Multivariate functional network connectivity for disorders of consciousness

Recent evidence suggests that healthy brain is organized on large-scale spatially distant brain regions, which are temporally synchronized. These regions are known as resting state networks (RSNs). The level of interaction among these functional entities has been studied in the so called functional network connectivity (FNC). FNC aims to quantify the level of interaction between pairs of RSNs, which commonly emerge at similar spatial scale. Nevertheless, the human brain is a complex functional structure which is partitioned into functional regions that emerge at multiple spatial scales. In this work, we propose a novel multivariate FNC strategy to study interactions among communities of RSNs, these communities may emerge at different spatial scales. For this, first a community or hyperedge detection strategy was used to conform groups of RSNs with a similar behavior. Following, a distance correlation measurement was employed to quantify the level of interaction between these communities. The proposed strategy was evaluated in the characterization of patients with disorders of consciousness, a highly challenging problem in the clinical setting. The results suggest that the proposed strategy may improve the capacity of characterization of these brain altered conditions

Tracking dynamic interactions between structural and functional connectivity : a TMS/EEG-dMRI study

Transcranial magnetic stimulation (TMS) in combination with neuroimaging techniques allows to measure the effects of a direct perturbation of the brain. When coupled with high-density electroencephalography (TMS/hd-EEG), TMS pulses revealed electrophysiological signatures of different cortical modules in health and disease. However, the neural underpinnings of these signatures remain unclear. Here, by applying multimodal analyses of cortical response to TMS recordings and diffusion magnetic resonance imaging (dMRI) tractography, we investigated the relationship between functional and structural features of different cortical modules in a cohort of awake healthy volunteers. For each subject, we computed directed functional connectivity interactions between cortical areas from the source-reconstructed TMS/hd-EEG recordings and correlated them with the correspondent structural connectivity matrix extracted from dMRI tractography, in three different frequency bands (alpha, beta, gamma) and two sites of stimulation (left precuneus and left premotor). Each stimulated area appeared to mainly respond to TMS by being functionally elicited in specific frequency bands, that is, beta for precuneus and gamma for premotor. We also observed a temporary decrease in the whole-brain correlation between directed functional connectivity and structural connectivity after TMS in all frequency bands. Notably, when focusing on the stimulated areas only, we found that the structure-function correlation significantly increases over time in the premotor area controlateral to TMS. Our study points out the importance of taking into account the major role played by different cortical oscillations when investigating the mechanisms for integration and segregation of information in the human brain

EEG-based effective and functional connectivity for differentiating and predicting altered states of consciousness

How does the brain sustain consciousness? In this thesis, and in the work leading up to it, we provide new computational evidence for the importance of the posterior hot zone on one hand, and for long-distance frontoparietal connectivity on the other, in explaining the contrast between loss of consciousness and in maintaining conscious responsiveness. We adopt a factorial approach in our study, crossing two altered states of consciousness with two analytical methods for measuring changes in brain associated with these altered states. Specifically, we study healthy controls under propofol-anaesthesia and patients suffering from disorders of consciousness (DoC), employing functional and effective electroencephalographic (EEG) connectivity, thereby forming a 2-by-2 study design. We first demonstrate the power of functional EEG connectivity for predicting anaesthetic states in the healthy brain, by building a single multivariate regression model combining phase-lag brain connectivity and behaviour- and power-based dependent measures. We show that baseline alpha- and beta-connectivity, as measured prior to an anaesthetic induction, can predict both behaviour- and power-based measures during the induction and peak unresponsiveness, specifically as measured from the posterior electrodes. Next, we study patients suffering from DoC and show that the alpha-band functional connectivity over the left hemisphere, and graph-theoretic network centrality on the right, significantly predict the patient's clinical diagnosis. Our findings suggest a dissociation between mean spectral connectivity and network properties. Building on these findings, we then turn to dynamic causal modelling (DCM) to estimate modulations in effective brain connectivity due to anaesthesia, in and between the default mode network (DMN), the salience network (SAL), and the central executive network (CEN). Advancing current understanding of anaesthetic-induced LOC, we show evidence for a selective breakdown in the posterior hot zone and in medial feedforward frontoparietal connectivity within the DMN, and of parietal inter-network connectivity linking DMN and CEN. In a novel DCM-based out-of-sample cross-validation, we establish the predictive validity of our models, specifically highlighting frontoparietal connectivity as a generalisable predictor of states of consciousness. Importantly, we demonstrate a generalisation of this predictive power in an unseen dataset from the post-anaesthetic recovery state. Finally, we again use DCM to investigate changes in the effective connectivity between DoC patients and healthy controls within the DMN. Specifically, we show that the key difference between healthy controls or conscious patients and completely unresponsive patients is a reduction in left-hemispheric backward frontoparietal connectivity. Finally, with out-of-sample cross-validation, we show that left-hemispheric frontoparietal connectivity can not only distinguish patient groups from each other, it can also generalise to an unseen data subset collected from seemingly unresponsive patients who show evidence of consciousness when assessed with functional neuroimaging. This suggests that effective EEG connectivity can be used to identify covertly aware patients who seem behaviourally unresponsive. Overall, this thesis provides novel insights into the brain dynamics underlying transitions between altered states of consciousness and highlights the value of tracking these dynamics in a clinical context. DCM, though computationally more expensive, can accurately predict states of consciousness and provide causal explanations of the brain dynamics that cannot be inferred from functional connectivity alone. Functional connectivity, though correlational, is still an accurate predictive tool of altered states of consciousness. With clinically challenging, ambiguous cases like potentially covertly aware patients, we propose that the causal explanations and accurate predictions of DCM modelling could outweigh the computational complexity

Oscillations, metastability and phase transitions in brain and models of cognition

Neuroscience is being practiced in many different forms and at many different organizational levels of the Nervous System. Which of these levels and associated conceptual frameworks is most informative for elucidating the association of neural processes with processes of Cognition is an empirical question and subject to pragmatic validation. In this essay, I select the framework of Dynamic System Theory. Several investigators have applied in recent years tools and concepts of this theory to interpretation of observational data, and for designing neuronal models of cognitive functions. I will first trace the essentials of conceptual development and hypotheses separately for discerning observational tests and criteria for functional realism and conceptual plausibility of the alternatives they offer. I will then show that the statistical mechanics of phase transitions in brain activity, and some of its models, provides a new and possibly revealing perspective on brain events in cognition

Metastability, Criticality and Phase Transitions in brain and its Models

This essay extends the previously deposited paper "Oscillations, Metastability and Phase Transitions" to incorporate the theory of Self-organizing Criticality. The twin concepts of Scaling and Universality of the theory of nonequilibrium phase transitions is applied to the role of reentrant activity in neural circuits of cerebral cortex and subcortical neural structures

Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI

Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state fMRI data from the Human Connectome Project, showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, whilst synergy occurs mainly between non-homologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.Comment: 6 figures. arXiv admin note: text overlap with arXiv:1403.515

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome