EEG MICROSTATES ANALYSIS IN PATIENTS WITH EPILEPSY

Analysis of EEG microstates is a promising topographical method that is currently being studied for diagnosis of neuro-psychiatric diseases such as schizophrenia, dementia, etc. The aim of our study is to describe the possibility of using the microstate analysis of electroencephalographic recordings (EEG) for examination of the epileptic activity. The EEG recordings were measured on patients with epilepsy and on control subjects (with no epileptic pathology) in the system 10 - 20. The data are analysed in average montage and filtered with bandpass from 0.5 to 30.0 Hz. We calculate the global field power (GFP) curve to extract microstates from the EEG recordings. We take local maxima (peaks) of GFP curve to create amplitude topographic maps. The microstate 1 seems to have higher occurrence for the non-epileptic controls than the patients with epilepsy. The duration of the microstate 4 seems to be higher in the epileptic patients than the non-epileptic controls. We have found that there is a significant difference in the duration, occurrence and contribution of the amplitude topographic maps between the non-epileptic controls and the patients with epilepsy

Editorial: Chasing brain dynamics at their speed: what can time-varying functional connectivity tell us about brain function?

In the past decades, the growing field of network neuroscience has opened new perspectives on the study of the brain and its function. The integration of tools from network analysis and system neuroscience has allowed researchers to explore the properties of brain networks, offering a valuable alternative to traditional methods based on simple subtraction and mass univariate analysis (Sporns, 2010; Behrens and Sporns, 2012). This has led to an exponential growth of connectivity algorithms and methods designed to capture the intrinsic dynamics of human brain networks, both at rest and during active tasks. As a result, a new research direction has emerged. The quantification of spatio-temporal dynamics of functional connectivity (FC) is offering new means to observe a vast repertoire of brain functions. Despite significant advances in this domain, there are still major challenges to address. This is partly due to the rapid and distributed nature of brain interactions, with large-scale networks that constantly evolve and coordinate activity to produce human perception, cognition, and behavior at sub-second timescales. Additionally, brain network activity can vary widely within and across individuals (Finn et al., 2015; Van De Ville et al., 2021), as well as in clinical conditions and brain disorders (see Miao et al.). Thus, modeling whole-brain network dynamics, accounting for the necessary spatial and temporal resolution at both individual and population levels, remains a crucial goal yet to be fully achieved. The present Research Topic contains a collection of methodological and empirical studies that touch upon some of the main challenges in the field, collectively providing insight into the current state of research and the potential solutions for advancing the field of dynamic network neuroscience in the future

Assessment of Event-Related EEG Power After Single-Pulse TMS in Unresponsive Wakefulness Syndrome and Minimally Conscious State Patients

In patients without a behavioral response, non-invasive techniques and new methods of data analysis can complement existing diagnostic tools by providing a method for detecting covert signs of residual cognitive function and awareness. The aim of this study was to investigate the brain oscillatory activities synchronized by single-pulse transcranial magnetic stimulation (TMS) delivered over the primary motor area in the time\u2013frequency domain in patients with the unresponsive wakefulness syndrome or in a minimally conscious state as compared to healthy controls. A time\u2013frequency analysis based on the wavelet transform was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS in patients as compared to healthy controls. The pattern of EEG changes in the patients differed from that of healthy controls. In the controls there was an early synchronization of slow waves immediately followed by a desynchronization of alpha and beta frequency bands over the frontal and centro-parietal electrodes, whereas an opposite early synchronization, particularly over motor areas for alpha and beta and over the frontal and parietal electrodes for beta power, was seen in the patients. In addition, no relevant modification in slow rhythms (delta and theta) after TMS was noted in patients. The clinical impact of these findings could be relevant in neurorehabilitation settings for increasing the awareness of these patients and defining new treatment procedures

Dysfunction of neurovascular/metabolic coupling in chronic focal epilepsy

In this study, we aim to evaluate the mechanisms underlying the neuro-vascular/metabolic coupling in the epileptogenic cortices of rats with chronic focal epilepsy. To that end, we first analyzed intracranial recordings (electrophysiology, laser Doppler flowmetry and optical imaging) obtained from the seizure onset zones during ictal periods and then used these data to fit a metabolically-coupled balloon model. This biophysical model is an extension of the standard balloon model with modulatory effects of changes in tissue oxygenation, capillary dynamics and variable O2 extraction fraction. As previously reported using acute seizure models, we found that there is a significant higher contribution from high local field potential frequency bands to the cerebral blood flow (CBF) responses in the epileptogenic cortices during ictal neuronal activities. The hemodynamic responses associated with ictal activities were distance-dependent with regard to the seizure focus, though varied in profiles from those obtained from acute seizure models. Parameters linking the CBF and relative concentration of deoxy-hemoglobin to neuronal activity in the biophysical model were significantly different between epileptic and normal rats. In particular, we found that the coefficient associated with the strength of the functional hyperemic response was significantly larger in the epileptogenic cortices, although changes in hemoglobin concentration associated with ictal activity reflected the existence of a significantly higher baseline for oxygen metabolism in the epileptogenic cortices

Study on ensemble classifiers for event-related potential based brain computer interfaces

九州工業大学博士学位論文 学位記番号:生工博甲第238号 学位授与年月日:平成27年3月25日第1章 序論|第2章 ブレイン-コンピュータ・インタフェース|第3章 ERP-based BCI の構成|第4章 オーバーラップト・パーティショニングを用いたERP-based BCI の集合識別器|第5章 結語九州工業大学平成26年

Verso la comprensione dello stato vegetativo e di minima coscienza.

