Fady Alnajjar

Muscle synergy space: learning model to create an optimal muscle synergy.

Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however, remains a long-standing challenge. One effective method of choosing muscle combinations is to create a set containing the muscle combinations of only efficient behaviors, and then to choose combinations from that set. The notion of muscle synergy, which was introduced to divide muscle activations into a lower-dimensional synergy space and time-dependent variables, is a suitable tool relevant to the discussion of this issue. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to control behaviors. In this study, we investigated the mechanism the CNS may use to define the appropriate region and size of the synergy space when performing skilled behavior. Two indices were introduced in this study, one is the synergy stability index (SSI) that indicates the region of the synergy space, the other is the synergy coordination index (SCI) that indicates the size of the synergy space. The results on automatic posture response experiments show that SSI and SCI are positively correlated with the balance skill of the participants, and they are tunable by behavior training. These results suggest that the CNS has the ability to create optimal sets of efficient behaviors by optimizing the size of the synergy space at the appropriate region through interacting with the environment.

The hierarchical and functional connectivity of higher-order cognitive mechanisms: neurorobotic model to investigate the stability and flexibility of working memory

Higher-order cognitive mechanisms (HOCM), such as planning, cognitive branching, switching, etc., are known to be the outcomes of a unique neural organizations and dynamics between various regions of the frontal lobe. Although some recent anatomical and neuroimaging studies have shed light on the architecture underlying the formation of such mechanisms, the neural dynamics and the pathways in and between the frontal lobe to form and/or to tune the stability level of its working memory remain controversial. A model to clarify this aspect is therefore required. In this study, we propose a simple neurocomputational model that suggests the basic concept of how HOCM, including the cognitive branching and switching in particular, may mechanistically emerge from time-based neural interactions. The proposed model is constructed such that its functional and structural hierarchy mimics, to a certain degree, the biological hierarchy that is believed to exist between local regions in the frontal lobe. Thus, the hierarchy is attained not only by the force of the layout architecture of the neural connections but also through distinct types of neurons, each with different time properties. To validate the model, cognitive branching and switching tasks were simulated in a physical humanoid robot driven by the model. Results reveal that separation between the lower and the higher-level neurons in such a model is an essential factor to form an appropriate working memory to handle cognitive branching and switching. The analyses of the obtained result also illustrates that the breadth of this separation is important to determine the characteristics of the resulting memory, either static memory or dynamic memory. This work can be considered as a joint research between synthetic and empirical studies, which can open an alternative research area for better understanding of brain mechanisms.

Muscle synergy stability and human balance maintenance.

BackgroundThe signals that the central nervous system (CNS) produces and sends to the muscles to effect movement are not entirely understood. Muscle synergy theory suggests that the central nervous system produces a small number of signals that pass through a network that distributes combinations of these signals to the muscles. Though these synergies are rather stable over time, some variability is present.MethodsHere, we investigated the variability of muscle synergy and defined a synergy stability index (SSI) to quantify it. We measured the activity of muscles responsible for maintaining lateral balance in humans standing on a platform that was subjected to lateral disturbance from the platform. We then calculated muscle synergies attributed to postural reflex and automatic response by using non-negative matrix factorization (NMF). Finally, from the calculated muscle synergies, we obtained SSI.ResultsWe observed a positive proportional relation between balance performance and SSI. Participants who were adept at maintaining balance were found to have invariant muscle synergies, and non-adept participants showed variable muscle synergies.ConclusionsThese results suggest that SSI can be used to quantitatively evaluate balance maintenance ability.

Artificial balancer - Supporting device for postural reflex

The evolutionarily novel ability to keep ones body upright while standing or walking, the human balance, deteriorates in old age or can be compromised after accidents or brain surgeries. With the aged society, age related balance problems are on the rise. Persons with balance problems are more likely to fall during their everyday life routines. Especially in elderly, falls can lead to bone fractures making the patient bedridden, weakening the body and making it more prone to other diseases. Health care expenses for a fall patient are often very high. There is a great deal of research being done on exoskeletons and power assists. However, these technologies concentrate mainly on the amplifications of human muscle power while balance has to be provided by the human themself. Our research has been focused on supporting human balance in harmony with the humans own posture control mechanisms such as postural reflexes. This paper proposes an artificial balancer that supports human balance through acceleration of a flywheel attached to the body. Appropriate correcting torques are generated through our device based on the measurements of body deflections. We have carried out experiments with test persons standing on a platform subject to lateral perturbations and ambulatory experiments while walking on a balance beam. These experiments have demonstrated the effectiveness of our device in supporting balance and the possibility of enhancing balance-keeping capability in human beings through the application of external torque.

The functional role of automatic body response in shaping voluntary actions based on muscle synergy theory

The functional role of automatic body response in forming voluntary actions remain controversial. We here support the hypothesis that the automatic body responses could be used as a reference to adapt voluntary actions to the environment. We validate this hypothesis by analyzing human body movements from the perspective of muscle synergy. In this study, a horizontal shoulder adduction of the dominant arm of four healthy subjects was examined in various tasks. The tasks include reflex and voluntary movements in regular and modified environments. Preliminary results were encouraging; the number and the consistency between the utilized synergies in automatic and voluntary tasks were fairly correlated. In contrast, there was a lack of the correlation when the environment was abruptly modified (an additional resistance applied to the voluntary movement). This lack of correlation, however, was gradually adjusted through training. Our results suggest that automatic synergy may encode some features which could be used by the central nervous system to shape the voluntary synergy.

Importance of muscle selection for EMG signal analysis during upper limb rehabilitation of stroke patients

Current work highlights the importance of muscle selection to evaluate paralysis and recovery level of stroke patients when comparing synergies of affected and non-affected side of the body. The proposed method allows the selection of important muscles that highly contribute to the specific movements according to the power and frequency distribution of the electromyographic signals.. Users participating performed steering-wheel-based therapy focused on upper limb rehabilitation. Final results show that with the appropriate muscles selection, it is possible to compute a Similarity Index between right and left arms (during symmetric motion) associated to the level of paralysis and potential recovery of a given subject.

Formulating a Cognitive Branching Task by MTRNN: A Robotic Neuroscience Experiments to Simulate the PFC and Its Neighboring Regions

The foremost objective of our research series is to construct a neurocomputational model that aims to achieve a Large-Scale Brain Network (LSBN), and to offer a better insight of how the macro-level anatomical structures, such as the connectivity between the frontal lobe regions and their dynamic properties, can be self-organized to obtain the higher order cognitive mechanisms. To address this issue, this paper focuses in proposing a model that intends to understand the mechanisms underlying the cognitive branching function, a higher order cognitive mechanism, in which a delaying to the execution of an original task occurs until the completion of a subordinate task. The model is constructed by a hierarchical Multi-Timescale Recurrent Neural Network (MTRNN) and conducted on a humanoid robot in a physical environment. Experimental results suggest possible neural activities and network’s layout at the investigated regions that act as an important factor to accomplish such a task.

Static and dynamic memory to simulate higher-order cognitive tasks

The foremost objective of our research series is to construct a neurocomputational model that aims to achieve a Large-Scale Brain Network, and to suggest a possible insight of how the macro-level anatomical structures, such as the connectivity between the frontal lobe regions and their dynamic properties, can be self-organized to obtain the higher-order cognitive mechanisms, such as: planning, reasoning, task switching, cognitive branching, etc. For addressing these issues, this paper, in particular, focuses in proposing a model that intends to clarify the neural structure and mechanisms underlying the task switching and the cognitive branching condition. Although both tasks requiring varying degree of a working memory, in contrast to the switching task, where the primary ongoing task is entirely replaced by a new task, in the branching task, a delaying to the execution of an original task occurs until the completion of a subordinate task. The proposed model is constructed by a hierarchical Multiple Timescale Recurrent Neural Network (MTRNN) and conducted on a humanoid robot in a physical environment. Experimental results suggest essential factors related to the neural activities and networks structure necessary to form a suitable working memory for accomplishing such tasks.

The Role of Inputs Combination to Enhance the Internal Model and Body Control Ability

The role of attention in formulating the input-signals to the CNS toward enhancing the motor-control ability in human is unclear. Here we hypothesized that the distance between the arms in alignment to the frontal center of a person, and the voluntary shifting of his visual attention play roles in enhancing the internal model and the body-control ability. To examine this, six participants were introduced to dual-steering-device. Using the device, we can modulate the participant’s visual attentions and arms distance while performing various tasks. Major muscles and brain activities of the participants were monitored using EMG, and fNIRS. The results were compatible with our hypothesis: users could inhibit muscular activities in the passive movements with increasing distance of the arms and with a visual focus on the inhibited arm. We believe that this study can add important contributing factors in designing rehabilitation program by adjusting the possible input-combination to enhance the internal-model.

Muscle Synergies Indices to Quantify the Skilled Behavior in Human

Muscle synergy interprets the neural strategy adopted by the central nervous system (CNS) to simplify the coordination of muscles recruitments when performing useful movements. The computational mechanism of defining the optimal muscle combinations, however, still debatable. Muscle synergy deals with muscle activations pattern and time-dependent variables. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to drive the behavior. In this study, we investigated the role of the CNS to optimize muscle patterns when performing skilled behavior. We introduced two synergy indices: the synergy stability index that indicates the similarity of the recruited synergies, and the synergy coordination index that indicates the size of the synergy space. The results on automatic posture response experiments on seven healthy participants show that both indices are positively correlated with the overall balance skill of the participants. Results suggest the optimal mechanisms adopted by the CNS to recruit muscles.