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)

Salience and default-mode network connectivity during threat and safety processing in older adults.

The appropriate assessment of threat and safety is important for decision-making but might be altered in old age due to neurobiological changes. The literature on threat and safety processing in older adults is sparse and it is unclear how healthy ageing affects the brain's functional networks associated with affective processing. We measured skin conductance responses as an indicator of sympathetic arousal and used functional magnetic resonance imaging and independent component analysis to compare young and older adults' functional connectivity in the default mode (DMN) and salience networks (SN) during a threat conditioning and extinction task. While our results provided evidence for differential threat processing in both groups, they also showed that functional connectivity within the SN - but not the DMN - was weaker during threat processing in older compared to young adults. This reduction of within-network connectivity was accompanied by an age-related decrease in low frequency spectral power in the SN and a reduction in inter-network connectivity between the SN and DMN during threat and safety processing. Similarly, we found that skin conductance responses were generally lower in older compared to young adults. Our results are the first to demonstrate age-related changes in brain activation during aversive conditioning and suggest that the ability to adaptively filter affective information is reduced in older adults

A sensitive and specific neural signature for picture-induced negative affect

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes

MULTIVARIATE MODELING OF COGNITIVE PERFORMANCE AND CATEGORICAL PERCEPTION FROM NEUROIMAGING DATA

State-of-the-art cognitive-neuroscience mainly uses hypothesis-driven statistical testing to characterize and model neural disorders and diseases. While such techniques have proven to be powerful in understanding diseases and disorders, they are inadequate in explaining causal relationships as well as individuality and variations. In this study, we proposed multivariate data-driven approaches for predictive modeling of cognitive events and disorders. We developed network descriptions of both structural and functional connectivities that are critical in multivariate modeling of cognitive performance (i.e., fluency, attention, and working memory) and categorical perceptions (i.e., emotion, speech perception). We also performed dynamic network analysis on brain connectivity measures to determine the role of different functional areas in relation to categorical perceptions and cognitive events. Our empirical studies of structural connectivity were performed using Diffusion Tensor Imaging (DTI). The main objective was to discover the role of structural connectivity in selecting clinically interpretable features that are consistent over a large range of model parameters in classifying cognitive performances in relation to Acute Lymphoblastic Leukemia (ALL). The proposed approach substantially improved accuracy (13% - 26%) over existing models and also selected a relevant, small subset of features that were verified by domain experts. In summary, the proposed approach produced interpretable models with better generalization.Functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions. The proposed data-driven approach to the source localized electroencephalogram (EEG) data includes an array of tools such as graph mining, feature selection, and multivariate analysis to determine the functional connectivity in categorical perceptions. We used the network description to correctly classify listeners behavioral responses with an accuracy over 92% on 35 participants. State-of-the-art network description of human brain assumes static connectivities. However, brain networks in relation to perception and cognition are complex and dynamic. Analysis of transient functional networks with spatiotemporal variations to understand cognitive functions remains challenging. One of the critical missing links is the lack of sophisticated methodologies in understanding dynamics neural activity patterns. We proposed a clustering-based complex dynamic network analysis on source localized EEG data to understand the commonality and differences in gender-specific emotion processing. Besides, we also adopted Bayesian nonparametric framework for segmentation neural activity with a finite number of microstates. This approach enabled us to find the default network and transient pattern of the underlying neural mechanism in relation to categorical perception. In summary, multivariate and dynamic network analysis methods developed in this dissertation to analyze structural and functional connectivities will have a far-reaching impact on computational neuroscience to identify meaningful changes in spatiotemporal brain activities

