Definition of Neurophysiological Indices to Describe and Quantify the Cortical Plasticity Induced by Neuro-Rehabilitation

The general objective of the PhD project was to develop a methodology for the definition and analysis of neurophysiological indices able to provide a stable and reliable measure of changes induced by a rehabilitative intervention in the brain activity and organization, with the aim to: i) provide a neurophysiological description of the modifications subtending a functional recovery; ii) allow the evaluation of the effects of rehabilitation treatments in terms of brain reorganization; iii) describe specific properties in the brain general organization to be correlated with the outcome of the intervention, with possible prognostic/decision support value. For this purpose, the research activity was focused on the development of an approach for the extraction of neurophysiological indices from non-invasive estimation of the cerebral activity and connectivity based on electroencephalographic recordings. Brain activity and its changes in time were investigated at three different interconnected levels: spectral analysis, connectivity estimation and graph theory. For each of these, the state of the art methods were evaluated and methodological advancements were proposed on the basis of open problems presented by the nature of the data and by the clinical problem. Experimental data were acquired from 56 stroke patients subjected to a rehabilitative intervention based on Motor Imagery (MI). A subgroup of randomly selected patients were trained in the MI task with the support of Brain Computer Interface. New spectral and functional indices were defined and computed from EEG recorded during the execution of specific tasks (e.g. motor execution), but also from resting state brain activity, to capture both specific and general brain functional modifications. The application of the developed methods allowed to return a proof of the nature, quality and properties of the brain description and quantitative indices that can be derived from data easily recordable from a wide range of patients

How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes

Small-World Brain Networks Revisited.

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.DSB acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Health (2-R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-MH107703, and R21-M MH-106799), the Office of Naval Research, and the National Science Foundation (BCS-1441502, CAREER PHY-1554488, and BCS-1631550).This is the final version of the article. It first appeared from Sage at http://dx.doi.org/10.1177/1073858416667720

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Emotional valence modulates the topology of the parent-infant inter-brain network.

Emotional communication between parents and children is crucial during early life, yet little is known about its neural underpinnings. Here, we adopt a dual connectivity approach to assess how positive and negative emotions modulate the interpersonal neural network between infants and their mothers during naturalistic interaction. Fifteen mothers were asked to model positive and negative emotions toward pairs of objects during social interaction with their infants (mean age 10.3 months) whilst the neural activity of both mothers and infants was concurrently measured using dual electroencephalography (EEG). Intra-brain and inter-brain network connectivity in the 6-9 Hz range (i.e. infant Alpha band) during maternal expression of positive and negative emotions was computed using directed (partial directed coherence, PDC) and non-directed (phase-locking value, PLV) connectivity metrics. Graph theoretical measures were used to quantify differences in network topology as a function of emotional valence. We found that inter-brain network indices (Density, Strength and Divisibility) consistently revealed strong effects of emotional valence on the parent-child neural network. Parents and children showed stronger integration of their neural processes during maternal demonstrations of positive than negative emotions. Further, directed inter-brain metrics (PDC) indicated that mother to infant directional influences were stronger during the expression of positive than negative emotional states. These results suggest that the parent-infant inter-brain network is modulated by the emotional quality and tone of dyadic social interactions, and that inter-brain graph metrics may be successfully applied to examine these changes in parent-infant inter-brain network topology.UK Economic and Social Research Council (ESRC) Transforming Social Sciences Grant ES/N006461/1 (to V.L. and S.W.), a Nanyang Technological University start-up Grant M4081585.SS0 (to V.L.), a Ministry of Education (Singapore) Tier 1 grant M4012105.SS0 (V.L.) and an ESRC Future Research Leaders Fellowship ES/N017560/1 (to S.W.)

Neuroelectrical Correlates of Trustworthiness and Dominance Judgments Related to the Observation of Political Candidates

The present research investigates the neurophysiological activity elicited by fast observations of faces of real candidates during simulated political elections. We used simultaneous recording of electroencephalographic (EEG) signals as well as galvanic skin response (GSR) and heart rate (HR) as measurements of central and autonomic nervous systems. Twenty healthy subjects were asked to give judgments on dominance, trustworthiness, and a preference of vote related to the politicians’ faces. We used high-resolution EEG techniques to map statistical differences of power spectral density (PSD) cortical activity onto a realistic head model as well as partial directed coherence (PDC) and graph theory metrics to estimate the functional connectivity networks and investigate the role of cortical regions of interest (ROIs). Behavioral results revealed that judgment of dominance trait is the most predictive of the outcome of the simulated elections. Statistical comparisons related to PSD and PDC values highlighted an asymmetry in the activation of frontal cortical areas associated with the valence of the judged trait as well as to the probability to cast the vote. Overall, our results highlight the existence of cortical EEG features which are correlated with the prediction of vote and with the judgment of trustworthy and dominant faces

Συγκριτική ανάλυση μεθόδων μείωσης διάστασης και εκμάθησης πολλαπλοτήτων για την ταξινόμηση δεδομένων Λειτουργικής Απεικόνισης Μαγνητικού Συντονισμού (FMRI)

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Εφαρμοσμένη Μηχανική

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

Statistical causality in the EEG for the study of cognitive functions in healthy and pathological brains

Understanding brain functions requires not only information about the spatial localization of neural activity, but also about the dynamic functional links between the involved groups of neurons, which do not work in an isolated way, but rather interact together through ingoing and outgoing connections. The work carried on during the three years of PhD course returns a methodological framework for the estimation of the causal brain connectivity and its validation on simulated and real datasets (EEG and pseudo-EEG) at scalp and source level. Important open issues like the selection of the best algorithms for the source reconstruction and for time-varying estimates were addressed. Moreover, after the application of such approaches on real datasets recorded from healthy subjects and post-stroke patients, we extracted neurophysiological indices describing in a stable and reliable way the properties of the brain circuits underlying different cognitive states in humans (attention, memory). More in detail: I defined and implemented a toolbox (SEED-G toolbox) able to provide a useful validation instrument addressed to researchers who conduct their activity in the field of brain connectivity estimation. It may have strong implication, especially in methodological advancements. It allows to test the ability of different estimators in increasingly less ideal conditions: low number of available samples and trials, high inter-trial variability (very realistic situations when patients are involved in protocols) or, again, time varying connectivity patterns to be estimate (where stationary hypothesis in wide sense failed). A first simulation study demonstrated the robustness and the accuracy of the PDC with respect to the inter-trials variability under a large range of conditions usually encountered in practice. The simulations carried on the time-varying algorithms allowed to highlight the performance of the existing methodologies in different conditions of signals amount and number of available trials. Moreover, the adaptation of the Kalman based algorithm (GLKF) I implemented, with the introduction of the preliminary estimation of the initial conditions for the algorithm, lead to significantly better performance. Another simulation study allowed to identify a tool combining source localization approaches and brain connectivity estimation able to provide accurate and reliable estimates as less as possible affected to the presence of spurious links due to the head volume conduction. The developed and tested methodologies were successfully applied on three real datasets. The first one was recorded from a group of healthy subjects performing an attention task that allowed to describe the brain circuit at scalp and source level related with three important attention functions: alerting, orienting and executive control. The second EEG dataset come from a group of healthy subjects performing a memory task. Also in this case, the approaches under investigation allowed to identify synthetic connectivity-based descriptors able to characterize the three main memory phases (encoding, storage and retrieval). For the last analysis I recorded EEG data from a group of stroke patients performing the same memory task before and after one month of cognitive rehabilitation. The promising results of this preliminary study showed the possibility to follow the changes observed at behavioural level by means of the introduced neurophysiological indices