Motor imagery and motor illusion: from plasticity to a translational approach

Motor imagery e illusione motoria: dalla plasticit\ue0 ad un approccio traslazional

Frequency‐dependent modulation of neural oscillations across the gait cycle

: Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait-related modulations of neural activity, we collected high-density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event-related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG-based brain-body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders

Sensorimotor inhibition during emotional processing

Visual processing of emotional stimuli has been shown to engage complex cortical and subcortical networks, but it is still unclear how it affects sensorimotor integration processes. To fill this gap, here, we used a TMS protocol named short-latency afferent inhibition (SAI), capturing sensorimotor interactions, while healthy participants were observing emotional body language (EBL) and International Affective Picture System (IAPS) stimuli. Participants were presented with emotional (fear- and happiness-related) or non-emotional (neutral) EBL and IAPS stimuli while SAI was tested at 120 ms and 300 ms after pictures presentation. At the earlier time point (120 ms), we found that fear-related EBL and IAPS stimuli selectively enhanced SAI as indexed by the greater inhibitory effect of somatosensory afferents on motor excitability. Larger early SAI enhancement was associated with lower scores at the Behavioural Inhibition Scale (BIS). At the later time point (300 ms), we found a generalized SAI decrease for all kind of stimuli (fear, happiness or neutral). Because the SAI index reflects integrative activity of cholinergic sensorimotor circuits, our findings suggest greater sensitivity of such circuits during early (120 ms) processing of threat-related information. Moreover, the correlation with BIS score may suggest increased attention and sensory vigilance in participants with greater anxiety-related dispositions. In conclusion, the results of this study show that sensorimotor inhibition is rapidly enhanced while processing threatening stimuli and that SAI protocol might be a valuable option in evaluating emotional-motor interactions in physiological and pathological conditions

Attentional control of gait and falls: Is cholinergic dysfunction a common substrate in the elderly and Parkinson's disease?

The aim of this study was to address whether deficits in the central cholinergic activity may contribute to the increased difficulty to allocate attention during gait in the elderly with heightened risk of falls. We recruited 50 participants with a history of two or more falls (33 patients with Parkinson's Disease and 17 older adults) and 14 non-fallers age-matched adults. Cholinergic activity was estimated by means of short latency afferent inhibition (SAI), a transcranial magnetic stimulation (TMS) technique that assesses an inhibitory circuit in the sensorimotor cortex and is regarded as a global marker of cholinergic function in the brain. Increased difficulty to allocate attention during gait was evaluated by measuring gait performance under single and dual-task conditions. Global cognition was also assessed. Results showed that SAI was reduced in patients with PD than in the older adults (fallers and non-fallers) and in older adults fallers with respect to non-fallers. Reduction in SAI indicates less inhibition i.e., less cholinergic activity. Gait speed was reduced in the dual task gait compared to normal gait only in our faller population and changes in gait speed under dual task significantly correlated with the mean value of SAI. This association remained significant after adjusting for cognitive status. These findings suggest that central cholinergic activity may be a predictor of change in gait characteristics under dual tasking in older adults and PD fallers independently of cognitive status

Neural oscillations during motor imagery of complex gait: an HdEEG study

The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of alpha- and beta-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed alpha and beta bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, alpha- and beta-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found alpha- and beta-band ERDs in the DT compared with the UW condition. Finally, in the beta band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related alpha- and beta-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrates a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks. ispartof: IEEE Open Journal of Engineering in Medicine and Biology status: accepte

Home-based exercise training by using a smartphone app in patients with Parkinson’s disease: a feasibility study