A sensorimotor account of visual attention in natural behaviour

The real-world sensorimotor paradigm is based on the premise that sufficient ecological complexity is a prerequisite for inducing naturally relevant sensorimotor relations in the experimental context. The aim of this thesis is to embed visual attention research within the real-world sensorimotor paradigm using an innovative mobile gaze-tracking system (EyeSeeCam, Schneider et al., 2009). Common laboratory set-ups in the field of attention research fail to create natural two-way interaction between observer and situation because they deliver pre-selected stimuli and human observer is essentially neutral or passive. EyeSeeCam, by contrast, permits an experimental design whereby the observer freely and spontaneously engages in real-world situations. By aligning a video camera in real time to the movements of the eyes, the system directly measures the observer’s perspective in a video recording and thus allows us to study vision in the context of authentic human behaviour, namely as resulting from past actions and as originating future actions. The results of this thesis demonstrate that (1) humans, when freely exploring natural environments, prefer directing their attention to local structural features of the world, (2) eyes, head and body perform distinct functions throughout this process, and (3) coordinated eye and head movements do not fully stabilize but rather continuously adjust the retinal image also during periods of quasi-stable “fixation”. These findings validate and extend the common laboratory concept of feature salience within whole-body sensorimotor actions outside the laboratory. Head and body movements roughly orient gaze, potentially driven by early stages of processing. The eyes then fine-tune the direction of gaze, potentially during higher-level stages of visual-spatial behaviour (Studies 1 and 2). Additional head-centred recordings reveal distinctive spatial biases both in the visual stimulation and the spatial allocation of gaze generated in a particular real-world situation. These spatial structures may result both from the environment and form the idiosyncrasies of the natural behaviour afforded by the situation. By contrast, when the head-centred videos are re-played as stimuli in the laboratory, gaze directions reveal a bias towards the centre of the screen. This “central bias” is likely a consequence of the laboratory set-up with its limitation to eye-in-head movements and its restricted screen (Study 3). Temporal analysis of natural visual behaviour reveals frequent synergistic interactions of eye and head that direct rather than stabilize gaze in the quasi-stable eye movement periods following saccades, leading to rich temporal dynamics of real-world retinal input (Study 4) typically not addressed in laboratory studies. Direct comparison to earlier data with respect to the visual system of cats (CatCam), frequently taken as proxy for human vision, shows that stabilizing eye movements play an even less dominant role in the natural behaviour of cats. This highlights the importance of realistic temporal dynamics of vision for models and experiments (Study 5). The approach and findings presented in this thesis demonstrate the need for and feasibility of real- world research on visual attention. Real-world paradigms permit the identification of relevant features triggered in the natural interplay between internal-physiological and external-situational sensorimotor factors. Realistic spatial and temporal characteristics of eye, head and body interactions are essential qualitative properties of reliable sensorimotor models of attention but difficult to obtain under laboratory conditions. Taken together, the data and theory presented in this thesis suggest that visual attention does not represent a pre-processing stage of object recognition but rather is an integral component of embodied action in the real world

Towards Data-Driven Large Scale Scientific Visualization and Exploration

Technological advances have enabled us to acquire extremely large datasets but it remains a challenge to store, process, and extract information from them. This dissertation builds upon recent advances in machine learning, visualization, and user interactions to facilitate exploration of large-scale scientific datasets. First, we use data-driven approaches to computationally identify regions of interest in the datasets. Second, we use visual presentation for effective user comprehension. Third, we provide interactions for human users to integrate domain knowledge and semantic information into this exploration process. Our research shows how to extract, visualize, and explore informative regions on very large 2D landscape images, 3D volumetric datasets, high-dimensional volumetric mouse brain datasets with thousands of spatially-mapped gene expression profiles, and geospatial trajectories that evolve over time. The contribution of this dissertation include: (1) We introduce a sliding-window saliency model that discovers regions of user interest in very large images; (2) We develop visual segmentation of intensity-gradient histograms to identify meaningful components from volumetric datasets; (3) We extract boundary surfaces from a wealth of volumetric gene expression mouse brain profiles to personalize the reference brain atlas; (4) We show how to efficiently cluster geospatial trajectories by mapping each sequence of locations to a high-dimensional point with the kernel distance framework. We aim to discover patterns, relationships, and anomalies that would lead to new scientific, engineering, and medical advances. This work represents one of the first steps toward better visual understanding of large-scale scientific data by combining machine learning and human intelligence