Analysis of consciousness for complete locked-in syndrome patients

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL)

Tracking brain dynamics across transitions of consciousness

How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.My PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College

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)

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

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Fractal dimension of cortical functional connectivity networks & severity of disorders of consciousness.

Recent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain and that consciousness emerges in a critical zone between low and high-entropy states. We propose fractal shapes as a measure of proximity to this critical point, as fractal dimension encodes information about complexity beyond simple entropy or randomness, and fractal structures are known to emerge in systems nearing a critical point. To validate this, we tested several measures of fractal dimension on the brain activity from healthy volunteers and patients with disorders of consciousness of varying severity. We used a Compact Box Burning algorithm to compute the fractal dimension of cortical functional connectivity networks as well as computing the fractal dimension of the associated adjacency matrices using a 2D box-counting algorithm. To test whether brain activity is fractal in time as well as space, we used the Higuchi temporal fractal dimension on BOLD time-series. We found significant decreases in the fractal dimension between healthy volunteers (n = 15), patients in a minimally conscious state (n = 10), and patients in a vegetative state (n = 8), regardless of the mechanism of injury. We also found significant decreases in adjacency matrix fractal dimension and Higuchi temporal fractal dimension, which correlated with decreasing level of consciousness. These results suggest that cortical functional connectivity networks display fractal character and that this is associated with level of consciousness in a clinically relevant population, with higher fractal dimensions (i.e. more complex) networks being associated with higher levels of consciousness. This supports the hypothesis that level of consciousness and system complexity are positively associated, and is consistent with previous EEG, MEG, and fMRI studies

Studying Resting State and Stimulus Induced BOLD Activity using the Generalized Ising Model and Independent Component Graph Analysis

Although many technical advancements have been made, neuroscientists still struggle to explain the underlying behaviour of how brain regions communicate with each other to form large-scale functional networks. functional Magnetic Resonance Imaging (fMRI) has been commonly used to investigate changes between brain regions over time using the Blood Oxygen Level Dependent (BOLD) signal. The goal of this thesis is to show the applicability of novel techniques and tools, such as the generalized Ising model (GIM) and the independent component graph analysis (GraphICA), to obtain information on the functional connectivity of populations with altered perception of consciousness. The GIM was used to model brain activity in healthy brains during various stages of consciousness, as induced by an anesthetic agent, propofol, in the auditory paradigm. GraphICA, a tool that combines ICA and graph theory was used to investigate the functional connectivity of resting state networks (RSNs) in patients with altered perception caused by tinnitus and in patients with altered states of consciousness caused by severe brain injury. For the tinnitus patients, we examined whether a correlation exists between tinnitus behavioural scores and functional brain connectivity of RSNs. Moreover, for the severely brain injured patients, a multimodal neuroimaging approach with hybrid FDG-PET/MRI was implemented to study the functional connectivity changes of the RSNs. The GIM simulated with an external field was able to model the brain activity at different levels of consciousness under naturalistic stimulation, at a temperature in the super critical regime. Further, a strong correlation was observed between tinnitus distress and the connectivity pattern within and between the right executive control network and the other RSNs. This suggests that tinnitus distress is the consequence of a hyperactive attention condition. A variability was observed in the appearance and temporal/spatial patterns of RSNs among the two resting state fMRI acquisitions acquired within the same scanning session of the severely brain injured patients. This suggests the need for new strategies to be developed in order to pick the best RSN from each acquisition. Overall, this work demonstrated that the GIM and GraphICA were promising tools to investigate brain activity of populations with altered perception of consciousness and in future can be extended to investigate different neurological populations

EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions

Abstract: The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent two robust and widely used methods for MEG/EEG functional connectivity estimation. Interestingly, both methods have been shown to detect relative alterations of brain functional connectivity in conditions associated with changes in the level of consciousness, such as following severe brain injury or under anaesthesia. Despite these promising findings, it was unclear whether wPLI and wSMI may account for distinct or similar types of functional interactions. Using simulated high-density (hd-)EEG data, we demonstrate that, while wPLI has high sensitivity for couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI can detect purely nonlinear interaction dynamics. Moreover, we evaluated the potential impact of these differences on real experimental data by computing wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults during wakefulness and deep (N3-)sleep, characterised by different levels of consciousness. In line with the simulation-based findings, this analysis revealed that both methods have different sensitivity for changes in brain connectivity across the two vigilance states. Our results indicate that the conjoint use of wPLI and wSMI may represent a powerful tool to study the functional bases of consciousness in physiological and pathological conditions