M. Molinari

Sensorimotor rhythm-based brain?computer interface training: the impact on motor cortical responsiveness

The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscles cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.

EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb

Brain-Computer Interfaces (BCIs) process brain activity in real time, and mediate non-muscular interaction between and individual and the environment. The subserving algorithms can be used to provide a quantitative measurement of physiological or pathological cognitive processes - such as Motor Imagery (MI) - and feed it back the user. In this paper we propose the clinical application of a BCI-based rehabilitation device, to promote motor recovery after stroke. The BCI-based device and the therapy exploiting its use follow the same principles that drive classical neuromotor rehabilitation, and (i) provides the physical therapist with a monitoring instrument, to assess the patients participation in the rehabilitative cognitive exercise; (ii) assists the patient in the practice of MI. The device was installed in the ward of a rehabilitation hospital and a group of 29 patients were involved in its testing. Among them, eight have already undergone a one month training with the device, as an add-on to the regular therapy. An improved system, which includes analysis of Electromyographic (EMG) patterns and Functional Electrical Stimulation (FES) of the arm muscles, is also under clinical evaluation. We found that the rehabilitation exercise based on BCI mediated neurofeedback mechanisms enables a better engagement of motor areas with respect to motor imagery alone and thus it can promote neuroplasticity in brain regions affected by a cerebrovascular accident. Preliminary results also suggest that the functional outcome of motor rehabilitation may be improved by the use of the proposed device.

Multiscale topological properties of functional brain networks during motor imagery after stroke

In recent years, network analyses have been used to evaluate brain reorganization following stroke. However, many studies have often focused on single topological scales, leading to an incomplete model of how focal brain lesions affect multiple network properties simultaneously and how changes on smaller scales influence those on larger scales. In an EEG-based experiment on the performance of hand motor imagery (MI) in 20 patients with unilateral stroke, we observed that the anatomic lesion affects the functional brain network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of the affected hand (Ahand) elicited a significantly lower smallworldness and local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the abnormal reduction in Eloc significantly depended on the increase in interhemispheric connectivity, which was in turn determined primarily by the rise of regional connectivity in the parieto-occipital sites of the affected hemisphere. Further, in contrast to the Uhand MI, in which significantly high connectivity was observed for the contralateral sensorimotor regions of the unaffected hemisphere, the regions with increased connectivity during the Ahand MI lay in the frontal and parietal regions of the contralaterally affected hemisphere. Finally, the overall sensorimotor function of our patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly predicted by the connectivity of their affected hemisphere. These results improve on our understanding of stroke-induced alterations in functional brain networks.

Brain Computer Interface for Hand Motor Function Restoration and Rehabilitation

Long-term disability is often associated with persistent impairment of an upper limb. In this respect, neurological rehabilitation aims to lessen motor impairment and related disability either by restoring functions with the help of assistive devices to aid daily living activities or by applying rehabilitative protocols based on task-specific training and practice to enhance recovery of motor functions. Brain–computer interface technology is a promising rehabilitation device in every such sense. On the one hand, BCI systems can be utilized to bypass central nervous system injury by controlling neuroprosthetics for patient’s arm to manage reach and grasp functional activities in peripersonal space. On the other, BCI technology can encourage motor training and practice by offering an on-line feedback about brain signals associated with mental practice, motor intention and other neural recruitment strategies, and thus helping to guide neuroplasticity associated with post-stroke motor impairment and its recovery. This chapter aims to provide a focused overview of non invasive-BCI technology advancement to serve patients in the field of restoration and recovery of hand motor function impairment accompanying spinal cord injuries and stroke.

9. Brain network modulation following motor imagery BCI-assisted training after stroke

To evaluate the influence of Motor Imagery (MI) training assisted via Brain Computer Interface (BCI) on brain network organization in subacute stroke patients. We analyzed EEG-derived brain networks estimated before and after two training strategies (with and without BCI support); correlations between connectivity indices and clinical improvement were performed. Twenty-eight subacute stroke patients were enrolled and assigned to two groups: 14 patients underwent a one-month motor imagery (MI) training supported by a sensorimotor–based BCI (BCI group) while 14 underwent a similar MI training without BCI support (CTRL group). Before and after training we recorded EEG from 61 positions during 5xa0min of rest. Effective connectivity was computed by means of Partial Directed Coherence. Paired sample t -tests and Pearson’s Correlation were employed to analyze data (significance was defined by p p R xa0=xa00.568, p xa0=xa00.034). Overall findings indicate that MI training supported via BCI induces a reinforcement of interhemispheric connections related to sensorimotor rhythms; brain connectivity is a promising neurophysiological marker for BCI training efficacy in stroke rehabilitation. This work was partially supported by the European ICT Programme Project FP7-224631 and by the project “Brain Computer Interface-Driven Rehabilitation After Stroke: An Add-On Intervention For Hand Motor Recovery” (RF-2010-2319611) founded by the Italian Ministry of Healthcare.

Brain-Computer Interfaces and Therapy

In recent times the idea that brain–computer interface (BCI) technology can be used to control brain mechanisms to sustain recovery and improve functions has been advanced and tested by different groups. This new development in BCI research and application raises ethical issues quite different from those previously addressed. After describing recent BCI-driven applications in neurological rehabilitation we focus on two main ethical issues stemming from present BCI therapeutic applications, namely the potential occurrence of iatrogenic effects because of potentiating maladaptive circuits and difficulties in addressing cognitive/behavioral performances in an uncontrolled loop.

P13.6 EEG sensorimotor reactivity after stroke: preliminary step to promote brain computer interface technology for rehabilitation

Introduction: Recent research achievements show that neural interfaces with the peripheral nervous system can be used to elicit different types of sensations which can provide proper and natural biofeedback for neural prosthesis control. Objectives: The aim of this work is to provide a comparative analysis of suitability (e.g. selectivity, stability, modulation of sensation) of current peripheral neural electrodes for natural biofeedback delivery. Methods: Cuff electrodes, wire-based and thin-film multicontact intrafascicular electrodes have been included in the present analysis. These devices have been compared in terms of several parameters, such as: implant duration, implanted nerves, contacts number, maximum electrical charge that can be applied to the contacts, stimulation parameters (amplitude, pulse width, frequency), clinical correlated features due to peripheral nerve stimulation (e.g. cortical reorganization). Results: Selectivity of stimulation improves with intrafascicular electrodes, since multicontact cuff electrodes are capable of selective and graded stimulation of nerve fibers closest to the contact, whereas intrafascicular electrodes can ideally interface individual or small groups of axons within a nerve fascicle. Implant of intrafascicular electrodes (both wire-based and thin-film) in upper limb amputees showed successful deliver of natural biofeedback (i.e. tactile and proprioceptive). Thin-film electrodes allow for multiple contacts within a small surface enabling more selective stimulation. Stability of the electrodes is influenced by progressive habituation of the patient to the stimulus and fibrotic tissue reaction around the electrodes. Conclusions: Thin-film multicontact intrafascicular electrodes appears as the most suitable to deliver natural biofeedback for neural prosthesis control; the main challenges to be faced deal with materials and techniques enabling safe and effective chronic stimulation and coatings that reduce fibrotic reaction.

P13.8 Functional brain networks during motor imagery after stroke

In the present study, we propose a methodological approach to assess the functional brain functional network organization underlying motor imagery after stroke. Functional brain connectvity was estimated from high density EEG recorded during motor imagery of hand movements performed with the affected and unaffected hand by a patient affected by unilateral cortical infarction. stroke. The use of a graph theoretical approach allowed the characterizatio of the connectivity pattrens and revealed that a different topological orgainization exists between the affected and unaffected movement imagination. These preliminary findings are promising for the investigation of network plasticiy during post-stroke rehabilitation.