Changes in EEG Power Spectral Density and Cortical Connectivity in Healthy and Tetraplegic Patients during a Motor Imagery Task

Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs) during a foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. The model assumes that each ROI can generate an intrinsic rhythm in the beta range, and receives rhythms in the alpha and gamma ranges from other two regions. Connectivity strengths among the ROIs were estimated by means of an original genetic algorithm that tries to minimize several cost functions of the difference between real and model power spectral densities. Results show that the stronger connections are those from the cingulate cortex to the primary and supplementary motor areas, thus emphasizing the pivotal role played by the CMA_L during the task. Tetraplegic patients exhibit higher connectivity strength on average, with significant statistical differences in some connections. The results are commented and virtues and limitations of the proposed method discussed

Brain-computer interface technology and neuroelectrical imaging to improve motor recovery after stroke

Stroke is defined as a focal lesion in the brain caused by acute ischemia or hemorrhage. The events that characterize acute stroke as well as the spontaneous recovery process occurring in the subacute phase, demonstrate that the focal damage affects remote interconnected areas. On the other hand, interconnected areas largely contribute to reorganization of the central nervous system (CNS) along the recovery process (plasticity) throughout compensatory or restorative mechanisms which can also lead to unwanted effects (maladaptive plasticity). Such post-stroke brain reorganization occurring spontaneously or within a rehabilitation program, is the object of wide literature in the fields of neuroimaging and neurophysiology. Brain-Computer Interfaces (BCIs) allow recognition, monitoring and reinforcement of specific brain activities as recorded eg. via electroencephalogram (EEG) and use such brain activity to control external devices via a computer. Sensorimotor rhythm (SMR) based BCIs exploit the modulation occurring in the EEG in response to motor imagery (MI) tasks: the subject is asked to perform MI of eg. left or right hand in order to control a cursor on a screen. In the context of post-stroke motor rehabilitation, such recruitment of brain activity within the motor system through MI can be used to harness brain reorganization towards a better functional outcome. Since 2009 my research activity has been focused mainly on BCI applications for upper limb motor rehabilitation after stroke within national (Ministry of Health) and international (EU) projects. I conducted (or participated to) several basic and clinical studies involving both healthy subjects and stroke patients and employing a combination of neurophysiological techniques (EEG, transcranial magnetic stimulation – TMS) and BCI technology (De Vico Fallani et al., 2013; Kaiser et al., 2012; Morone et al., 2015; Pichiorri et al., 2011). Such studies culminated in a randomized controlled trial (RCT) conducted on subacute stroke patients in which we demonstrated that a one-month training with a BCI system, which was specifically designed to support upper limb rehabilitation after stroke, significantly improved functional outcome (upper limb motor function) in the target population. Moreover, we observed changes in brain activity and connectivity (from high-density EEG recordings) occurring in motor related frequency ranges that significantly correlated to the functional outcome in the target group (Pichiorri et al., 2015). Following these promising results, my activity proceeded along two main pathways during the PhD course. On one hand, efforts were made ameliorate the prototypal BCI system used in (Pichiorri et al., 2015); the current system (called Promotœr) is an all-in-one BCI training station with several improvements in usability for both the patient and the therapist (it is easier to use, employs wireless EEG system with reduced number of electrodes) (Colamarino et al., 2017a,b). The Promotœr system is currently employed in add-on to standard rehabilitation therapy in patients admitted at Fondazione Santa Lucia. Preliminary results are available on chronic stroke patients, partially retracing those obtained in the subacute phase (Pichiorri et al., 2015) as well as explorative reports on patients with upper limb motor deficit of central origin other than stroke (eg. spinal cord injury at the cervical level). In the last year, I submitted research projects related to the Promotœr system to private and public institutions. These projects foresee i) the addition of a proprioceptive feedback to the current visual one by means of Functional Electrical Stimulation (FES) ii) online evaluation of residual voluntary movement as recorded via electromyography (EMG), and iii) improvements in the BCI control features to integrate concepts derived from recent advancements in brain connectivity. On these themes, I recently obtained a grant from a private Swedish foundation. On the other hand, I conducted further analyses of data collected in the RCT (Pichiorri et al., 2015) to identify possible neurophysiological markers of good motor recovery. Specifically, I focused on interhemispheric connectivity (EEG derived) and its correlation with the integrity of the corticospinal tract (as assessed by TMS) and upper limb function (measured with clinical scales) in subacute stroke patients. The results of these analyses were recently published on an international peer-reviewed journal (Pichiorri et al., 2018). In the first chapter of this thesis, I will provide an updated overview on BCI application in neurorehabilitation (according to the current state-of-the-art). The content of this chapter is part of a wider book chapter, currently in press in Handbook of Clinical Neurology (Pichiorri and Mattia, in press). In the second chapter, I will report on the status of BCI applications for motor rehabilitation of the upper limb according to the approach I developed along my research activity, including ongoing projects and prliminary findings. In the third chapter I will present the results of a neurophysiological study on subacute stroke patients, exploring EEG derived interhemispheric connectivity as a possible neurophysiological correlate of corticospinal tract integrity and functional impairment of the upper limb. Overall this work aims to outline the current and potential role of BCI technology and EEG based neuroimaging in post-stroke rehabilitation mainly in relation to upper limb motor function, nonetheless touching upon possible different applications and contexts in neighboring research fields

HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury

Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 10–20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range

Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

Recent studies on motor imagery (MI)-based brain computer interaction (BCI) reported that the interaction of spatially separated brain areas in forms of functional or effective connectivity leads to a better insight of brain neural patterns during MI movements and can provide useful features for BCIs. However, existing studies suffer from unrealistic assumptions or technical weaknesses for processing brain signals, such as stationarity, linearity and bivariate analysis framework. Besides, volume conduction effect as a critical challenge in this area and the role of subcortical regions in connectivity analysis have not been considered and studied well. In this thesis, the neurophysiological connectivity patterns of healthy human brain during different MI movements are deeply investigated. At first, an adaptive nonlinear multivariate statespace model known as dual extended Kalman filter is proposed for connectivity pattern estimation. Several frequency domain functional and effective connectivity estimators are developed for nonlinear non-stationary signals. Evaluation results show superior parameter tracking performance and hence more accurate connectivity analysis by the proposed model. Secondly, source-space time-varying nonlinear multivariate brain connectivity during feet, left hand, right hand and tongue MI movements is investigated in a broad frequency range by using the developed connectivity estimators. Results reveal the similarities and the differences between MI tasks in terms of involved regions, density of interactions, distribution of interactions, functional connections and information flows. Finally, organizational principles of brain networks of MI movements measured by all considered connectivity estimators are extensively explored by graph theoretical approach where the local and global graph structures are quantified by computing different graph indexes. Results report statistical significant differences between and within the MI tasks by using the graph indexes extracted from the networks formed particularly by normalized partial directed coherence. This delivers promising distinctive features of the MI tasks for non-invasive BCI applications

Fusion of virtual reality and brain-machine interfaces for the assessment and rehabilitation of patients with spinal cord injury

La presente tesis está centrada en la utilización de nuevas tecnologías (Interfaces Cerebro-Máquina y Realidad Virtual). En la primera parte de la tesis se describe la definición y la aplicación de un conjunto de métricas para evaluar el estado funcional de los pacientes con lesión medular en el contexto de un sistema de realidad virtual para la rehabilitación de los miembros superiores. El objetivo de este primer estudio es demostrar que la realidad virtual puede utilizarse, en combinación con sensores inerciales para rehabilitar y evaluar simultáneamente. 15 pacientes con lesión medular llevaron a cabo 3 sesiones con el sistema de realidad virtual Toyra y se aplicó el conjunto definido de métricas a las grabaciones obtenidas con los sensores inerciales. Se encontraron correlaciones entre algunas de las métricas definidas y algunas de las escalas clínicas utilizadas con frecuencia en el contexto de la rehabilitación. En la segunda parte de la tesis se ha combinado una retroalimentación virtual con un estimulador eléctrico funcional (en adelante FES, por sus siglas en inglés Functional Electrical Stimulator), ambos controlados por un Interfaz Cerebro-Máquina (BMI por sus siglas en inglés Brain-Machine Interface), para desarrollar un nuevo tipo de enfoque terapéutico para los pacientes. El sistema ha sido utilizado por 4 pacientes con lesión medular que intentaron mover sus manos. Esta intención desencadenó simultáneamente el FES y la retroalimentación virtual, cerrando la mano de los pacientes y mostrándoles una fuente adicional de retroalimentación para complementar la terapia. Este trabajo es, de acuerdo al estado del arte revisado, el primero que integra BMI, FES y realidad virtual como terapia para pacientes con lesión medular. Se han obtenido resultados clínicos prometedores por 4 pacientes con lesión medular después de realizar 5 sesiones de terapia con el sistema, mostrando buenos niveles de precisión en las diferentes sesiones (79,13% en promedio). En la tercera parte de la tesis se ha definido una nueva métrica para estudiar los cambios de conectividad cerebral en los pacientes con lesión medular, que incluye información de las interacciones neuronales entre diferentes áreas. El objetivo de este estudio ha sido extraer información clínicamente relevante de la actividad del EEG cuando se realizan terapias basadas en BMI

Operant EEG-based BMI: Learning and consolidating device control with brain activity

"Whether you are reading this thesis on paper or screen, it is easy to take for granted all the highly specialized movements you are doing at this very moment just to go through each page. Just to turn a page, you have to reach for and grasp it, turn it and let go at the precise moment not to rip it.(...)

The predictive value of cortical activity during motor imagery for subacute spinal cord injury-induced neuropathic pain

Objective: The aim of this study is to explore whether cortical activation and its lateralization during motor imagery (MI) in subacute spinal cord injury (SCI) are indicative of existing or upcoming central neuropathic pain (CNP). Methods: Multichannel electroencephalogram was recorded during MI of both hands in four groups of participants: able-bodied (N=10), SCI and CNP (N=11), SCI who developed CNP within 6 months of EEG recording (N=10), and SCI who remained CNP-free (N=10). Source activations and its lateralization were derived in four frequency bands in 20 regions spanning sensorimotor cortex and pain matrix. Results: Statistically significant differences in lateralization were found in the theta band in premotor cortex (upcoming vs existing CNP, p=0.036), in the alpha band at the insula (healthy vs upcoming CNP, p=0.012), and in the higher beta band at the somatosensory association cortex (no CNP vs upcoming CNP, p=0.042). People with upcoming CNP had stronger activation compared to those with no CNP in the higher beta band for MI of both hands. Conclusions: Activation intensity and lateralization during MI in pain-related areas might hold a predictive value for CNP. Significance: The study increases understanding of the mechanisms underlying transition from asymptomatic to symptomatic early CNP in SCI

Electroencephalography-based biological and functional characteristics of spinal cord injury patients with neuropathic pain and numbness

ObjectivesTo identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI patients with NP or numbness.Participants and methodsThe EEG signals during rest, as well as left- and right-hand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the θ (4–8 Hz), α (8–12 Hz), and β (13–30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation.ResultsThe specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower θ and α bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all p < 0.05), but higher β band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all p < 0.05). During left-hand and feet MI, in the lower frequency bands (θ and α bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (β band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group.ConclusionThe differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness