Concordance of MEG and fMRI patterns in adolescents during verb generation

In this study we focused on direct comparison between the spatial distributions of activation detected by functional magnetic resonance imaging (fMRI) and localization of sources detected by magnetoencephalography (MEG) during identical language tasks. We examined the spatial concordance between MEG and fMRI results in 16 adolescents performing a three-phase verb generation task that involves repeating the auditorily presented concrete noun and generating verbs either overtly or covertly in response to the auditorily presented noun. MEG analysis was completed using a synthetic aperture magnetometry (SAM) technique, while the fMRI data were analyzed using the general linear model approach with random-effects. To quantify the agreement between the two modalities, we implemented voxel-wise concordance correlation coefficient (CCC) and identified the left inferior frontal gyrus and the bilateral motor cortex with high CCC values. At the group level, MEG and fMRI data showed spatial convergence in the left inferior frontal gyrus for covert or overt generation versus overt repetition, and the bilateral motor cortex when overt generation versus covert generation. These findings demonstrate the utility of the CCC as a quantitative measure of spatial convergence between two neuroimaging techniques

Multimodal Integration: fMRI, MRI, EEG, MEG

This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specically, we look at correlative analysis, decomposition techniques, equivalent dipole tting, distributed sources modeling, beamforming, and Bayesian methods. Due to difculties in assessing ground truth of a combined signal in any realistic experiment difculty further confounded by lack of accurate biophysical models of BOLD signal we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difculties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research

Development of a Touch Stimulator for Functional Magnetic-Resonance Imaging

A tactile display system has been built with 25 contactors in a 5 × 5 array with 2mm spacing, designed to stimulate the fingertip. The drive mechanism for each contactor is a piezoelectric bimorph, allowing the display to use in functional magnetic resonance imaging experiments (fMRI). The amplitude and frequency of stimulation can be pre-set, and each contactor can be activated separately using a personal computer. The tactile produce a wide variety of time-varying spatial patterns of touch stimulation. The sensation is “natural” and the participants do not find the experience unpleasant. The psychophysics experiment and the first fMRI experiment involved identification of various patterns on the display: the tactile stimulus was stationary or moved in a circle or in a “random” trajectory with no obvious shape. Response was by push buttons. The second fMRI experiment focused on the relationship between the speed of tactile motion and the corresponding activation in the brain, using stimuli moving in a circular trajectory on the tactile display at various speeds in the range 2.9 to 77.9 mm s –1. In the psychophysics experiment, the mean identification score was 80% after only a few minutes’ practice. The results of the first fMRI experiment showed highly significant activations in primary and secondary somatosensory cortices for contrasts of circle or random stimuli with the rest condition; low significant activations in SI and SII were observed for the contrast of stationary stimuli with rest. Broca's area was found to be activated for circle and random stimulation but not for stationary stimulation. Results from the second fMRI experiment showed small speed-sensitive activations in the left side of the brain, mostly in the primary somatosensory cortex. The conclusion in present study was our tactile system can produce different types of tactile patterns and it works inside MRI scanner

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5

EpiGauss : caracterização espacio-temporal da actividade cerebral em epilepsia

Doutoramento em Engenharia ElectrotécnicaA epilepsia é uma patologia cerebral que afecta cerca de 0,5% da população mundial. Nas epilepsias focais, o principal objectivo clínico é a localização da zona epileptogénica (área responsável pelas crises), uma informação crucial para uma terapêutica adequada. Esta tese é centrada na caracterização da actividade cerebral electromagnética do cérebro epiléptico. As contribuições nesta área, entre a engenharia e neurologia clínica, são em duas direcções. Primeiro, mostramos que os conceitos associados às pontas podem ser imprecisos e não ter uma definição objectiva, tornando necessária uma reformulação de forma a definir uma referência fiável em estudos relacionados com a análise de pontas. Mostramos que as características das pontas em EEG são estatisticamente diferentes das pontas em MEG. Esta constatação leva a concluir que a falta de objectividade na definição de ponta na literatura pode induzir utilizações erradas de conceitos associados ao EEG na análise de MEG. Também verificamos que o uso de conjuntos de detecções de pontas efectuadas por especialistas (MESS) como referência pode fornecer resultados enganadores quando apenas baseado em critérios de consenso clínico, nomeadamente na avaliação da sensibilidade e especificidade de métodos computorizados de detecção de pontas Em segundo lugar, propomos o uso de métodos estatísticos para ultrapassar a falta de precisão e objectividade das definições relacionadas com pontas. Propomos um novo método de neuroimagem suportado na caracterização de geradores electromagnéticos – EpiGauss – baseado na análise individual dos geradores de eventos do EEG que explora as suas estruturas espacio-temporais através da análise de “clusters”. A aplicação de análise de “clusters” à análise geradores de eventos do EEG tem como objectivo usar um método não supervisionado, para encontrar estruturas espacio-temporais dps geradores relevantes. Este método, como processo não supervisionado, é orientado a utilizadores clínicos e apresenta os resultados sob forma de imagens médicas com interpretação similar a outras técnicas de imagiologia cerebral. Com o EpiGauss, o utilizador pode determinar a localização estatisticamente mais provável de geradores, a sua estabilidade espacial e possíveis propagações entre diferente áreas do cérebro. O método foi testado em dois estudos clínicos envolvendo doentes com epilepsia associada aos hamartomas hipotalâmicos e o outro com doentes com diagnóstico de epilepsia occipital. Em ambos os estudos, o EpiGauss foi capaz de identificar a zona epileptogénica clínica, de forma consistente com a história e avaliação clínica dos neurofisiologistas, fornecendo mais informação relativa à estabilidade dos geradores e possíveis percursos de propagação da actividade epileptogénica contribuindo para uma melhor caracterização clínica dos doentes. A conclusão principal desta tese é que o uso de técnicas não supervisionadas, como a análise de “clusters”, associadas as técnicas não-invasivas de EMSI, pode contribuir com um valor acrescido no processo de diagnóstico clínico ao fornecer uma caracterização objectiva e representação visual de padrões complexos espaciotemporais da actividade eléctrica epileptogénica.Epilepsy is a brain pathology that affects 0.5% of the world population. In focal epilepsies, the main clinical objective is the localization of the epileptogenic zone (brain area responsible for the epileptic seizures – EZ), a key information to decide an adequate therapeutic approach. This thesis is centred on electromagnetic activity characterization of the epileptic brain. Our contribution to this boundary area between engineering and clinical neurology is two-folded. First we show that spike related clinical concepts can be unprecise and some do not have objective definitions making necessary a reformulation in order to have a reliable reference in spike related studies. We show that EEG spike wave quantitative features are statistically different from their MEG counterparts. This finding leads to the conclusion that the lack of objective spike feature definitions in the literature can induce the wrong usage of EEG feature definition in MEG analysis. We also show that the use of multi-expert spike selections sets (MESS) as gold standard, although clinically useful, may be misleading whenever defined solely in terms of clinical agreement criteria, namely as references for automatic spike detection algorithms in sensitivity and specificity method analysis. Second, we propose the use of statistical methods to overcome some lack of precision and objectivity in spike related definitions. In this context, we propose a new ElectroMagnetic Source Imaging (EMSI) method – EpiGauss – based on cluster analysis that explores both spatial and temporal information contained in individual events sources analysis characterisation. This automatic cluster method for the analysis of spike related electric generators based in EEG is used to provide an unsupervised tool to find their relevant spatio-temporal structures. This method enables a simple unsupervised procedure aimed for clinical users and presents its results in an intuitive representation similar to other brain imaging techniques. With EpiGauss, the user is able to determine statistically probable source locations, their spatial stability and propagation patterns between different brain areas. The method was tested in two different clinical neurophysiology studies, one with a group of Hypothalamic Hamartomas and another with a group of Occipital Epilepsy patients. In both studies EpiGauss identified the clinical epileptogenic zone, consistent with the clinical background and evaluation of neurophysiologists, providing further information on stability of source locations and their probable propagation pathways that enlarges their clinical interpretation. This thesis main conclusion is that the use of unsupervised techniques, such as clustering, associated with EMSI non-invasive techniques, can bring an added value in clinical diagnosis process by providing objective and visual representation of complex epileptic brain spatio-temporal activity patterns

Prediction related phenomena of visual perception

Perception is grounded in our ability to optimize predictions about upcoming events. Such predictions depend on both the incoming sensory input and on our previously acquired conceptual knowledge. Correctly predicted or expected sensory stimuli induce reduced responses when compared to incorrectly predicted, surprising inputs. Predictions enable an efficient neuronal encoding so that less energy is invested to interpret redundant sensory stimuli. Several different neuronal phenomena are the consequences of predictions, such as repetition suppression (RS) and mismatch negativity (MMN). RS represents the reduced neuronal response to a stimulus upon its repeated presentation. MMN is the electrophysiological response difference between rare and frequent stimuli in an oddball sequence. While both are currently studied extensively, the underlying mechanisms of RS and MMN as well as their relation to predictions remains poorly understood. In the current thesis, four experiments were devised to investigate prediction related phenomena dependent on the repetition probability of stimuli. Two studies deal with the RS phenomenon, while the other two investigate the MMN response. In Experiment 1 the temporal dynamics underlying prediction and RS effects were tested. Participants were presented with expected and surprising stimulus pairs with two different inter-stimulus intervals (0.5s for Immediate and 1.75 or 3.75s for Delayed target presentation). These pairs could either repeat or alternate. Expectations were contingent on face gender and were manipulated with the repetition probability. We found that the prediction effects do not depend on the length of the ISI period, suggesting that Immediate and Delayed cue-target stimulus arrangements create similar expectation effects. In order to elucidate the neuronal mechanisms underlying these prediction effects (i.e. surprise enhancement or expectation suppression), in our second study, we employed the experimental design of the first experiment with the addition of random events as a control. We found that surprising events elicit stronger Blood Oxygen Level Dependent (BOLD) responses than random events, implying that predictions influence the neuronal responses via surprise enhancement. Similarly, the third experiment was employed to disentangle which neural mechanism underlies the visual MMN (vMMN). We compared the responses to the stimuli (chairs, faces, real and false characters) presented in conventional oddball sequences to the same stimuli in control sequences (Kaliukhovich and Vogels, 2014). We found that the neural mechanisms underlying vMMN are category dependent: the vMMN of faces and chairs was due to RS, while the vMMN response of real and false characters was mainly driven by surprise-related changes. So far, no study used category-specific regions of interest (ROIs) to examine the neuroimaging correlates of the vMMN. Therefore, for the fourth experiment, we recorded electrophysiological and neuroimaging data from the same participants with an oddball paradigm for real and false characters. We found a significant correlation between vMMN (CP1 cluster at 400 ms) and functional magnetic resonance imaging adaptation (in the letter form area for real characters), suggesting their strong relationship. Taking the four studies into consideration, it is clear that surprise has an important role in prediction related phenomena. The role of surprise is discussed in the light of these results and other recent developments reported in the literature. Overall, this thesis suggests the unification of RS and MMN within the framework of predictive coding