Visual Exploration of Dynamic Multichannel EEG Coherence Networks

Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline-based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time-annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks

Molecular Mechanisms Responsible for Functional Cortical Plasticity During Development and after Focal Ischemic Brain Injury

The cerebral cortex is organized into functional representations, or maps, defined by increased activity during specific tasks. In addition, the brain exhibits robust spontaneous activity with spatiotemporal organization that defines the brain’s functional architecture (termed functional connectivity). Task-evoked representations and functional connectivity demonstrate experience-dependent plasticity, and this plasticity may be important in neurological development and disease. An important case of this is in focal ischemic injury, which results in destruction of the involved representations and disruption of functional connectivity relationships. Behavioral recovery correlates with representation remapping and functional connectivity normalization, suggesting functional organization is critical for recovery and a potentially valuable therapeutic target. However, the cellular and molecular mechanisms that drive this systems-level plasticity are unknown, making it difficult to approach therapeutic modulation of functional brain organization. Using cortical neuroimaging in mice, this dissertation explores the role of specific genes in sensory deprivation induced functional brain map plasticity during development and after focal ischemic injury. In the three contained chapters, I demonstrate the following: 1) Arc, an excitatory neuron synaptic-plasticity gene, is required for representation remapping and behavioral recovery after focal cortical ischemia. Further, perilesional sensory deprivation can direct remapping and improve behavioral recovery. 2) Early visual experience modulates functional connectivity within and outside of the visual cortex through an Arc-dependent mechanism. 3) Electrically coupled inhibitory interneuron networks limit spontaneous activity syncrhony between distant cortical regions. This work starts to define the molecular basis for plasticity in functional brain organization and may help develop approaches for therapeutic modulation of functional brain organization

Sparse Predictive Structure of Deconvolved Functional Brain Networks

The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional structures between experimental conditions or groups shuns typical mass univariate methods. Furthermore most network estimation methods cannot distinguish between real and spurious correlation arising from the convolution due to nodes' interaction, which thus introduces additional noise in the data. We propose a machine learning pipeline aimed at identifying multivariate differences between brain networks associated to different experimental conditions. The pipeline (1) leverages the deconvolved individual contribution of each edge and (2) maps the task into a sparse classification problem in order to construct the associated "sparse deconvolved predictive network", i.e., a graph with the same nodes of those compared but whose edge weights are defined by their relevance for out of sample predictions in classification. We present an application of the proposed method by decoding the covert attention direction (left or right) based on the single-trial functional connectivity matrix extracted from high-frequency magnetoencephalography (MEG) data. Our results demonstrate how network deconvolution matched with sparse classification methods outperforms typical approaches for MEG decoding

Resting-state connectivity and functional specialization in human medial parieto-occipital cortex

According to recent models of visuo-spatial processing, the medial parieto-occipital cortex is a crucial node of the dorsal visual stream. Evidence from neurophysiological studies in monkeys has indicated that the parieto-occipital sulcus (POS) contains three functionally and cytoarchitectonically distinct areas: the visual area V6 in the fundus of the POS, and the visuo-motor areas V6Av and V6Ad in a progressively dorsal and anterior location with respect to V6. Besides different topographical organization, cytoarchitectonics, and functional properties, these three monkey areas can also be distinguished based on their patterns of cortico-cortical connections. Thanks to wide-field retinotopic mapping, areas V6 and V6Av have been also mapped in the human brain. Here, using a combined approach of resting-state functional connectivity and task-evoked activity by fMRI, we identified a new region in the anterior POS showing a pattern of functional properties and cortical connections that suggests a homology with the monkey area V6Ad. In addition, we observed distinct patterns of cortical connections associated with the human V6 and V6Av which are remarkably consistent with those showed by the anatomical tracing studies in the corresponding monkey areas. Consistent with recent models on visuo-spatial processing, our findings demonstrate a gradient of functional specialization and cortical connections within the human POS, with more posterior regions primarily dedicated to the analysis of visual attributes useful for spatial navigation and more anterior regions primarily dedicated to analyses of spatial information relevant for goal-directed action

Construction of embedded fMRI resting state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations