A synergy-based hand control is encoded in human motor cortical areas

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses

A synergy-based hand control is encoded in human motor cortical areas

abstract: How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses

Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates

Most functional MRI (fMRI) studies map task-driven brain activity using a block or event-related paradigm. Sparse paradigm free mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information, but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of activation likelihood estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the sensorimotor network (SMN) to six motor functions (left/right fingers, left/right toes, swallowing, and eye blinks). We validated the framework using simultaneous electromyography (EMG)–fMRI experiments and motor tasks with short and long duration, and random interstimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events were 77 ± 13% and 74 ± 16%, respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55% and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this article discusses methodological implications and improvements to increase the decoding performance

Mechanisms of motor learning: by humans, for robots

Whenever we perform a movement and interact with objects in our environment, our central nervous system (CNS) adapts and controls the redundant system of muscles actuating our limbs to produce suitable forces and impedance for the interaction. As modern robots are increasingly used to interact with objects, humans and other robots, they too require to continuously adapt the interaction forces and impedance to the situation. This thesis investigated the motor mechanisms in humans through a series of technical developments and experiments, and utilized the result to implement biomimetic motor behaviours on a robot. Original tools were first developed, which enabled two novel motor imaging experiments using functional magnetic resonance imaging (fMRI). The first experiment investigated the neural correlates of force and impedance control to understand the control structure employed by the human brain. The second experiment developed a regressor free technique to detect dynamic changes in brain activations during learning, and applied this technique to investigate changes in neural activity during adaptation to force fields and visuomotor rotations. In parallel, a psychophysical experiment investigated motor optimization in humans in a task characterized by multiple error-effort optima. Finally a computational model derived from some of these results was implemented to exhibit human like control and adaptation of force, impedance and movement trajectory in a robot

Modelling individual variations in brain structure and function using multimodal MRI

Every brain is different. Understanding this variability is crucial for investigating the neural substrate underlying individuals’ unique behaviour and developing personalised diagnosis and treatments. This thesis presents novel computational approaches to study individual variability in brain structure and function using magnetic resonance imaging (MRI) data. It comprises three main chapters, each addressing a specific challenge in the field. In Chapter 3, the thesis proposes a novel Image Quality Transfer (IQT) technique, HQ-augmentation, to accurately localise a Deep Brain Stimulation (DBS) target in low-quality clinical-like data. Leveraging high-quality diffusion MRI datasets from the Human Connectome Project (HCP), the HQ-augmentation approach is robust to corruptions in data quality while preserving the individual anatomical variability of the DBS target. It outperforms existing alternatives and generalises to unseen low-quality diffusion MRI datasets with different acquisition protocols, such as the UK Biobank (UKB) dataset. In Chapter 4, the thesis presents a framework for enhancing prediction accuracy of individual task-fMRI activation profiles using the variability of resting-state fMRI. Assuming resting-state functional modes underlie task-evoked activity, this chapter demonstrates that shape and intensity of individualised task activations can be separately modelled. This chapter introduced the concept of "residualisation" and showed that training on residuals leads to better individualised predictions. The framework’s prediction accuracy, validated on HCP and UKB data, is on par with task-fMRI test-retest reliability, suggesting potential for supplementing traditional task localisers. In Chapter 5, the thesis presents a novel framework for individualised retinotopic mapping using resting-state fMRI, from the primary visual cortex to visual cortex area 4. The proposed approach reproduces task-elicited retinotopy and captures individual differences in retinotopic organisation. The proposed framework delineates borders of early visual areas more accurately than group-average parcellation and is effective with both high-field 7T and more common 3T resting-state fMRI data, providing a valuable alternative to resource-intensive retinotopy task-fMRI experiments. Overall, this thesis demonstrates the potential of advanced MRI analysis techniques to study individual variability in brain structure and function, paving the way for improved clinical applications tailored to individual patients and a better understanding of neural mechanisms underlying unique human behaviour

Reconstructing Resting State Networks from EEG

Resting state networks (RSNs) have been found in human brains during awake resting states. RSNs are composed of spatially distributed regions in which spontaneous activity fluctuations are temporally and dynamically correlated. In contrast to task-related brain activities, RSNs reflect intrinsic functional organizations and rhythms of the human brain when it is not engaged in any task and/or disturbed by external stimuli. To date, RSNs have been widely studied using functional magnetic resonance imaging (fMRI), which has identified various RSNs associated with different brain functions. More recently, due to the advantage of millisecond temporal resolution, both electroencephalography (EEG) and magnetoencephalography (MEG) have been used to investigate RSNs and their electrophysiological underpinnings. Despite these advantages, current RSN studies using EEG/MEG, as compared with those using fMRI, are still at their infant stage in many aspects, such as the quality of spatial pattern reconstructions and the reliability of detections. These limitations require further studies to obtain accurate reconstructions of RSNs directly from EEG/MEG data. My research aims to develop, optimize, and validate a variety of computational and analytical frameworks to reconstruct and investigate RSNs based on EEG data. In this dissertation, several studies have been conducted as outlined below. Firstly, a comparison in defining RSNs at the sensor space and at the source space was performed to evaluate the accuracy in reconstructing RSN spatial patterns. Results from both simulated and experimental data indicated that the analysis in the source space performed better in reconstructing various features of RSNs. Secondly, a new computational framework for reconstructing RSNs with human EEG data was developed. The proposed framework utilized independent component analysis (ICA) on short-time Fourier transformed inverse source maps imaged from EEG data and statistical correlation analysis to generate cortical tomography of electrophysiological RSNs. The proposed framework was validated using three sets of experimental data. The results indicated that the framework is reliable and efficient in the reconstruction of RSNs. Thirdly, an advanced inverse source imaging (ISI) method was used in the established framework discussed above to improve the spatial estimation of RSNs. The comparison between the new and conventional frameworks suggested that the ISI method significantly improved the accuracy of spatial estimations of RSNs. Fourthly, an ICA-based framework was used to assess RSN alternations under different conditions, which has been the model to identify imaging biomarkers, for example, for diseased patients as compared with healthy control. The results from both simulated and experimental data indicated that the framework could detect RSN alternations due to condition differences. My results further suggest that the framework could provide a finer resolution in detecting RSN changes as a contrast for multi-level (more than 2) condition differences, which can be used to study the difference, for example, among patients with a long history of a certain disorder, a short history, and healthy control. Overall, the findings of this dissertation study provided insights into the underlying electrophysiological basis of RSNs. More importantly, this study developed new frameworks that can be used as powerful tools for future investigations of more characteristics of RSNs, in particular for those not available in fMRI, e.g., spectral patterns

Network mechanisms underlying motor control after ischemic stroke

Motor impairment is one of the most common symptoms after ischemic stroke. While many patients partially regain lost functions due to plastic changes to the structural and functional architecture of brain networks, recovery is often incomplete, making stroke a leading cause of long-term disability worldwide. Thus, a better mechanistic understanding of motor recovery seems crucial to inform future plasticity-enhancing treatment approaches. The studies summarized in the present thesis therefore aimed at furthering our mechanistic insights into motor network reorganization in acute and chronic stroke patients. In study 1, we focused on the role of different descending motor pathways on distinct aspects of motor control. Study 2 addressed the role of corticospinal output fibers descending from the primary motor cortex (M1) and various premotor areas. In study 3, we assessed cortico-cortical structural connectivity and its differential association with basal and complex motor functions. Finally, study 4 utilized fMRI-data from acute stroke patients to conduct the first direct comparison of resting-state functional and task-related effective connectivity. Taken together, our findings offer novel insights into mechanisms underlying motor control after stroke and hold important implications for therapeutic interventions