Resting State EEG Directed Functional Connectivity Unveils Changes in Motor Network Organization in Subacute Stroke Patients After Rehabilitation

Top-Down and Bottom-Up Mechanisms of Motor Recovery Poststroke

Stroke remains a leading cause of disability. Motor recovery requires the interaction of top-down and bottom-up mechanisms, which reinforce each other. Injury to the brain initiates a biphasic neuroimmune process, which opens a window for spontaneous recovery during which the brain is particularly sensitive to activity. Physical activity during this sensitive period can lead to rapid recovery by potentiating anti-inflammatory and neuroplastic processes. On the other hand, lack of physical activity can lead to early closure of the sensitive period and downstream changes in muscles, such as sarcopenia, muscle stiffness, and reduced cardiovascular capacity, and blood flow that impede recovery.

Influence of Transcutaneous Vagus Nerve Stimulation on Motor Planning: A Resting-State and Task-State EEG Study

Transcutaneous vagus nerve stimulation (tVNS) shows a potential regulatory role for motor planning. Still, existing research mainly focuses on behavioral studies, and the neural modulation mechanism needs to be clarified. Therefore, we designed a multi-condition (active or sham, pre or under, difficult or easy, left-hand or right-hand) motor planning experiment to explore the effect of online tVNS (i.e., tVNS and tasks synchronized). Twenty-eight subjects were recruited and randomly assigned to active and sham groups. Both groups performed the same tasks in the experiment and separately collected task-state EEG and 5-min eye-open resting-state EEG. The results showed that the changes in event-related potential (ERP) and movement-related cortical potential (MRCP) amplitudes were more significant for the left-hand difficult task (LD) under active-tVNS. According to the power spectrum results, active-tVNS significantly modulated the activities of the contralateral motor cortex at beta and gamma bands in the resting state. The functional connectivity based on partial directed coherence (PDC) showed significant changes in the parietal lobe after active-tVNS. These findings suggest that tVNS is a promising way to improve motor planning ability.

Bilateral upper limb robot-assisted rehabilitation improves upper limb motor function in stroke patients: a study based on quantitative EEG

BACKGROUND

Upper limb dysfunction after stroke seriously affects quality of life. Bilateral training has proven helpful in recovery of upper limb motor function in these patients. However, studies evaluating the effectiveness of bilateral upper limb robot-assisted training on improving motor function and quality of life in stroke patients are lacking. Quantitative electroencephalography (EEG) is non-invasive, simple, and monitors cerebral cortical activity, which can be used to evaluate the effectiveness of interventions. In this study, EEG was used to evaluate the effect of end-drive bilateral upper extremity robot-assisted training on upper extremity functional recovery in stroke patients.

METHODS

24 stroke patients with hemiplegia were randomly divided into a conventional training (CT, n = 12) group or a bilateral upper limb robot-assisted training (BRT, n = 12) group. All patients received 60 min of routine rehabilitation treatment including rolling, transferring, sitting, standing, walking, etc., per day, 6 days a week, for three consecutive weeks. The BRT group added 30 min of bilateral upper limb robot-assisted training per day, while the CT group added 30 min of upper limb training (routine occupational therapy) per day, 6 days a week, for 3  weeks. The primary outcome index to evaluate upper limb motor function was the Fugl-Meyer functional score upper limb component (FMA-UE), with the secondary outcome of activities of daily living (ADL), assessed by the modified Barthel index (MBI) score. Quantitative EEG was used to evaluate functional brain connectivity as well as alpha and beta power current source densities of the brain.

RESULTS

Significant (p < 0.05) within-group differences were found in FMA-UE and MBI scores for both groups after treatment. A between-group comparison indicated the MBI score of the BRT group was significantly different from that of the CT group, whereas the FMA-UE score was not significantly different from that of the CT group after treatment. The differences of FMA-UE and MBI scores before and after treatment in the BRT group were significantly different as compared to the CT group. In addition, beta rhythm power spectrum energy was higher in the BRT group than in the CT group after treatment. Functional connectivity in the BRT group, under alpha and beta rhythms, was significantly increased in both the bilateral frontal and limbic lobes as compared to the CT group.

