Multi-modal and multi-model interrogation of large-scale functional brain networks

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours

Statistical Analysis of Functional Connectivity in Brain Imaging: Measurement Reliability and Clinical Applications

Measurement reliability is crucial for the research of functional connectivity data in the context of pursuing more reproducible research. Unfortunately, the utility of traditional reliability measures, such as the intraclass correlation coefficient, is limited given the size and complexity of functional connectivity data. In recent work, novel reliability measures have been introduced in the context where a set of subjects are measured twice or more, including: fingerprinting, rank sums, and generalizations of the intraclass correlation coefficient. However, the relationships between, and the best practices among these measures remains largely unknown. In this thesis, we consider a novel reliability measure, discriminability. We show that it is deterministically linked with the correlation coefficient under univariate random effect models, and has desired property of optimal accuracy for inferential tasks using multivariate measurements. Additionally, we propose a universal framework of reliability test based on permutations of the statistics.The power of permutation tests derived from these measures are compared numerically under Gaussian and non-Gaussian settings, with and without simulated batch effects. Motivated by both theoretical and empirical results, we provide methodological recommendations for each benchmark setting to serve as a resource for future analyses. We investigate the Poisson and Gaussian approximations of the tests so that the computational cost is reduced. We demonstrate possible follow-up research using reliability tests via applications on the Human Connectome Project functional connectivity data. We believe these results will play an important role towards improving reproducibility not only for functional connectivity, but also in fields such as functional magnetic resonance imaging in general, genomics, pharmacology, and more. Lastly, we illustrate the potential of functional connectivity as a source of causal biomarkers with an example of analyzing the trial data for an aphasia treatment

Improving Engagement Assessment by Model Individualization and Deep Learning

This dissertation studies methods that improve engagement assessment for pilots. The major work addresses two challenging problems involved in the assessment: individual variation among pilots and the lack of labeled data for training assessment models. Task engagement is usually assessed by analyzing physiological measurements collected from subjects who are performing a task. However, physiological measurements such as Electroencephalography (EEG) vary from subject to subject. An assessment model trained for one subject may not be applicable to other subjects. We proposed a dynamic classifier selection algorithm for model individualization and compared it to other two methods: base line normalization and similarity-based model replacement. Experimental results showed that baseline normalization and dynamic classifier selection can significantly improve cross-subject engagement assessment. For complex tasks such as piloting an air plane, labeling engagement levels for pilots is challenging. Without enough labeled data, it is very difficult for traditional methods to train valid models for effective engagement assessment. This dissertation proposed to utilize deep learning models to address this challenge. Deep learning models are capable of learning valuable feature hierarchies by taking advantage of both labeled and unlabeled data. Our results showed that deep models are better tools for engagement assessment when label information is scarce. To further verify the power of deep learning techniques for scarce labeled data, we applied the deep learning algorithm to another small size data set, the ADNI data set. The ADNI data set is a public data set containing MRI and PET scans of Alzheimer\u27s Disease (AD) patients for AD diagnosis. We developed a robust deep learning system incorporating dropout and stability selection techniques to identify the different progression stages of AD patients. The experimental results showed that deep learning is very effective in AD diagnosis. In addition, we studied several imbalance learning techniques that are useful when data is highly unbalanced, i.e., when majority classes have many more training samples than minority classes. Conventional machine learning techniques usually tend to classify all data samples into majority classes and to perform poorly for minority classes. Unbalanced learning techniques can balance data sets before training and can improve learning performance

Exploring the latent space between brain and behaviour using eigen-decomposition methods

Machine learning methods have been successfully used to analyse neuroimaging data for a variety of applications, including the classification of subjects with different brain disorders. However, most studies still rely on the labelling of the subjects, constraining the study of several brain diseases within a paradigm of pre-defined clinical labels, which have shown to be unreliable in some cases. The lack of understanding regarding the association between brain and behaviour presents itself as an interesting challenge for more exploratory machine learning approaches, which could potentially help in the study of diseases whose clinical labels have shown limitations. The aim of this project is to explore the possibility of using eigen-decomposition approaches to find multivariate associative effects between brain structure and behaviour in an exploratory way. This thesis addresses a number of issues associated with eigen-decomposition methods, in order to enable their application to investigate brain/behaviour relationships in a reliable way. The first contribution was showing the advantages of an alternative matrix deflation approach to be used with Sparse Partial Least Squares (SPLS). The modified SPLS method was later used to model the associations between clinical/demographic data and brain structure, without relying on a priori assumptions on the sparsity of each data source. A novel multiple hold-out SPLS framework was then proposed, which allowed for the detection of robust multivariate associative effects between brain structure and individual questionnaire items. The linearity assumption of most machine learning methods used in neuroimaging might be a limitation, since these methods will not have enough flexibility to detect non-linear associations. In order to address this issue, a novel Sparse Canonical Correlation Analysis (SCCA) method was proposed, which allows one to use sparsity constraints in one data source (e.g. neuroimaging data), with non-linear transformations of the data in the other source (e.g. clinical data)

