Lateralised dynamic modulations of corticomuscular coherence associated with bimanual learning of rhythmic patterns

Supplementary Information: The online version contains supplementary material available at https://doi.org/ 10.1038/s41598-022-10342-5Human movements are spontaneously attracted to auditory rhythms, triggering an automatic activation of the motor system, a central phenomenon to music perception and production. Cortico- muscular coherence (CMC) in the theta, alpha, beta and gamma frequencies has been used as an index of the synchronisation between cortical motor regions and the muscles. Here we investigated how learning to produce a bimanual rhythmic pattern composed of low- and high-pitch sounds affects CMC in the beta frequency band. Electroencephalography (EEG) and electromyography (EMG) from the left and right First Dorsal Interosseus and Flexor Digitorum Superficialis muscles were concurrently recorded during constant pressure on a force sensor held between the thumb and index finger while listening to the rhythmic pattern before and after a bimanual training session. During the training, participants learnt to produce the rhythmic pattern guided by visual cues by pressing the force sensors with their left or right hand to produce the low- and high-pitch sounds, respectively. Results revealed no changes after training in overall beta CMC or beta oscillation amplitude, nor in the correlation between the left and right sides for EEG and EMG separately. However, correlation analyses indicated that left- and right-hand beta EEG–EMG coherence were positively correlated over time before training but became uncorrelated after training. This suggests that learning to bimanually produce a rhythmic musical pattern reinforces lateralised and segregated cortico-muscular communication.This work was supported by a grant from the Australian Research Council (DP170104322)

Network Physiology of Cortico–Muscular Interactions

Skeletal muscle activity is continuously modulated across physiologic states to provide coordination, flexibility and responsiveness to body tasks and external inputs. Despite the central role the muscular system plays in facilitating vital body functions, the network of brain-muscle interactions required to control hundreds of muscles and synchronize their activation in relation to distinct physiologic states has not been investigated. Recent approaches have focused on general associations between individual brain rhythms and muscle activation during movement tasks. However, the specific forms of coupling, the functional network of cortico-muscular coordination, and how network structure and dynamics are modulated by autonomic regulation across physiologic states remains unknown. To identify and quantify the cortico-muscular interaction network and uncover basic features of neuro-autonomic control of muscle function, we investigate the coupling between synchronous bursts in cortical rhythms and peripheral muscle activation during sleep and wake. Utilizing the concept of time delay stability and a novel network physiology approach, we find that the brain-muscle network exhibits complex dynamic patterns of communication involving multiple brain rhythms across cortical locations and different electromyographic frequency bands. Moreover, our results show that during each physiologic state the cortico-muscular network is characterized by a specific profile of network links strength, where particular brain rhythms play role of main mediators of interaction and control. Further, we discover a hierarchical reorganization in network structure across physiologic states, with high connectivity and network link strength during wake, intermediate during REM and light sleep, and low during deep sleep, a sleep-stage stratification that demonstrates a unique association between physiologic states and cortico-muscular network structure. The reported empirical observations are consistent across individual subjects, indicating universal behavior in network structure and dynamics, and high sensitivity of cortico-muscular control to changes in autonomic regulation, even at low levels of physical activity and muscle tone during sleep. Our findings demonstrate previously unrecognized basic principles of brain-muscle network communication and control, and provide new perspectives on the regulatory mechanisms of brain dynamics and locomotor activation, with potential clinical implications for neurodegenerative, movement and sleep disorders, and for developing efficient treatment strategies

Rehabilitation of the upper limb after an stroke. Part 5. Dissociation to an “open“ chain and hand treatment! An multi-eclectic approach

Introduction: Part 4 show what the skill must be to achieve dissociation and with the hand onfacilitation we say amazing results with the possibilities to create an goal in the ADL and often with some hand possibilities. To get the hand on the right spot we need the arm therefore also the trunk and the diagonals. This part we go further with this dissociation and search for an “open “chain but we see also the possibilities that sciences have created. The science has stoke much effort in the developmental of robotic and F.E.S. stimulation techniques but still today it isn’t clear of this the answer is for the recovery of the arm and especially the hand and it isn’t also clear or this is an better way to get more function in the hand than the “old way”. Design: But obvious there is much more possible to get an better arm-hand function by using this inventions, but than for all stroke survivors with arm and hand function decrease. An eclectic approach will use everything to get an better result but always with an good base of science and with the training and motoric learning rules in the head. To get an result in the damaged brain, there must be an amount of intensity to get the brain working on his plasticity. The question remain that we not know how much plasticity an damaged brain can obtain and obvious that is different for every stroke survivor. Conclusion: Therefore therapist try everything what you think can help but also science keep on searching!! And this can also be done in the chronic stage of this disease. In this part will always the care for the diagonals (Whole Trunk), the shoulder all away to the hand be central because you cannot forget an part an think that this has no influence on the goal that you are exercising. Hands-on – hands-off, electro treatment, incorporation in the ADL, mobility care for all tissues and tone control etc. make that the outcome is maximal

Dissecting muscle synergies in the task space

The research presented here concerns the notion of modularity and its role in the functional organisation of the human motor system. In Chapter 1, I introduce the concept of modularity and a popular computational approach to its investigation in the motor neurosciences known as muscle synergy analysis. I highlight open problems in this field, in particular the lack of a direct mapping of muscle synergies to task performance, and present the ways in which i will address these open problems both conceptually and analytically. In Chapter 2, I address current analytical limitations in the field by leveraging information- and network-theoretic tools to present a novel, generalisable approach to muscle synergy extraction under relaxed model assumptions. This approach builds on top of traditional methods and is referred to as the Network-Information Framework (NIF). In Chapter 3, I then employ the NIF to provide a new perspective on muscle synergies that is made implicit in this novel computational approach. This novel perspective integrates important findings from recent influential works showing how muscles not only ’work together’ towards common task-goals as previously conceived, but also complementary and task-irrelevant objectives concomitantly. By directly including task parameters into muscle synergy extraction, i effectively dissect the task-relevant information dynamics underlying coordinated movement, thus providing a principled way to access this complex functional architecture. In Chapter 4, I further develop the NIF to simultaneously quantify diverse types of muscle interactions across inter- and intra-muscular scales, including functionally similar (i.e. redundant), -complementary (i.e. synergistic) and -independent (i.e. unique) interactions. In doing so, I reveal novel insights into movement control in health and with pathology. I also align current muscle synergy analysis with the forefront in theoretical understanding on human movement modularity. To conclude this work, in Chapter 5 I summarise these contributions, their implications for neurobiological mechanisms, and the novel research opportunities they present for the motor control field

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

The human sensorimotor cortex fosters muscle synergies through cortico-synergy coherence

In neuromotor control, the dimensionality of complex muscular activation patterns is effectively reduced through the emergence of muscle synergies. Muscle synergies are tailored to task-specific biomechanical needs. Traditionally, they are considered as low-dimensional neural output of the spinal cord and as such their coherent cortico-muscular pathways have remained underexplored in humans. We investigated whether muscle synergies have a higher-order origin, especially, whether they are manifest in the cortical motor network. We focused on cortical muscle synergy representations involved in balance control and examined changes in cortico-synergy coherence accompanying short-term balance training. We acquired electromyography and electro-encephalography and reconstructed cortical source activity using adaptive spatial filters. The latter were based on three muscle synergies decomposed from the activity of nine unilateral leg muscles using non-negative matrix factorization. The corresponding cortico-synergy coherence displayed phase-locked activity at the Piper rhythm, i.e., cortico-spinal synchronization around 40 Hz. Our study revealed the presence of muscle synergies in the motor cortex, in particular, in the paracentral lobule, known for the representation of lower extremities. We conclude that neural oscillations synchronize between the motor cortex and spinal motor neuron pools signifying muscle synergies. The corresponding cortico-synergy coherence around the Piper rhythm decreases with training-induced balance improvement