CONCLUSIONS

BRT outperformed CT in improving ADL in stroke patients within three months, and BRT facilitates the recovery of upper limb function by enhancing functional connectivity of the bilateral cerebral hemispheres.

CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG

Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.

A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

The role of neurophysiological tools in the evaluation of ischemic stroke evolution: a narrative review

Ischemic stroke is characterized by a complex cascade of events starting from vessel occlusion. The term "penumbra" denotes the area of severely hypo-perfused brain tissue surrounding the ischemic core that can be potentially recovered if blood flow is reestablished. From the neurophysiological perspective, there are local alterations-reflecting the loss of function of the core and the penumbra-and widespread changes in neural networks functioning, since structural and functional connectivity is disrupted. These dynamic changes are closely related to blood flow in the affected area. However, the pathological process of stroke does not end after the acute phase, but it determines a long-term cascade of events, including changes of cortical excitability, that are quite precocious and might precede clinical evolution. Neurophysiological tools-such as Transcranial Magnetic Stimulation (TMS) or Electroencephalography (EEG)-have enough time resolution to efficiently reflect the pathological changes occurring after stroke. Even if they do not have a role in acute stroke management, EEG and TMS might be helpful for monitoring ischemia evolution-also in the sub-acute and chronic stages. The present review aims to describe the changes occurring in the infarcted area after stroke from the neurophysiological perspective, starting from the acute to the chronic phase.

A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures

Pragmatic, objective, and accurate motor assessment tools could facilitate more frequent appraisal of longitudinal change in motor function and subsequent development of personalized therapeutic strategies. Brain functional connectivity (FC) has shown promise as an objective neurophysiological measure for this purpose. The involvement of different brain networks, along with differences across subjects due to age or existing capabilities, motivates an individualized approach towards the evaluation of FC. We advocate the use of EEG-based resting-state FC (rsFC) measures to address the pragmatic requirements. Pertaining to appraisal of accuracy, we suggest using the acquisition of motor skill by healthy individuals that could be quantified at small incremental change. Computer-based tracing tasks are a good candidate in this regard when using spatial error in tracing as an objective measure of skill. This work investigates the application of an individualized method that utilizes Partial Least Squares analysis to estimate the longitudinal change in tracing error from changes in rsFC. Longitudinal data from participants yielded an average accuracy of 98% (standard deviation of 1.2%) in estimating tracing error. The results show potential for an accurate individualized motor assessment tool that reduces the dependence on the expertise and availability of trained examiners, thereby facilitating more frequent appraisal of function and development of personalized training programs.

Cortical connectivity in stroke using signals from resting-state EEG: a review of current literature

INTRODUCTION

Stroke is considered a substantial cause of disability worldwide and many researches are focused on rehabilitative interventions. Functional magnetic resonance imaging studies centered on brain networks after stroke describe affected functional connectivity between areas within the default mode, sensorimotor, visual, fronto-parietal and executive networks. Recent studies renewed the perspective of utilizing electroencephalography to describe markers of cortical activity in stroke and recovery neurophysiological processes.

METHODS

We included in our research studies realized on patients that had an ischemic or hemorrhagic stroke that performed electroencephalography and had an analysis of connectivity indices. Resting-state electroencephalography has the advantage of including patients with any neurological deficit and it is easier to perform than the task-based variant. The changes in resting-state EEG networks after stroke are important to determine a relationship between frequency cortical activity and spatial conformation of a network. From conventional to quantitative EEG analysis in stroke, these techniques are improved with additional brain connectivity tools that lead to a better characterization between injured areas and other intra- and inter-hemispheric areas.

RESULTS

There are studies that underline the disruptions in local networks in a frequency-dependent modality after stroke, while other results are focused on bilateral changes in resting-state cortical networks, independent of the side of the lesions.

CONCLUSIONS

Many studies found alterations in various connectivity measures after stroke with the help of EEG, but the clinical significance of these findings is a field of increasing interest in research area.