Development and assessment of new post-processing methodologies in 3D contrast enhanced MRI

This thesis aims to investigate some of the methods currently used in contrast MR imaging. It specifically focuses on methods that require subtraction of noncontrast enhanced (pre) 3D imaging data sets from contrast-enhanced (post) data, collected within a single imaging session. Current methods assume that there is little or no intra-scan patient motion and thus do not attempt to correct for this. This thesis aims to determine if such motion does exist and if so what methods are best suited to correct it. The thesis begins by describing some of the relevant MR physics and history of contrast enhancement in chapter 1, and expands on this in chapter 2 by focusing on angiographic, and contrast-enhanced techniques. Chapter 2 continues by investigating an MP RAGE subtraction technique for producing venograms, which requires pre and post-contrast data subtraction. Data is collected for 20 patients and the effects of motion correction on the resulting venograms are investigated. Chapter 3 investigates a different type of pre and post-contrast enhanced study where it is used for tumour volume measurement. Examining the effects on tumour volumes measured with and without the realignment correction provides quantitative evidence that realignment is a requirement in this and similar types of study. To enable the significance of segmentation accuracy on realigmnent to be tested a phantom pre and post-contrast data set is developed in chapter 4. Chapter 5 uses this data set to test the effects of differing segmentation accuracies, with respect to the accurately segmented phantom data, on realignment accuracy where the pre and post-contrast data differ by known rotations and translations. This provides information on the effects of contrast enhancement on realignment accuracy, as well as providing information on the required brain segmentation accuracy required to accurately realign these data sets. Chapter 6 expands on this work by testing segmentation accuracy effects on two real patient data sets. The first patient data set differs from the phantom data in terms of its noise characteristics and the second has a space occupying lesion similar to those regularly encountered in the clinical setting. Chapter 7 aims to develop an automatic technique for segmenting, realigning and visualising venographic data using the venography technique described in chapter 2. It uses a histogram and morphological operations to ensure that all of the contrast enhanced data is removed from the data, whilst attempting to segment the brain to an acceptable accuracy. Although this algorithm is specifically designed for venograms visualisation, it would require only a small amount of adjustment enabling it to be applied to the tumour volume measurement technique described in chapter 3. Chapter 8 tests this algorithm using the data collected in chapter 2 and measures its performance in producing satisfactory brain segmentations, which is required for accurate realignment. This would also be required for accurate realignment in tumour volume measurement studies. Chapter 8 also measures the algorithms capabilities in correctly producing visualisation data sets for the purposes of venography. The algorithm has limited success in both brain segmentation and venous visualisation, nevertheless this is encouraging as a first attempt as the algorithm is being applied to real patient data sets reflecting a range of pathological conditions and not only to selected normal data sets. Chapter 8 suggests some modifications that could be applied to the algorithm that might improve its future success. This includes modifying it to become a semi-automated technique. (Abstract shortened by ProQuest.)

Study of the electroencephalographic correlates of mind wandering and meditation. Etude des corrélats électroencéphalographiques de la dérive attentionnelle et de la méditation

Si l'on essaye de concentrer notre attention sur un objet physique ou mental donné, on s'aperçoit vite qu'elle ne peut pas rester indéfiniment fixée sur l'objet en question mais se ré-oriente rapidement vers d'autres pensées ou sensations, un phénomène appelé dérive attentionnelle. Fait intéressant, les anciennes traditions de pratiques de méditation ont développé une grande variété de méthodes visant à développer la prise de conscience des épisodes de dérive attentionnelle et à entraîner l'esprit à rester concentré. Il est important de souligner que la connaissance du contenu de l'attention est une information qui est personnelle au sujet et qui ne peut être évaluée qu'à l'aide de méthode prennant en compte la perspective à la première personne. Au cours de ma thèse, j'ai étudié à la fois la dérive attentionnelle et les états de méditation dans un effort pour mieux comprendre ce qui se passe dans le cerveau quand quelqu'un médite. Quelle que soit la tradition de méditation, les dérives attentionnelles sont omniprésentes pendant la méditation. Ce sujet constitue donc un point de départ idéal pour l'étude de la méditation. Ce phénomène de dérive attentionnelle n'est pas unique à la méditation mais est présent dès qu'une personne se concentre sur une tâche à l'exclusion de toute autre. En utilisant un protocole nouveau, nous montrons que les épisodes de dérive attentionnelle sont accompagnés par une amplitude accrue des basses fréquences EEG 1-3Hz delta et 4-7Hz theta ainsi qu'une réduction du traitement sensoriel pré-attentif, comme le montre l'analyse de potentiels évoqués. Ces résultats indiquent que la dérive attentionnelle est associée à un niveau réduit de vigilance, similaire aux premiers stades de la somnolence. Ceci est cohérent avec certains textes bouddhistes sur la méditation, qui représentent la dérive attentionelle comme un état de sommeil par rapport aux périodes où l'esprit est concentré. Puis, nous avons réalisé une étude comparative de l'activité EEG au cours de la méditation pour tenter de déterminer l'origine des résultats divergents de la littérature. Nous avons enregistré l'activité spontanée EEG de 3 groupes de méditants de 3 différentes traditions de méditation et d'un groupe de non-méditants en utilisant le même protocole. Nous avons montré que tous les groupes de méditants avaient une amplitude de fréquence gamma 60-110Hz plus élevée par rapport aux contrôles pendant la méditation, indiquant peut-être des processus attentionnels différents chez les méditants. Aucune différence n'a été trouvée entre l'état mental contrôle et l'état méditatif chez les méditants, ce qui suggère que les modifications dues à la pratique longue de la méditation sont plus robustes que les effets de l'état mental de méditation par rapport à un état contrôle. Dans l'ensemble, notre étude souligne la nécessité de mieux définir ce que pourrait être le meilleur état de contrôle mental pour la méditation. Au cours de ce travail j'ai également exploré les méthodologies pour recueillir des informations subjectives pertinentes. Notre travail apporte de nouvelles perspectives pour l'étude la conscience humaine, mais la route reste longue avant que nous ne comprenions parfaitement les mécanismes sous-jacents de notre vie intérieure.Trying to focus our attention on any given physical or mental object, we soon realize it cannot be kept indefinitely focused and soon drifts towards other thoughts or sensations, a phenomenon called mind wandering. Interestingly, ancient traditions of meditation practices have developed a large variety of methods aiming at developing the awareness of mind wandering episodes and training the mind to remain focused. It is important to point out that the knowledge of the focus of attention is a type of information that is private to the subject and that can only be assessed using methods that take into account first-person perspectives. During my thesis, I studied both the mind wandering and the meditation mental states in an effort to better understand what is happening in the brain when someone meditates. First, regardless of the meditation tradition, mind wandering is ever present during meditation and it seemed like an ideal starting point for studying meditation. It is also a phenomenon that is not unique to meditation and is present whenever a person attempts to focus. Using a novel EEG protocol, we show that mind wandering episodes are accompanied by increased amplitude at low frequencies in the delta (1-3Hz) and theta (4-7Hz) frequency bands as well as a reduction of pre-attentive sensory processing as shown by the analysis event-related potentials. These results indicate that mind wandering is associated with a lower vigilance level, resembling early stages of drowsiness. These results are consistent with some Buddhist texts on meditation, in which mind wandering is considered to be a state of relative sleep where the mind is not aware. Then, we realized a comparative study of EEG activity during meditation to attempt to sort out the origin of the divergent results found in the literature. We recorded the spontaneous EEG activity of 3 groups of meditators from 3 different meditation traditions in addition to a non-meditator group using the same protocol and equipment. We showed that all groups of meditators had higher 60-110Hz gamma amplitude when compared to the controls during meditation, possibly indicating different attentional processes in meditators. No differences were found between the mental control state and the meditative state in meditators, suggesting that we were observing trait rather than state effects of meditation. Overall, our study emphasizes the need to better define what could be the best control mental state for meditation. During this work, I also explored the methodologies allowing the collection of accurate subjective data. Our work brings new data in the field of consciousness, mind wandering and meditation study, but the road will be long before we fully understand the mechanisms underlying our inner life

Data Acquisition Applications

Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book