role of functionality in the body model for self-attribution. Neurosci. Res.
us
Chiba, R., Takakusaki, K., Ota, J., Yozu, A., Haga, N., 2016. Human upright posture control models based on multisensory inputs; in fast and slow dynamics. Neurosci. Res.
M
an
Furuya, S., Hanakawa, T., 2016. A curse of motor expertise: use-dependent focal dystonia as manifestation of maladaptive changes in body representation. Neurosci. Res. Gallagher, S., 2005. How the body shapes the mind. Oxford university press, New York.
ed
Hanakawa, T., 2016. Organizing motor imageries. Neurosci. Res.
ce pt
Hirashima, M., Oya, T., 2016. How does the brain solve muscle redundancy? Filling the gap between optimization and muscle synergy hypotheses. Neurosci. Res.
Ac
Ishikawa, T., Tomatsu, S., Izawa, J., Kakei, S., 2016. The cerebro-cerebellum: Could it be loci of forward models? Neurosci. Res. Izawa, J., Asai, T., Imamizu, H., 2016. Computational motor control as a window to understanding schizophrenia. Neurosci. Res. Murata, A., Wen, W., Asama, H., 2016. The body and objects represented in the ventral stream of the parieto-premotor network. Neurosci. Res. Naito, E., Morita, T., Amemiya, K., 2016. Body representations in the human brain revealed by kinesthetic illusions and their essential contributions to motor control and corporeal awareness. Neurosci. Res.
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