A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

High-frequency Broadband Modulations of Electroencephalographic Spectra

High-frequency cortical potentials in electroencephalographic (EEG) scalp recordings have low amplitudes and may be confounded with scalp muscle activities. EEG data from an eyes-closed emotion imagination task were linearly decomposed using independent component analysis (ICA) into maximally independent component (IC) processes. Joint decomposition of IC log spectrograms into source- and frequency-independent modulator (IM) processes revealed three distinct classes of IMs that separately modulated broadband high-frequency (∼15–200 Hz) power of brain, scalp muscle, and likely ocular motor IC processes. Multi-dimensional scaling revealed significant but spatially complex relationships between mean broadband brain IM effects and the valence of the imagined emotions. Thus, contrary to prevalent assumption, unitary modes of spectral modulation of frequencies encompassing the beta, gamma, and high gamma frequency ranges can be isolated from scalp-recorded EEG data and may be differentially associated with brain sources and cognitive activities

Mobile brain/body imaging of landmark-based navigation with high-density EEG.

Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, and executive processes that jointly mediate active exploration and spatial learning. However, most neuroimaging approaches in humans are based on static, motion-constrained paradigms and they do not account for all these processes, in particular multisensory integration. Following the Mobile Brain/Body Imaging approach, we aimed to explore the cortical correlates of landmark-based navigation in actively behaving young adults, solving a Y-maze task in immersive virtual reality. EEG analysis identified a set of brain areas matching state-of-the-art brain imaging literature of landmark-based navigation. Spatial behavior in mobile conditions additionally involved sensorimotor areas related to motor execution and proprioception usually overlooked in static fMRI paradigms. Expectedly, we located a cortical source in or near the posterior cingulate, in line with the engagement of the retrosplenial complex in spatial reorientation. Consistent with its role in visuo-spatial processing and coding, we observed an alpha-power desynchronization while participants gathered visual information. We also hypothesized behavior-dependent modulations of the cortical signal during navigation. Despite finding few differences between the encoding and retrieval phases of the task, we identified transient time-frequency patterns attributed, for instance, to attentional demand, as reflected in the alpha/gamma range, or memory workload in the delta/theta range. We confirmed that combining mobile high-density EEG and biometric measures can help unravel the brain structures and the neural modulations subtending ecological landmark-based navigation

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

Localization of brain signal sources using blind source separation

Reliable localization of brain signal sources by using convenient, easy, and hazardless data acquisition techniques can potentially play a key role in the understanding, analysis, and tracking of brain activities for determination of physiological, pathological, and functional abnormalities. The sources can be due to normal brain activities, mental disorders, stimulation of the brain, or movement related tasks. The focus of this thesis is therefore the development of novel source localization techniques based upon EEG measurements. Independent component analysis is used in blind separation (BSS) of the EEG sources to yield three different approaches for source localization. In the first method the sources are localized over the scalp pattern using BSS in various subbands, and by investigating the number of components which are likely to be the true sources. In the second method, the sources are separated and their corresponding topographical information is used within a least-squares algorithm to localize the sources within the brain region. The locations of the known sources, such as some normal brain rhythms, are also utilized to help in determining the unknown sources. The final approach is an effective BSS algorithm partially constrained by information related to the known sources. In addition, some investigation have been undertaken to incorporate non-homogeneity of the head layers in terms of the changes in electrical and magnetic characteristics and also with respect to the noise level within the processing methods. Experimental studies with real and synthetic data sets are undertaken using MATLAB and the efficacy of each method discussed

Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling

Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses