With or without spikes: localization of focal epileptic activity by simultaneous electroencephalography and functional magnetic resonance imaging

In patients with medically refractory focal epilepsy who are candidates for epilepsy surgery, concordant non-invasive neuroimaging data are useful to guide invasive electroencephalographic recordings or surgical resection. Simultaneous electroencephalography and functional magnetic resonance imaging recordings can reveal regions of haemodynamic fluctuations related to epileptic activity and help localize its generators. However, many of these studies (40-70%) remain inconclusive, principally due to the absence of interictal epileptiform discharges during simultaneous recordings, or lack of haemodynamic changes correlated to interictal epileptiform discharges. We investigated whether the presence of epilepsy-specific voltage maps on scalp electroencephalography correlated with haemodynamic changes and could help localize the epileptic focus. In 23 patients with focal epilepsy, we built epilepsy-specific electroencephalographic voltage maps using averaged interictal epileptiform discharges recorded during long-term clinical monitoring outside the scanner and computed the correlation of this map with the electroencephalographic recordings in the scanner for each time frame. The time course of this correlation coefficient was used as a regressor for functional magnetic resonance imaging analysis to map haemodynamic changes related to these epilepsy-specific maps (topography-related haemodynamic changes). The method was first validated in five patients with significant haemodynamic changes correlated to interictal epileptiform discharges on conventional analysis. We then applied the method to 18 patients who had inconclusive simultaneous electroencephalography and functional magnetic resonance imaging studies due to the absence of interictal epileptiform discharges or absence of significant correlated haemodynamic changes. The concordance of the results with subsequent intracranial electroencephalography and/or resection area in patients who were seizure free after surgery was assessed. In the validation group, haemodynamic changes correlated to voltage maps were similar to those obtained with conventional analysis in 5/5 patients. In 14/18 patients (78%) with previously inconclusive studies, scalp maps related to epileptic activity had haemodynamic correlates even when no interictal epileptiform discharges were detected during simultaneous recordings. Haemodynamic changes correlated to voltage maps were spatially concordant with intracranial electroencephalography or with the resection area. We found better concordance in patients with lateral temporal and extratemporal neocortical epilepsy compared to medial/polar temporal lobe epilepsy, probably due to the fact that electroencephalographic voltage maps specific to lateral temporal and extratemporal epileptic activity are more dissimilar to maps of physiological activity. Our approach significantly increases the yield of simultaneous electroencephalography and functional magnetic resonance imaging to localize the epileptic focus non-invasively, allowing better targeting for surgical resection or implantation of intracranial electrode array

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

EEG and fMRI are important tools in cognitive and clinical neuroscience. Combined EEGfMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological-haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals, and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (“EEG-fMRI mapping”), or exploring a range of EEGderived quantities to determine which best explain co-localised BOLD fluctuations (“local EEG-fMRI coupling”). While reviewing studies of different forms of brain activity (epileptic and non-epileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG-fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations

Neuroimaging of Epilepsy: Lesions, Networks, Oscillations

While analysis and interpretation of structural epileptogenic lesion is an essential task for the neuroradiologist in clinical practice, a substantial body of epilepsy research has shown that focal lesions influence brain areas beyond the epileptogenic lesion, across ensembles of functionally and anatomically connected brain areas. In this review article, we aim to provide an overview about altered network compositions in epilepsy, as measured with current advanced neuroimaging techniques to characterize the initiation and spread of epileptic activity in the brain with multimodal noninvasive imaging techniques. We focus on resting-state functional magnetic resonance imaging (MRI) and simultaneous electroencephalography/fMRI, and oppose the findings in idiopathic generalized versus focal epilepsies. These data indicate that circumscribed epileptogenic lesions can have extended effects on many brain systems. Although epileptic seizures may involve various brain areas, seizure activity does not spread diffusely throughout the brain but propagates along specific anatomic pathways that characterize the underlying epilepsy syndrome. Such a functionally oriented approach may help to better understand a range of clinical phenomena such as the type of cognitive impairment, the development of pharmacoresistance, the propagation pathways of seizures, or the success of epilepsy surgery

Topographic Electrophysiological Signatures of fMRI Resting State Networks

Background: fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG. Methodology/Principal Findings: In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band. Conclusions/Significance: Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signature

The EEG in acute ischaemic cerebrovascular disease

The electroencephalogram (EEG) is a neurophysiological technique with high temporal resolution and sensibility in the evaluation of brain function in real time. Besides this, EEG is the gold standard for the identification of epileptogenesis and ictogenesis biomarkers. Epileptic seizures and Cerebrovascular disease are two of the most frequent neurological disorders imposing important mutual challenges. Furthermore, in recent years, stroke care has evolved remarkably and, facing a new paradigm of acute standard of care (centred on multidisciplinary Stroke Units), epileptic seizures (as stroke complications) deserve to be rethought. The EEG is an essential neurophysiological exam in the evaluation of patients with epileptic seizures, status epilepticus and/or epilepsy, both for diagnosis and classification, as well as for the establishment of a correct treatment or outcome prediction. Furthermore, EEG has been previously used in cerebrovascular disease with different purposes. However, its clinical usefulness in the differential diagnosis of transient neurological symptoms, specifically in the differentiation between a transient ischaemic attack and some epileptic seizures, and also in the diagnosis or prediction of post-stroke seizures or in post-stroke prognosis prediction, remains uncertain. In this work, we aim to use the clinical model of acute ischaemic cerebrovascular disease to study the value of EEG in the differential diagnosis of transient neurological symptoms, in the diagnosis and prediction of post-stroke seizures and epilepsy, as well as to analyse if electroencephalographic abnormalities and/or epileptic seizures are independent predictors of an anterior circulation ischaemic stroke outcome. Furthermore, since the gold standard of acute stroke care (namely intravenous alteplase treatment) is associated with a reduction of mortality and incapacity of treated patients with possible consequences in post-stroke seizure frequency, but a pro-convulsive and an epileptogenic effect of alteplase has also been described, we aim to test the hypothesis that ischaemic stroke patients treated with intravenous alteplase have a different frequency of epileptic (clinic and/or electroencephalographic) manifestations compared to non-treated patients. Different research methodologies were used in this thesis. A systematic review and meta-analysis of observational studies was performed to evaluate both the frequency of post-stroke (ictal and interictal) epileptiform activity in the EEG, and the quality of studies about this subject. Furthermore, different types of observational studies (including clinical case report, case series and cohort studies) were completed to answer relevant clinical questions. We performed a prospective longitudinal study of possible transient ischaemic attacks (TIA) patients evaluated at a tertiary centre during 36 months, with 1-3 months follow-up and also of acute anterior circulation ischaemic stroke patients, consecutively admitted to a Stroke Unit over 24 months and followed-up for one year. In both studies, patients underwent standardized clinical, diagnostic and neurophysiological assessment. A short duration (≤60 minutes) video-EEG protocol with an extended montage including 64 EEG, two electrooculogram, one electrocardiogram and at least one electromyogram channel was established. Different electroencephalographic investigation technics including visual, back-average and quantitative analysis were used in the clinical workup of patients with possible and definite, transient and established, cerebrovascular disease as tools for the differential diagnosis and for brain functional assessment, concerning not only epileptic manifestations detection and prediction but also to search for predictors of ischaemic stroke functional outcome and vital prognosis. Although epileptic seizures were the most frequent defined final diagnosis (n=13; 16.3%) in our series of 80 patients with difficult-to-diagnose transient neurological symptoms, visual inspection of EEG supported this diagnosis only in 7.5% (n=6) of patients with possible TIA. Moreover, the majority (n=6; 53.8%) of patients with the final diagnosis of epileptic seizures did not have interictal epileptiform activity in an early EEG. Furthermore, early focal slow wave activity, the most frequent EEG abnormality in this patient’s series, did not distinguished between TIA and seizure patients. Our systematic review and random-effects meta-analysis showed that the pooled frequency of post-stroke ictal and interictal epileptiform activity was 7% (95%CI: 3%-12%) and 8% (95%CI: 4%-13%) respectively. Only 2 out of 17 included studies (11.7%) attained the maximum quality score. Moreover, no study exclusively enrolled ischaemic stroke patients, highlighting the need for higher quality studies in the evaluation of epileptiform activity frequency in this type of cerebrovascular disease. Furthermore, due to detection bias, it was not possible to correlate clinical and electrographic seizures. In our prospective cohort of 151 anterior circulation acute stroke patients, we identified different post-stroke, clinical and electroencephalographic, epileptic manifestations including 22.7% (5/22) of acute symptomatic seizures that were exclusively electrographic and therefore could not otherwise be recognised. Furthermore, only EEG back-average analysis allowed the diagnosis of cortical myoclonus during intravenous alteplase perfusion in one clinical vignette included in this work and the recognition of epilepsia partialis continua as a chronic complication of this stroke type in 1.7% of patients. This original work also showed that studied clinical and EEG epileptic manifestations were not significantly different between intravenous alteplase treated and non-treated patients. This thesis work established which abnormalities of an early EEG after acute stroke (background activity asymmetry and the presence of interictal epileptiform activity) are independent predictors of epilepsy in the year after (even when adjusted for clinical and imaging stroke severity). Besides this, early (within the first 72h) post-stroke EEG features, extracted from visual (background activity diffuse slowing and asymmetry) and quantitative (such as delta-theta to alpha-beta ratio and alfa relative power) analysis were recognized as independent predictors of death or functional dependency, at hospital discharge and at 12 months after stroke. Furthermore, outcome models that incorporate delta-theta to alpha-beta ratio or alpha relative power were better than models based exclusively on clinical and imaging-related ischaemic stroke severity at hospital admission. Additionally, post-stroke acute symptomatic seizures and epilepsy were independently associated to death and to an unfavourable outcome 1 year after an acute anterior circulation ischaemic stroke, respectively. Globally, these research projects have shown the value of EEG in the current paradigm of stroke patient’s care. Furthermore, they expand the knowledge both about the EEG role as a complementary neurophysiological tool in general Neurology and about different aspects of the diagnosis and outcome of two of the most prevalent neurological disorders, Cerebrovascular Diseases and Epilepsy, in particular. Beyond the value of specific results, with this work several other research questions about EEG and seizures in ischaemic cerebrovascular disease emerge. Therefore, new possibilities of future research, ideally multicentric, clinical or translational arise

A multimodal imaging approach for quantitative assessment of epilepsy

Le tecniche di coregistrazione elettroencefalogramma-risonanza magnetica funzionale (EEG-fMRI) ed EEG ad alta densità (hdEEG) consentono di mappare attivazioni cerebrali anomale evocate da processi epilettici. L’EEG-fMRI è una tecnica di imaging non invasivo che permette la localizzazione delle variazioni del livello di ossigenazione nel sangue presente nelle regioni irritative (segnale BOLD). Diversamente, l’analisi di sorgente stima, a partire da un potenziale elettrico misurato sullo scalpo (EEG), la densità di corrente della sorgente elettrica a livello corticale producendo una plausibile localizzazione del dipolo nelle regioni irritative. Lo scopo di questa tesi è quello di sviluppare un approccio multimodale attraverso l’uso di dati di coregistrazione EEG-fMRI e hdEEG al fine di localizzare l’attività epilettica e verificare l’affidabilità sia dell’attivazione BOLD che della localizzazione della sorgente. Nel Capitolo I si introduce il concetto di approccio multimodale. Il capitolo è suddiviso principalmente in due parti: la prima descrive la tecnica di coregistrazione EEG-fMRI e la seconda la tecnica di localizzazione della sorgente in epilessia. La prima parte consiste in una breve analisi delle basi fisiologiche del dato di coregistrazione EEG-fMRI, nella descrizione di tecniche di registrazione simultanea e nell’introduzione del metodo convenzionale di analisi dei dati. Sono inoltre descritti problemi tecnici, problemi di sicurezza, modalità di scansione e strategie di rimozione degli artefatti EEG. È quindi presentata una panoramica sullo stato dell’arte delle coregistrazioni EEG-fMRI con discussione dei problemi aperti riguardanti l’analisi convenzionale. La seconda parte introduce i principi di base della stima delle sorgenti da dati hdEEG ed i loro limiti. Il primo capitolo fornisce un quadro generale, mentre i due capitoli successivi sono dedicati ad introdurre approcci di tipo diverso. Nell’analisi convenzionale di dati EEG-fMRI, l’apparizione di eventi interictali (IED) guida l’analisi dei dati fMRI. Il neurologo identifica gli intervalli degli eventi IED, che sono rappresentati da un’onda quadra, e successivamente questo protocollo viene convoluto con una risposta emodinamica (HRF) canonica per la costruzione di un modello o regressore da impiegare nell’analisi con modelli lineari generalizzati (GLM). I problemi principali dell’analisi convenzionale consistono nel fatto che essa non è automatica, ossia soffre di soggettività nella classificazione degli IED, e che, se la scelta dell’HRF non è ottimale, l’attivazione può essere sovra o sotto stimata. Il nuovo metodo proposto integra nell’analisi GLM convenzionale due nuove funzioni: il regressore basato sul segnale EEG (Capitolo II), e l’individuazione di una risposta emodinamica individual-based (ibHRF) (Capitolo III). Nel Capitolo IV le prestazioni del nuovo metodo per l’analisi di dati EEG-fMRI sono validate su dati in silico. A questo scopo sono stati creati dati fMRI simulati per testare la scelta dell’HRF ottima tra cinque modelli: quattro standard ed un modello HRF individual-based. Le prestazioni del metodo sono state valutate utilizzando come selezione il criterio di Akaike. Le simulazioni dimostrano la superiorità del nuovo metodo rispetto a quelli convenzionali e mostrano come la variazione del modello HRF influisce sui risultati dell’analisi statistica. Il Capitolo V introduce un criterio automatico volto a separare le componenti del segnale fMRI relative a network interni dal rumore. Dopo il processo di decomposizione probabilistico delle componenti indipendenti (PICA), si seleziona il numero ottimale di componenti applicando un nuovo algoritmo che tiene conto, per ciascuna componente, dei valori medi delle mappe spaziali di attivazione seguito da passaggi di clustering, segmentazione ed analisi spettrale. Confrontando i risultati dell’identificazione visiva dei network neuronali con i risultati di quella automatica, l’algoritmo mostra elevata accuratezza e precisione. In questo modo, il metodo di selezione automatica permette di separare ed individuare i network in stato di riposo, riducendo la soggettività nella valutazione delle componenti indipendenti. Nel Capitolo VI sono descritti il design sperimentale e l’analisi dei dati reali. Il capitolo illustra i risultati di dodici pazienti epilettici, concentrandosi sull’attività BOLD, sulla localizzazione della sorgente e sulla concordanza con il quadro clinico del paziente. Lo scopo è quello di applicare un approccio multimodale che combini tecniche non invasive di acquisizione ed analisi. Sequenze di EEG standard e fMRI sono acquisite nel corso della stessa sessione di scansione. L’analisi dei dati EEG-fMRI è eseguita utilizzando l’approccio GLM tradizionale, il nuovo approccio e l’analisi PICA. La sorgente dell’attività epilettica è stimata a partire da tracciati EEG a 256-canali. L’attivazione BOLD è confrontata con la ricostruzione della sorgente EEG. Questi risultati sono infine confrontati con l’attività epilettica definita da EEG standard ed esiti clinici. La combinazione di tecniche multimodali ed i loro rispettivi metodi di analisi sono strumenti utili per creare un workup prechirurgico completo dell’epilessia, fornendo diversi metodi di localizzazione dello stesso focolaio epilettico. L’approccio non invasivo di integrazione multimodale di dati EEG-fMRI e hdEEG sembra essere uno strumento molto promettente per lo studio delle scariche epilettiche.Electroencephalography-functional magnetic resonance imaging (EEG-fMRI) coregistration and high density EEG (hdEEG) can be combined to noninvasively map abnormal brain activation elicited by epileptic processes. EEG-fMRI can provide information on the pathophysiological processes underlying interictal activity, since the hemodynamic changes are a consequence of the abnormal neural activity generating interictal epileptiform discharges (IEDs). The source analysis estimates the current density of the source that generates a measured electric potential and it yields a plausible dipole localization of irritative regions. The aim of this thesis is to develop a multimodal approach with hdEEG and EEG-fMRI coregistration in order to localize the epileptic activity and to verify the reliability of source localization and BOLD activation. In Chapter I the multimodal approach is introduced. The chapter is divided in two main parts: the first is based on EEG-fMRI coregistration and the second on the source localization in epilepsy. The first part consists of a brief review of the physiologic basis of EEG and fMRI and the technical basics of simultaneous recording, examining the conventional method for EEG-fMRI data. Technical challenges, safety issues, scanning modalities and EEG artifact removal strategies are also described. An overview of the state of EEG-fMRI is presented and the open problems of conventional analysis are discussed. The second part introduces the basic principles of the source estimation from EEG data in epilepsy and their limitations. The first chapter provides a general framework. The next two are devoted to introduce different approaches. Conventional analysis of EEG-fMRI data relies on spike-timing of epileptic activity: the neurologist identifies the intervals of the IEDs events, as represented by a square wave; this protocol is then convolved with a canonical hemodynamic response function (HRF) to construct a model for the general linear model (GLM) analysis. There are limitations to the technique, however. The conventional analysis is not automatic, suffers of subjectivity in IEDs classification, and using a suboptimal HRF to model the BOLD response the activation map may result over or under estimated. The novel method purposed integrates in the conventional GLM two new features: the regressor based on the EEG signal (Chapter II) and the individual-based hemodynamic response function (ibHRF) (Chapter III). In Chapter IV the performance of the novel method of EEG-fMRI data was tested on in silico data. Simulated fMRI datasets were created and used for the choice of the optimal HRF among five models: four standard and an individual-based HRF models. The performance of the method was evaluated using the Akaike information criterion as selection. Simulations would demonstrate the superiority of the novel method compared with the conventional ones and assess how the variations in HRF model affect the results of the statistical analysis. Chapter V introduces an automatic criterion aiming to separate in fMRI data the signal related to an internal network from the noise. After the decomposition process (probabilistic independent component analysis [PICA]), the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, the mean values of the spatial activation maps followed by clustering, segmentation and spectral analysis steps. Comparing visual and automatic identification of the neuronal networks, the algorithm demonstrated high accuracy and precision. Thus, the automatic selection method allows to separate and detect the resting state networks reducing the subjectivity of the independent component assessment. In Chapter VI experimental design and analysis on real data are described. The chapter focuses on BOLD activity, source localization and agreement with the clinical history of twelve epileptic patients. The scope is to apply a multimodal approach combining noninvasive techniques of acquisition and analysis. Standard EEG and fMRI data were acquired during a single scanning session. The analysis of EEG-fMRI data was performed by using both the conventional GLM, the new GLM and the PICA. Source localization of IEDs was performed using 256-channels hdEEG. BOLD localizations were then compared to the EEG source reconstruction and to the expected epileptic activity defined by standard EEG and clinical outcome. The combination of multimodal techniques and their respectively methods of analysis are useful tools in the presurgical workup of epilepsy providing different methods of localization of the same epileptic foci. Furthermore, the combined use of EEG-fMRI and hdEEG offers a new and more complete approach to the study of epilepsy and may play an increasingly important role in the evaluation of patients with refractory focal epilepsy

Imaging brain networks in focal epilepsy: a prospective study of the clinical application of simultaneous EEG-fMRI in pre-surgical evaluation

Epilepsy is a common disorder with significant associated morbidity and mortality. Despite advances in treatment, there remain a minority of people with pharmacoresistant focal epilepsy for whom surgery may be beneficial. It has been suggested that not enough people are offered surgical treatment, partly owing to the fact that current non-invasive techniques do not always adequately identify the seizure onset zone so that invasive EEG is required. EEG-fMRI is an imaging technique, developed in the 1990s (Ives, Warach et al. 1993) which identifies regions of interictal epileptiform discharge associated haemodynamic changes, that are concordant with the seizure onset zone in some patients (Salek-Haddadi, Diehl et al. 2006). To date there has been no large scale prospective comparison with icEEG and postoperative outcome. This thesis presents a series of experiments, carried out in a cohort of patients scanned using EEG-fMRI as part of a multi-centre programme, designed to investigate the relationship between EEG-fMRI and intracranial EEG and to assess its potential role in pre-surgical evaluation of patients with focal epilepsy. The results suggested that positive, localised IED-related BOLD signal changes were sensitive for the seizure onset zone, as determined on icEEG, both in patients neocortical epilepsies, but were not predictive of outcome. Widespread regions of positive IEDrelated BOLD signal change were associated with widespread or multifocal abnormalities on icEEG and poor outcome. Patterns of haemodynamic change, identified using both data driven and EEG derived modeling approaches, correspond to regions of seizure onset on icEEG, but improvements for modeling seizures are required. A study of a single seizure in a patient who underwent simultaneous icEEGfMRI, showed similar findings.. An exploratory investigation of fMRI-DCM in EEG-fMRI, suggested it can provide information about seizure propagation and this opens new avenues for the non-invasive study of the epileptic network and interactions with function

Multimodal functional neuroimaging of epilepsy and Pain

University of Minnesota Ph.D. dissertation.June 2015. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); vi, 139 pages.The overall goal of this thesis work is to use advanced noninvasive neuroimaging modalities and techniques to study the underlying neurological mechanism of both diseased and healthy brains. The two main applications of this work are for the diagnosis of epilepsy and management of pain. Epilepsy is one of the most prevalent neurological disorders. It affects an estimated 2.7 million Americans. There are two broad types of epilepsies: partial and generalized epilepsy. For patients with drug resistant focal epilepsy, which account for one third of the patient population, surgical resection may provide the opportunity of seizure control. Existing presurgical planning methods are not only invasive in nature; they may also fail to provide additional information needed for surgery due to the relatively limited spatial coverage. On the other hand, idiopathic generalized epilepsy (IGE), unlike focal or partial epilepsy, often affects the whole or a larger portion of the brain without obvious, known cause. Treatment options are more restricted as resection is not a choice. Therefore, it is important to understand the underlying network which generates epileptic activity and through which epileptic activity propagates. The aim of the present study in the epilepsy portion was to use noninvasive imaging techniques including fMRI and EEG to localize epileptic areas for the purpose of assisting surgical planning in the focal epilepsy cases; and to improve our understanding the underlying mechanism of generalized epilepsy, thalamocortical relationship in the IGE cases. Chronic Pain is one of the biggest medical burdens in developed countries, affecting 20% of adult population with estimated economic cost in the United States alone over $150 billion. Functional imaging of brain networks associated with pain processing is of vital importance to aid developing new pain-relief therapies and to better understand the mechanisms of pain perception. The long-term goal of this project is to study the neurological mechanism of subjective perception of pain using non-invasive neuroimaging methods. In the present work of the pain portion, changes brain activities in healthy subjects experiencing sustained external painful stimuli were first studied. Neural activities in patient with sickle cell disease, who often surfer spontaneous acute or chronic pain as one of the comorbidities of the disease, were contrasted with healthy controls to study changes in neural network as a result of prolonged exposure to internal In summary, the present dissertation research developed and evaluated the spatiotemporal imaging approaches for the non-invasive mapping of network activities in the diseased and normal brain. Evaluations were conducted in patient and healthy control groups in order to test the clinical applicability of such a pre-surgical noninvasive imaging tool. An investigation has been conducted to study the widespread GSWDs of generalized epilepsy patients. The spatial resolution has been further improved by adding the component of fMRI through an EEG-fMRI integrated imaging framework. For the application in pain study, two investigations were conducted to study changes in network level activity due to external pain in healthy subjects and spontaneous pain in patients with SCD. All of the results that were obtained suggest the importance of noninvasive spatiotemporal neuroimaging approaches for solving clinical problems and for investigating neuroscience questions. Furthermore, an improved understanding of neurological diseases and their mechanisms would help us to develop and deliver curative treatments of neurological diseases

Imaging functional and structural networks in the human epileptic brain

Epileptic activity in the brain arises from dysfunctional neuronal networks involving cortical and subcortical grey matter as well as their connections via white matter fibres. Physiological brain networks can be affected by the structural abnormalities causing the epileptic activity, or by the epileptic activity itself. A better knowledge of physiological and pathological brain networks in patients with epilepsy is critical for a better understanding the patterns of seizure generation, propagation and termination as well as the alteration of physiological brain networks by a chronic neurological disorder. Moreover, the identification of pathological and physiological networks in an individual subject is critical for the planning of epilepsy surgery aiming at resection or at least interruption of the epileptic network while sparing physiological networks which have potentially been remodelled by the disease. This work describes the combination of neuroimaging methods to study the functional epileptic networks in the brain, structural connectivity changes of the motor networks in patients with localisation-related or generalised epilepsy and finally structural connectivity of the epileptic network. The combination between EEG source imaging and simultaneous EEG-fMRI recordings allowed to distinguish between regions of onset and propagation of interictal epileptic activity and to better map the epileptic network using the continuous activity of the epileptic source. These results are complemented by the first recordings of simultaneous intracranial EEG and fMRI in human. This whole-brain imaging technique revealed regional as well as distant haemodynamic changes related to very focal epileptic activity. The combination of fMRI and DTI tractography showed subtle changes in the structural connectivity of patients with Juvenile Myoclonic Epilepsy, a form of idiopathic generalised epilepsy. Finally, a combination of intracranial EEG and tractography was used to explore the structural connectivity of epileptic networks. Clinical relevance, methodological issues and future perspectives are discussed

Methods and models for brain connectivity assessment across levels of consciousness

The human brain is one of the most complex and fascinating systems in nature. In the last decades, two events have boosted the investigation of its functional and structural properties. Firstly, the emergence of novel noninvasive neuroimaging modalities, which helped improving the spatial and temporal resolution of the data collected from in vivo human brains. Secondly, the development of advanced mathematical tools in network science and graph theory, which has recently translated into modeling the human brain as a network, giving rise to the area of research so called Brain Connectivity or Connectomics. In brain network models, nodes correspond to gray-matter regions (based on functional or structural, atlas-based parcellations that constitute a partition), while links or edges correspond either to structural connections as modeled based on white matter fiber-tracts or to the functional coupling between brain regions by computing statistical dependencies between measured brain activity from different nodes. Indeed, the network approach for studying the brain has several advantages: 1) it eases the study of collective behaviors and interactions between regions; 2) allows to map and study quantitative properties of its anatomical pathways; 3) gives measures to quantify integration and segregation of information processes in the brain, and the flow (i.e. the interacting dynamics) between different cortical and sub-cortical regions. The main contribution of my PhD work was indeed to develop and implement new models and methods for brain connectivity assessment in the human brain, having as primary application the analysis of neuroimaging data coming from subjects at different levels of consciousness. I have here applied these methods to investigate changes in levels of consciousness, from normal wakefulness (healthy human brains) or drug-induced unconsciousness (i.e. anesthesia) to pathological (i.e. patients with disorders of consciousness)