BackgroundParkinson’s disease (PD) patients experience deterioration in mobility with consequent inactivity and worsened health and social status. Physical activity and physiotherapy can improve motor impairments, but several barriers dishearten PD patients to exercise regularly. Home-based approaches (e.g., via mobile apps) and remote monitoring, could help in facing this issue.ObjectiveThis study aimed at testing the feasibility, usability and training effects of a home-based exercise program using a customized version of Parkinson Rehab® application.MethodsTwenty PD subjects participated in a two-month minimally supervised home-based training. Daily session consisted in performing PD-specific exercises plus a walking training. We measured: (i) feasibility (training adherence), usability and satisfaction (via an online survey); (ii) safety; (iii) training effects on PD severity, mobility, cognition, and mood. Evaluations were performed at: baseline, after 1-month of training, at the end of training (T2), and at 1-month follow-up (T3).ResultsEighteen out of twenty participants completed the study without important adverse events. Participants’ adherence was 91% ± 11.8 for exercise and 105.9% ± 30.6 for walking training. Usability and satisfaction survey scored 70.9 ± 7.7 out of 80. Improvements in PD severity, mobility and cognition were found at T2 and maintained at follow-up.ConclusionThe home-based training was feasible, safe and seems to positively act on PD-related symptoms, mobility, and cognition in patients with mild to moderate stage of PD disease. Additionally, the results suggest that the use of a mobile app might increase the amount of daily physical activity in our study population. Remote monitoring and tailored exercise programs appear to be key elements for promoting exercise. Future studies in a large cohort of PD participants at different stages of disease are needed to confirm these findings

Yet another artefact rejection study: An exploration of cleaning methods for biological and neuromodulatory noise

none5Objective. Electroencephalography (EEG) cleaning has been a longstanding issue in the research community. In recent times, huge leaps have been made in the field, resulting in very promising techniques to address the issue. The most widespread ones rely on a family of mathematical methods known as blind source separation (BSS), ideally capable of separating artefactual signals from the brain originated ones. However, corruption of EEG data still remains a problem, especially in real life scenario where a mixture of artefact components affects the signal and thus correctly choosing the correct cleaning procedure can be non trivial. Our aim is here to evaluate and score the plethora of available BSS-based cleaning methods, providing an overview of their advantages and downsides and of their best field of application. Approach. To address this, we here first characterized and modeled different types of artefact, i.e. arising from muscular or blinking activity as well as from transcranial alternate current stimulation. We then tested and scored several BSS-based cleaning procedures on semi-synthetic datasets corrupted by the previously modeled noise sources. Finally, we built a lifelike dataset affected by many artefactual components. We tested an iterative multistep approach combining different BSS steps, aimed at sequentially removing each specific artefactual component. Main results. We did not find an overall best method, as different scenarios require different approaches. We therefore provided an overview of the performance in terms of both reconstruction accuracy and computational burden of each method in different use cases. Significance. Our work provides insightful guidelines for signal cleaning procedures in the EEG related field.noneBarban F.; Chiappalone M.; Bonassi G.; Mantini D.; Semprini M.Barban, F.; Chiappalone, M.; Bonassi, G.; Mantini, D.; Semprini, M

Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging

Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait

Provision of somatosensory inputs during motor imagery enhances learning-induced plasticity in human motor cortex

Abstract Motor learning via physical practice leads to long-term potentiation (LTP)-like plasticity in motor cortex (M1) and temporary occlusion of additional LTP-like plasticity. Motor learning can be achieved through simulation of movement, namely motor imagery (MI). When combined with electrical stimulation, MI influenced M1 excitability to a larger extent than MI itself. We explored whether a training based on the combination of MI and peripheral nerve stimulation (ESMI) modulates M1 LTP-like plasticity inducing retention of a new acquired skill. Twelve subjects mentally performed thumb-index movements, with synchronous electrical nerve stimulation, following an acoustic cue, in order to increase movement speed. Two control groups physically performed or imagined the same number of finger movements following the acoustic cue. After each training session, M1 LTP-like plasticity was assessed by using PAS25 (paired associative stimulation) technique. Performance was tested before and after training and 24 hours after training. Results showed that physical practice and ESMI training similarly increased movement speed, prevented the subsequent PAS25-induced LTP-like plasticity, and induced retention of motor skill the following day. Training with MI had significant, but minor effects. These findings suggest that a training combining MI with somatosensory input influences motor performance through M1 plasticity similarly to motor execution