EXPERIMENTAL-COMPUTATIONAL ANALYSIS OF VIGILANCE DYNAMICS FOR APPLICATIONS IN SLEEP AND EPILEPSY

Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. Notable progress in neuroscience research—and particularly sleep and epilepsy—has been achieved through experimentation on animals. Experimental models of epilepsy provide us with the opportunity to explore or even manipulate the sleep-seizure relationship in order to decipher different aspects of their interactions. Important in this process is the development of techniques for modeling and tracking sleep dynamics using electrophysiological measurements. In this dissertation experimental and computational approaches are proposed for modeling vigilance dynamics and their utility demonstrated in nonepileptic control mice. The general framework of hidden Markov models is used to automatically model and track sleep state and dynamics from electrophysiological as well as novel motion measurements. In addition, a closed-loop sensory stimulation technique is proposed that, in conjunction with this model, provides the means to concurrently track and modulate 3 vigilance dynamics in animals. The feasibility of the proposed techniques for modeling and altering sleep are demonstrated for experimental applications related to epilepsy. Finally, preliminary data from a mouse model of temporal lobe epilepsy are employed to suggest applications of these techniques and directions for future research. The methodologies developed here have clear implications the design of intelligent neuromodulation strategies for clinical epilepsy therapy

Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646)National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502)National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901

Neural correlates of consciousness in the complexity of brain networks

How do we define consciousness? Besides philosophical endeavours, the development of modern neuroimaging techniques fostered a principled way of quantifying the neural correlates of consciousness. Acquiring and analysing resting-state functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data, has allowed neuroscientists to noninvasively map the brain’s functional interactions (or functional connectivity). Based on data obtained during controlled loss of consciousness and in cases of patients with disorders of consciousness, it has now been suggested that multiple, functionally specialized/segregated areas need to interact and integrate information in order to support consciousness. Thus an emerging idea in neuroscience is that the brain needs to balance the coexistence of functional segregation and integration, a property often termed as brain complexity, in order to produce consciousness. A resulting hypothesis is that consciousness is abolished when the balance between segregation and integration is lost and brain complexity is attenuated. In that regard, I use complexity of functional connectivity, an aggregate measure of segregation and integration, as a marker of consciousness. This effort consists of two parts. First, I provide evidence that complexity in the healthy, awake brain is critical in the sense that it reflects a critical balance of segregation and integration designed to support efficient information communication. In turn, I provide evidence that loss of consciousness is associated with decreased complexity i.e. that functional connectivity departs from the critical complexity of the healthy, awake brain towards a more segregated configuration. The structure of this thesis follows accordingly. In the first experimental chapter (3), I show the importance of the critical balance of complexity in the healthy, awake brain by using a structure-to-function association model. Specifically, I show that complexity can be derived upon certain optimal, structural connections (computed as the Nash equilibrium between regions), which promote efficient communication in the brain from the regional to the whole-brain level. Chapter 4 focuses on capturing alterations of complexity in cases of sedation, anaesthesia and disorders of consciousness. Specifically, I show that as one goes from the awake state to anaesthetic-induced unconsciousness and disorders of consciousness, functional connectivity becomes less complex and more segregated. A refined approach that quantifies complexity in different parts of the brain allowed me to see whether this reduction in complexity is more evident in specific regions and networks. Under this framework, at the regional level I provide evidence that sparsely connected regions linking different parts of the brain play a critical role in whole-brain complexity. At the network level I show the importance of the default mode network in whole-brain complexity. Even during rest, the brain is not static and displays rich temporal dynamics. Thus it is not only the complexity at each snapshot of time but also how complexity changes across time that can help us understand loss of consciousness. In chapter 5 I use a dynamic framework to derive and characterize the dynamics of functional connectivity during loss of consciousness. In turn, I provide evidence that brains become less temporally complex as one goes from the awake state to anaesthetic-induced unconsciousness and disorders of consciousness. Moreover, my goal is to see whether the principle of complexity reduction can be also applied to the developing brain. Towards this direction, in chapter 6 I use complexity on EEG connectivity data to examine anaesthetic-induced loss of consciousness in infants. Specifically, I show that complexity in anaesthetised infants aged 0-3 years is reduced compared to a state of emergence from anaesthesia, indicating its importance in supporting consciousness and brain function since infancy. Taken together, these findings show that while the complexity of the healthy, awake brain during rest is critically configured, the unconscious brain is characterized by reduced complexity. Based on the results presented in this work, I propose that consciousness can be assessed on the basis of complexity of resting-state functional connectivity data

Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration

Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration

SLEEP AND THERMOREGULATION: A STUDY OF THE EFFECT OF AMBIENT TEMPERATURE MANIPULATION ON MOUSE SLEEP ARCHITECTURE

Good quality sleep is essential for mental and physical health. Inadequate sleep impacts memory consolidation, learning and cognition, immune function, autonomic regulation, physical performance, and other vital functions. In many neurological disorders that are associated with sleep problems such as epilepsy and Alzheimer’s disease, changes in brain circuitry affect sleep-wake regulation mechanisms; this is reflected in anomalous sleep-wake architecture and usually accompanied by poor sleep depth. Thus, over many years, many approaches have been tried in humans and animal models with the goal of improving sleep quality. Unfortunately, each of those approaches comes with limitations or side effects. Thus, there is a need for a natural, safe, and low cost approach that overcomes many limitations to improve sleep and eventually the lives of individuals with sleep problems. Environmental temperature is one of the most important factors that affect sleep in humans and other animals. Studies have shown that the part of the brain governing thermoregulation is also involved in sleep-wake regulation. Even a mild change in environmental temperature can produce a significant effect on sleep. Thus, a better understanding of the sleep-thermoregulation interaction could lead to novel ways for treating many sleep disorders. As a first step on the translational pathway, experiments in animal models of disease conditions with disordered sleep are needed for investigating sleep–thermoregulation interactions and for devising and validating related approaches to enhance sleep quality before conducting them on humans. This dissertation explores and assesses the effect of changes in ambient temperature on sleep-wake architecture in control mice and epileptic mice, the latter from a model of temporal lobe epilepsy as an example of a disease model with disordered sleep. Then, based on the results of temperature effects on sleep in control and epileptic mice, different strategies are proposed and tested to modulate sleep through ambient temperature regulation in closed loop to improve sleep depth and regulate the timing of the sleep-wake cycle. The results presented in this dissertation demonstrate the feasibility of sleep enhancement and regulation of its timing and duration through manipulation of ambient temperature using closed-loop control systems. Similar approaches could foreseeably be used as more natural means for enhancing deep sleep in patients with epilepsy, Alzheimer’s, or Parkinson’s disease in which poor sleep is common and associated with adverse outcomes

Spectrally and temporally resolved estimation of neural signal diversity

Quantifying the complexity of neural activity has provided fundamental insights into cognition, consciousness, and clinical conditions. However, the most widely used approach to estimate the complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that substantially restrict its domain of applicability. In this article we leverage the information-theoretic foundations of LZ to overcome these limitations by introducing a complexity estimator based on state-space models — which we dub Complexity via State-space Entropy Rate (CSER). While having a performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial advantages: 1) CSER offers a principled decomposition into spectral components, which allows us to rigorously investigate the relationship between complexity and spectral power; and 2) CSER provides a temporal resolution two orders of magnitude better than LZ, which allows complexity analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver of complexity changes across states of consciousness; and reveals early entropy increases that precede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms. Overall, by overcoming the main limitations of LZ and substantially extending its range of applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal structure of the signal complexity associated with cognitive processes and conscious states

Depth of anaesthesia assessment based on time and frequency features of simplified electroencephalogram (EEG)

Anaesthesiology is a medical subject focusing on the use of drugs and other methods to deprive patients’ sensation for discomfort in painful medical diagnosis or treatment. It is important to assess the depth of anaesthesia (DoA) accurately since a precise as- sessment is helpful for avoiding various adverse reactions such as intraoperative awareness with recall (underdosage), prolonged recovery and an increased risk of post- operative complications for a patient (overdosage). Evidence shows that the depth of anaesthesia monitoring using electroencephalograph (EEG) improves patient treat- ment outcomes by reducing the incidences of intra-operative awareness, minimizing anaesthetic drug consumption and resulting in faster wake-up and recovery. For an accurate DoA assessment, intensive research has been conducted in finding 'an ulti- mate index', and various monitors and DoA algorithms were developed. Generally, the limitations of the existing DoA monitors or latest DoA algorithms include unsatis- factory data filtering techniques, time delay and inflexible. The focus of this dissertation is to develop reliable DoA algorithms for accurate DoA assessment. Some novel time-frequency domain signal processing techniques, which are better suited for non-stationary EEG signals than currently established methods, have been proposed and applied to monitor the DoA based on simplified EEG signals based on plenty of programming work (including C and other programming language). The fast Fourier transform (FFT) and the discrete wavelet transforms are applied to pre-process EEG data in the frequency domain. The nonlocal mean, mobility, permu- tation entropy, Lempel-Ziv complexity, second order difference plot and interval feature extraction methods are modified and applied to investigate the scaling behaviour of the EEG in the time domain. We proposed and developed three new indexes for identifying, classifying and monitoring the DoA. The new indexes are evaluated by comparing with the most popular BIS index. Simulation results demonstrate that our new methods monitor the DoA in all anaesthesia states accurately. The results also demonstrate the advantages of proposed indexes in the cases of poor signal quality and the consistency with the anaesthetists’ records. These new indexes show a 3.1-59.7 seconds earlier time response than BIS during the change from awake to light anaesthesia and a 33-264 seconds earlier time response than BIS during the change from deep anaesthesia to moderate anaesthesia

Modèles de semi-Markov cachés pour la segmentation de trajectoires oculométriques en phases de lecture

Textual information search is not a homogeneous process in time, neither from a cognitive perspective nor in terms ofeye-movement patterns, as shown in previous studies. Our objective is to analyze eye-tracking signals acquired throughparticipants achieving a reading task aiming at answering a binary question: Is the text related or not to some given targettopic? This activity is expected to involve several phases with contrasted oculometric characteristics, such as normalreading, scanning, careful reading, associated with different cognitive strategies, such as creation and rejection ofhypotheses, confirmation and decision. To model such phases, we propose an analytical data-driven method based onhidden semi-Markov chains, whose latent states represent different dynamics in eye movements.Four interpretable phases were highlighted: normal reading, speed reading, information search and slow confirmation.This interpretation was derived using model parameters and scanpath segmentations. It was then confirmed usingdifferent external covariates, among which semantic information extracted from texts. Analyses highlighted a gooddiscrimination of reading speeds by phases, some contrasted use of phases depending on the degree of relationshipbetween text semantic contents and target topics, and a strong preference of specific participants for specific strategies.As another output of our analyses, the individual variability in all eye-movement characteristics was assessed to be highand thus had to be taken into account, particularly trough mixed-effects models.As a perspective, the possibility of improving reading models by accounting for possible heterogeneity sources duringreading was discussed. We highlighted how analysing other sources of information regarding the cognitive processes atstake, such as EEG recordings, could benefit from the segmentation induced by our approach.La recherche d’information textuelle n’est pas un processus homogène dans le temps, que ce soit d’unpoint de vue cognitif ou de celui des mouvements des yeux, ainsi que l’ont montré des études précédentes. Notreobjectif est d’analyser des signaux oculométriques acquis lors de tâches où les participant.e.s doivent répondre à unequestion binaire : est-ce que le texte est lié ou non à un thème cible donné ? Nous nous attendons à ce que cetteactivité mette en jeu diverses phases avec des caractéristiques oculométriques contrastées, telle que la lecturenormale, rapide, de confirmation et de décision. Pour mettre en évidence des différentes phases, nous proposons uneméthode basée sur l’analyse de données fondée sur des modèles semi-markoviens cachés, dont les états latentsreprésentent différentes dynamiques relatives aux mouvements des yeux. Quatre phases interprétables ont étémises en évidence : lecture normale, lecture rapide, recherche d’information et confirmation lente. Leur interprétationdécoule des paramètres du modèle et de la segmentation des traces oculométriques.En perspective, nous discutons des possibilités offertes par cette approche pour améliorer des modèles de lecture enprenant en compte de potentiels modes de lecture hétérogènes mobilisés dans ce type de tâche. Nous mettons enévidence comment l’analyse d’autres sources d’information relatives aux processus cognitifs mis en jeu, telles quedes enregistrements EEG, pourraient bénéficier de la segmentation induite par notre approche