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

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

Exploring the combined use of electrical and hemodynamic brain activity to investigate brain function

This thesis explored the relationship between electrical and metabolic aspects of brain functioning in health and disease, measured with QEEG and NIRS, in order to evaluate its clinical potential. First the limitations of NIRS were investigated, depicting its susceptibility to different types of motion artefacts and the inability of the CBSI-method to remove them from resting state data. Furthermore, the quality of the NIRS signals was poor in a significant portion of the investigated sample, reducing clinical potential. Different analysis methods were used to explore both EEG and NIRS, and their coupling in an eyes open eyes closed paradigm in healthy participants. It could be reproduced that during eyes closed blocks less HbO2 (p = 0.000), more Hbb (p = 0.008), and more alpha activity (p = 0.000) was present compared to eyes open blocks. Furthermore, dynamic cross correlation analysis reproduced a positive correlation between alpha and Hbb (r: 0.457 and 0.337) and a negative correlation between alpha and HbO2 (r: -0.380 and -0.366) with a delayed hemodynamic response (7 to 8s). This was only possible when removing all questionable and physiological illogical data, suggesting that an 8s hemodynamic delay might not be the golden standard. Also the inability of the cross correlation to take non-linear relationships into account may distort outcomes. Therefore, In chapter 5 non-linear aspects of the relationship were evaluated by introducing the measure of relative cross mutual information. A newly suggested approach and the most valuable contribution of the thesis since it broadens knowledge in the fields of EEG, NIRS and general time series analysis. Data of two stroke patients then showed differences from the healthy group between the coupling of EEG and NIRS. The differences in long range temporal correlations (p= 0.000 for both cases), entropy (p< 0.040 and p =0.000), and relative cross mutual information (p < 0.003 and p < 0.013) provide the proof of principle that these measures may have clinical utility. Even though more research is necessary before widespread clinical use becomes possible

Dynamics of large-scale electrophysiological networks: a technical review

For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography / electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity

Studying connectivity in the neonatal EEG

In humans the few months surrounding birth comprise a developmentally critical period characterised by the growth of major neuronal networks as well as their initial tuning towards more functionally mature large-scale constellations. Proper wiring in the neonatal brain, especially during the last trimester of pregnancy and the first weeks of postnatal life, relies on the brain’s endogenous activity and remains critical throughout one’s life. Structural or functional abnormalities at the stage of early network formation may result in a neurological disorder later during maturation. Functional connectivity measures based on an infant electroencephalographic (EEG) time series may be used to monitor these processes. A neonatal EEG is temporally discrete and consists of events (e.g., spontaneous activity transients (SATs)) and the intervals between them (inter-SATs). During early maturation, communication between areas of the brain may be transmitted through two distinct mechanisms: synchronisation between neuronal oscillations and event co-occurrences. In this study, we proposed a novel algorithm capable of assessing the coupling on both of these levels. Our analysis of real data from preterm neonates using the proposed algorithm demonstrated its ability to effectively detect functional connectivity disruptions caused by brain lesions. Our results also suggest that SAT synchronisation represents the dominant means through which inter-areal cooperation occurs in an immature brain. Structural disturbances of the neuronal pathways in the brain carry a frequency selective effect on the functional connectivity decreasing at the event level. Next, we used mathematical models and computational simulations combined with real EEG data to analyse the propagation of electrical neuronal activity within the neonatal head. Our results show that the conductivity of the neonatal skull is much higher than that found in adults. This leads to greater focal spread of cortical signals towards the scalp and requires high-density electrode meshes for quality monitoring of neonatal brain activity. Additionally, we show that the specific structure of the neonatal skull fontanel does not represent a special pathway for the spread of electrical activity because of the overall high conductivity of the skull. Finally, we demonstrated that the choice of EEG recording montage may strongly affect the fidelity of non-redundant neuronal information registration as well as the output of functional connectivity analysis. Our simulations suggest that high-density EEG electrode arrays combined with mathematical transformations, such as the global average or current source density (CSD), provide more spatially accurate details about the underlying cortical activity and may yield results more robust against volume conduction effects. Furthermore, we provide clear instruction regarding how to optimise recording montages for different numbers of sensors.Lähikuukaudet ennen ja jälkeen syntymää ovat ihmisillä hermoston kehityksen kannalta kriittisiä vaiheita, joita luonnehtii mittavien hermostollisten verkostojen kasvu sekä niiden alustava virittäytyminen toiminnallisesti kypsiksi suuren mittakaavan yhteenliittymiksi. Vastasyntyneen aivojen koko loppuelämään vaikuttavien hermoverkostojen muodostuminen määräytyy ensimmäisten syntymän jälkeisten viikkojen mutta erityisesti raskauden viimeisen kolmanneksen aikaisen aivojen sisäsyntyisen aktiivisuuden mukaan. Rakenteelliset tai toiminnalliset epämuodostumat näiden varhaisten hermoverkostojen muodostumisvaiheessa voi johtaa neurologisiin häiriöihin myöhemmässä kypsymisessä. Varhaisen kehityksen vaiheita voidaan valvoa vastasyntyneillä mittaamalla hermoyhteyksien toiminnallista liittyvyyttä aivosähkökäyrien (EEG) aikasarjojen avuilla. Vastasyntyneen aivosähkökäyrä on ajallisesti epäjatkuva ja koostuu lyhytkestoisista spontaanin aktiivisuuden tapahtumista, SATeista (Spontaneous Activity Transients) sekä niiden välisistä ajanhetkistä, inter-SATeista. Varhaisessa hermostollisessa kypsymisessä aivoalueiden välinen yhteydenpito voi tapahtua kahdella eri mekanismilla: hermostollisten oskillaatioiden välisellä synkronisaatioilla ja tapahtumien samanaikaisuudella. Tässä tutkimuksessa me kehitimme uuden matemaattisen mallin, algoritmin, jolla voi arvioida näiden kahden mekanismin välistä kytkeytymistä. Vastasyntyneiden keskosten mittausdataan pohjautuva analyysimme osoitti, että kehittämämme algoritmi on toimiva väline aivovaurioiden aiheuttamien toiminnallisten liittyvyyskatkoksien havaitsemisessa. Tuloksemme osoittavat myös, että SAT-synkronisaatio on aivoalueiden pääasiallisin yhteydenpitokeino kypsymättömissä vastasyntyneen aivoissa. Hermostollisten yhteyksien rakenteelliset epämuodostumat heikentävät toiminnallista liittyvyyttä taajuuskohtaisesti tapahtumatasolla. Seuraavaksi me käytimme matemaattisia malleja ja tietokonesimulaatioita yhdistettynä varsinaiseen EEG-mittausdataan analysoidaksemme sähköisen hermostollisen aktiivisuuden leviämistä vastasyntyneen päässä. Tulostemme mukaan vastasyntyneen kallon sähkönjohtavuus on paljon korkeampi kuin aikuisilla ihmisillä. Tämä aiheuttaa aivokuoren signaalien suurempaa paikallista leviämistä päänahkaa kohti, minkä takia vastasyntyneen aivoaktiivisuuden luotettava rekisteröinti vaatii enemmän ja tiheämmin mittauselektrodeja kuin aikuisilla. Lisäksi todistimme, että vastasyntyneen kallon aukileet eivät muodosta erityistä reittiä sähköisen aktiivisuuden leviämiselle, kallon suuren johtavuuden takia. Lopuksi osoitimme, että EEG-mittausasetelman valinta voi vahvasti vaikuttaa ei-päällekkäisen hermostollisen datan mittaustarkkuuteen ja sitä seuraaviin liittyvyysanalyyseihin. Simulaatiomme mukaan suuritiheyksinen EEG-mittauselektrodiasetelma yhdistettynä matemaattisiin muunnoksiin, kuten virtalähdetiheyden (Current Source Density) globaalikeskiarvoon, tarjoavat spatiaalisesti tarkkoja yksityiskohtia alla sijaitsevasta aivokuoren aktiivisuudesta ja voi erottaa selkeästi sekundääristen virtatihentymien osuuden. Lisäksi laadimme selkeät ohjeet kuinka optimoida mittausasetelma eri elektrodimäärille

Patient centric intervention for children with high functioning autism spectrum disorder. Can ICT solutions improve the state of the art ?

In my PhD research we developed an integrated technological platform for the acquisition of neurophysiologic signals in a semi-naturalistic setting where children are free to move around, play with different objects and interact with the examiner. The interaction with the examiner rather than with a screen is another very important feature of the present research, and allows recreating a more real situation with social interactions and cues. In this paradigm, we can assume that the signals acquired from the brain and the autonomic system, are much more similar to what is generated while the child interacts in common life situations. This setting, with a relatively simple technical implementation, can be considered as one step towards a more behaviorally driven analysis of neurophysiologic activity. Within the context of a pilot open trial, we showed the feasibility of the technological platform applied to the classical intervention solutions for the autism. We found that (1) the platform was useful during both children-therapist interaction at hospital as well as children-parents interaction at home, (2) tailored intervention was compatible with at home use and non-professional therapist/parents. Going back to the title of my thesis: 'Can ICT solution improve the state-of-the-art ?' the answer could be: 'Yes it can be an useful support for a skilled professional in the field of autis