The limited evidence and inconsistency of purposeful behaviors in patients in a minimally conscious state (MCS) asks for objective electrophysiological marker of the level of consciousness. Here, a comparison between event-related potentials (ERPs) was investigated using different level of stimulus complexity. ERPs were recorded in seventeen patients, 6 of which in vegetative state (VS), 11 in MCS, and 10 controls. Three oddball paradigms with different level of complexity were applied: sine tones, the subject’s own name versus sine tones and other first names. Latencies and amplitudes of N1 and P3 waves were compared. Cortical responses were found in all MCS patients, and in 6 of 11 patients in VS. Healthy controls and MCS patients showed a progressive increase of P3 latency in relation to the level of stimulus complexity. No modulation of P3 latency was observed in the vegetative patients. These results suggest that the modulation of P3 latency related to stimulus complexity may represent an objective index of higher-order processing integration that predicts the recovery of consciousness from VS to MCS when clinical manifestations are inconsistent. A second step was encouraged by the work of Schiff et al. (2007) reporting a MCS patient who responded to deep brain stimulation (DBS). We explored six patients that participated in an ABA design alternating between repetitive transcranial magnetic stimulation (rTMS) and peripheral nerve stimulation. After peripheral stimulation, patients did not exhibit clinical, behavioral, or electroencephalographic (EEG) changes. The frequency of specific and meaningful behaviors increased after rTMS in a patient, along with the absolute and relative power of the EEG δ, β, and α bands. Afterwards, a more consistent sample has been enrolled to reproduce the first encouraging results. Thirty MCS/VS patients participated to a randomized controlled trial consisting of transcranial stimulations with transcranial direct current stimulation (tDCS) and rTMS. Patients in MCS showed an increase of long range fronto-parietal connectivity indicating a complex information processing and a decrease of fluctuation of arousal . VS patients did not. These results suggest that rTMS may improve long range connections between remote cortical areas and promote, at some level, recovery of awareness and arousal in MCS patients

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

Study of the relationship between the EEG and BOLD signals using intracranial EEG - fMRI data simultaneously acquired in humans

The principal aim of this work was to further characterise the relationship between the electrophysiological and BOLD fMRI signals at the local level, exploiting the unique opportunity to analyse intracranial EEG (icEEG) and fMRI data recorded simultaneously in humans, during a finger tapping task and at rest. The MR-environment (gradient switch and mechanical vibration) related artefacts corrupting the icEEG data were the first problem tackled; they were characterised and removed using techniques developed by me. The two parts that followed aimed to shed further light on the neurophysiological basis of the BOLD effect. Firstly, the influence of the phase of the low frequency EEG activities (70 Hz) (phase-amplitude coupling: PAC) was found to explain variance in addition to a combination of , , and band powers, suggesting that PAC strength and power fluctuations result from complementary neuronal processes. Secondly, five interictal epileptiform discharge (IED) morphology and field extent related features were tested in their individual capability to predict the amplitude of the co-localised BOLD signal; these were the amplitude and rising phase slope, thought to reflect the degree of neuronal activity synchrony; width and energy, thought to reflect the duration of the excitatory post-synaptic potentials; and spatial field extent, thought to reflect the spatial extent of the surrounding, synchronised sources of neuronal activity. Among these features, the IED width was the only one found to explain BOLD signal variance in addition to the IED onsets, suggesting that the amplitude of the BOLD signal is comparatively better predicted by the duration of the underlying field potential, than by the degree of neuronal activity synchrony

EEG-fMRI in epilepsy and sleep

This thesis used simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) to investigate both epilepsy and sleep. Initially, EEG-fMRI was used in a cohort of patients with complex epilepsy referred from a tertiary epilepsy clinic for both pre-surgical evaluation and diagnostic reasons. The results suggest a limited utility of EEG-fMRI in the epilepsy clinic with a very complex patient group. Following on, investigation of early blood oxygen level dependent (BOLD) signal changes in a group of patients with focal epilepsy demonstrated potentially meaningful BOLD changes occurring six seconds prior to interictal epileptiform discharges, and modelling less than this six seconds can result in overlap of the haemodynamic response function used to model BOLD changes. The same analysis was used to model endogenously occurring sleep paroxysms; K-complexes (KCs), vertex sharp waves (VSWs) and sleep spindles (SSs), finding early BOLD signal changes with SSs in group data. Finally, KCs and VSWs were investigated in more detail in a group of participants under both sleep deprived and non-deprived conditions, demonstrating an increase in overall activation for both KCs and VSWs following sleep deprivation. Overall, we find early BOLD changes are not restricted to pathological events and sleep deprivation can enhance BOLD responses