Integration and segregation in whole-brain networks: implications for altered states of consciousness

To survive in an ever-changing environment, the brain must seamlessly integrate a rich stream of incoming information into coherent internal representations that can then be used to efficiently plan for action. The brain must also balance its ability to integrate information with a complementary capacity to segregate information into modules which perform specialized computations in local circuits. The central focus of this Thesis is to investigate the dynamical properties of the integration/segregation balance in functional MRI data collected in two distinct altered states of consciousness: slow-wave sleep, and a psychedelic experience induced by intravenously administering psilocybin; the psychoactive compound in “magic mushrooms”. In Chapter 2, I implement a novel method for identifying highly integrative nodes in human brain networks from the persistent homology of fMRI data, as a complement to standard graph theoretical methods. I report that topologically central nodes in the ‘persistence homological scaffold’ have a combination of high betweenness-centrality and high participation coefficient, whilst simultaneously avoiding densely connected neighborhood clusters. In Chapter 3, I investigate dynamical changes in global measures of functional integration and segregation derived from fMRI data. Synchrony, metastability and chimeraness metrics are computed separately in slow-wave sleep and in the psychedelic state. It is found that global synchrony and metastability are reduced in slow-wave sleep relative to a wakeful rest baseline, whilst chimeraness is increased. Diametrically opposite effects on each measure are observed in the psychedelic state. Chapter 4 characterizes the brain’s dynamical landscape from a system-level perspective under psilocybin by computing the fractional occupancy and transition probabilities of specific functional network states over time. An increase in the probability of occurrence of a globally coherent functional connectivity state is observed, alongside a strong decrease in the fractional occupancy of a fronto-parietal control network. In Chapter 5, I further characterize the capacity of brain areas to dynamically broadcast regional activity signals locally or globally in both deep sleep and the psychedelic state. The results notably show that intrinsic activity perturbations in a given brain area are more likely to propagate strictly locally in slow-wave sleep than during wakeful rest. In the psychedelic state, the size of integration cascades elicited by local perturbations over time is significantly more variable than during the baseline scan. The experimental findings presented in this Thesis indicate that alterations in consciousness level and/or contents are accompanied by temporally-dependent changes in the brain’s integration and segregation balance, promoting cross-modular integration in the psychedelic state, and functionally segregated brain dynamics in slow-wave sleep

Identifying Network Correlates of Memory Consolidation

Neuronal spiking activity carries information about our experiences in the waking world but exactly how the brain can quickly and efficiently encode sensory information into a useful neural code and then subsequently consolidate that information into memory remains a mystery. While neuronal networks are known to play a vital role in these processes, detangling the properties of network activity from the complex spiking dynamics observed is a formidable challenge, requiring collaborations across scientific disciplines. In this work, I outline my contributions in computational modeling and data analysis toward understanding how network dynamics facilitate memory consolidation. For experimental perspective, I investigate hippocampal recordings of mice that are subjected to contextual fear conditioning and subsequently undergo sleep-dependent fear memory consolidation. First, I outline the development of a functional connectivity algorithm which rapidly and robustly assesses network structure based on neuronal spike timing. I show that the relative stability of these functional networks can be used to identify global network dynamics, revealing that an increase in functional network stability correlates with successful fear memory consolidation in vivo. Using an attractor-based model to simulate memory encoding and consolidation, I go on to show that dynamics associated with a second-order phase transition, at a critical point in phase-space, are necessary for recruiting additional neurons into network dynamics associated with memory consolidation. I show that successful consolidation subsequently shifts dynamics away from a critical point and towards sub-critical dynamics. Investigations of in vivo spiking dynamics likewise revealed that hippocampal dynamics during non-rapid-eye-movement (NREM) sleep show features of being near a critical point and that fear memory consolidation leads to a shift in dynamics. Finally, I investigate the role of NREM sleep in facilitating memory consolidation using a conductance-based model of neuronal activity that can easily switch between modes of activity loosely representing waking and NREM sleep. Analysis of model simulations revealed that oscillations associated with NREM sleep promote a phase-based coding of information; neurons with high firing rates during periods of wake lead spiking activity during NREM oscillations. I show that when phase-coding is active in both simulations and in vivo, synaptic plasticity selectively strengthens the input to neurons firing late in the oscillation while simultaneously reducing input to neurons firing early in the oscillation. The effect is a net homogenization of firing rates observed in multiple other studies, and subsequently leads to recruitment of new neurons into a memory engram and information transfer from fast firing neurons to slow firing neurons. Taken together, my work outlines important, newly-discovered features of neuronal network dynamics related to memory encoding and consolidation: networks near criticality promote recruitment of additional neurons into stable firing patterns through NREM-associated oscillations and subsequently consolidates information into memories through phase-based coding.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162991/1/qmskill_1.pd

Global neural rhythm control by local neuromodulation

Neural oscillations are a ubiquitous form of neural activity seen across scales and modalities. These neural rhythms correlate with diverse cognitive functions and brain states. One mechanism for changing the oscillatory dynamics of large neuronal populations is through neuromodulator activity. An intriguing phenomenon explored here is when local neuromodulation of a distinct neuron type within a single brain nucleus exerts a powerful influence on global cortical rhythms. One approach to investigate the impact of local circuits on global rhythms is through optogenetic techniques. My first project involves the statistical analysis of electrophysiological recordings of an optogenetically-mediated Parkinsonian phenotype. Empirical studies demonstrate that Parkinsonian motor deficits correlate with the emergence of exaggerated beta frequency (15-30 Hz) oscillations throughout the cortico-basal ganglia-thalamic network. However, the mechanism of these aberrant oscillatory dynamics is not well understood. A previous modeling study predicted that cholinergic neuromodulation of medium spiny neurons in the striatum of the basal ganglia may mediate the pathologic beta rhythm. Here, this hypothesis was tested using selective optogenetic stimulation of striatal cholinergic interneurons in normal mice; stimulation robustly and reversibly amplified beta oscillations and Parkinsonian motor symptoms. The modulation of global rhythms by local networks was further studied using computational modeling in the context of intrathalamic neuromodulation. While intrathalamic vasoactive intestinal peptide (VIP) is known to cause long-lasting excitation in vitro, its in vivo dynamical effects have not been reported. Here, biophysical computational models were used to elucidate the impact of VIP on thalamocortical dynamics during sleep and propofol general anesthesia. The modeling results suggest that VIP can form robust sleep spindle oscillations and control aspects of sleep architecture through a novel homeostatic mechanism. This homeostatic mechanism would be inhibited by general anesthesia, representing a new mechanism contributing to anesthetic-induced loss of consciousness. While the previous two projects differed in their use of empirical versus theoretical methods, a challenge common to both domains is the difficulty in visualizing and analyzing large multi-dimensional datasets. A tool to mitigate these issues is introduced here: GIMBL-Vis is a Graphical Interactive Multi-dimensional extensiBLe Visualization toolbox for Matlab. This toolbox simplifies the process of exploring multi-dimensional data in Matlab by providing a graphical interface for visualization and analysis. Furthermore, it provides an extensible open platform for distributed development by the community

A Computational Study of Sleep and the Hemispheres of the Brain

Sleep and sleep cycles have been studied for over a century, and scientists have worked on modeling sleep for nearly as long as computers have existed. Despite this extensive study, sleep still holds many mysteries. Larger and more extensive sleep-wake models have been developed, and the circadian drive has been depicted in numerous fashions, as well as incorporated into scores of studies. With the ever-growing knowledge of sleep comes the need to find more ways to examine, quantify, and define it in the context of the most complex part of the human anatomy – the brain. Presented here is the development of a computational model that explores the activity of individual neurons, modeled with coupled nonlinear ordinary differential equations, in key sleep-related brain regions. The activity patterns of the individual neurons are studied, as well as their synchronization with other neurons within the same region. The model is expanded into two separate interacting hemispheres, whose activity and synchronization reveal chimera-like activity. Multiple different perspectives on jetlag are presented, exploring the impact of circadian rhythm changes. Unihemispheric sleep, the unusual form of sleep exhibited by some ocean creatures and species of birds, is observed, as well as asymmetric sleep, which occurs in human subjects suffering from sleep apnea. These investigations provide a new perspective on the intricate balance between the neural activity in different brain regions that drives the essential phenomenon that is sleep

Light sleep versus slow wave sleep in memory consolidation:a question of global versus local processes?

Contains fulltext : 135488.pdf (publisher's version ) (Closed access)Sleep is strongly involved in memory consolidation, but its role remains unclear. 'Sleep replay', the active potentiation of relevant synaptic connections via reactivation of patterns of network activity that occurred during previous experience, has received considerable attention. Alternatively, sleep has been suggested to regulate synaptic weights homeostatically and nonspecifically, thereby improving the signal:noise ratio of memory traces. Here, we reconcile these theories by highlighting the distinction between light and deep nonrapid eye movement (NREM) sleep. Specifically, we draw on recent studies to suggest a link between light NREM and active potentiation, and between deep NREM and homeostatic regulation. This framework could serve as a key for interpreting the physiology of sleep stages and reconciling inconsistencies in terminology in this field

SLEEPING WHILE AWAKE: A NEUROPHYSIOLOGICAL INVESTIGATION ON SLEEP DURING WAKEFULNESS.

Il sonno e la veglia vengono comunemente considerati come due stati distinti. L\u2019alternanza tra essi, la cui presenza \ue8 stata dimostrata in ogni specie animale studiata fino ad oggi, sembra essere una delle caratteristiche che definisce la nostra vita. Allo stesso tempo, per\uf2, le scoperte portate alla luce negli ultimi decenni hanno offuscato i confini tra questi due stati. I meccanismi del sonno hanno sempre affascinato i neurofisiologi, che infatti, nell\u2019ultimo secolo, li hanno caratterizzati in dettaglio: ora sappiamo che all\u2019attivit\ue0 del sonno sottost\ue0 una specifica attivit\ue0 neuronale chiamata slow oscillation. La slow oscillation, che \ue8 costituita da (ancora una volta) un\u2019alternanza tra periodi di attivit\ue0 e periodi di iperpolarizzazione e silenzio neuronale (OFF-periods), \ue8 la modalit\ue0 base di attivazione del cervello dormiente. Questa alternanza \ue8 dovuta alla tendenza dei neuroni surante lo stato di sonno, di passare ad un periodo silente dopo un\u2019attivazione iniziale, una tendenza a cui viene dato il nome di bistabilit\ue0 neuronale. Molti studi hanno dimostrato come la bistabilit\ue0 neuronale tipica del sonno ed i relativi OFF-periods, possano accadere anche durante la veglia in particolari condizioni patologiche, nelle transizioni del sonno e durante le deprivazioni di sonno. Per questo motivo, se accettassimo che la bistabilit\ue0 neuronale e gli OFF-periods rappresentino una caratteristica fondamentale del sonno, allora dovremmo ammettere che stiamo assistendo ad un cambio di paradigma: da una prospettiva neurofisiologica il sonno pu\uf2 intrudere nella veglia. In questa tesi ho analizzato i nuovi -fluidi- confini tra sonno e veglia e le possibili implicazioni di questi nel problema della persistenza personale attraverso il tempo. Inoltre, ho studiato le implicazioni cliniche dell\u2019intrusione di sonno nella veglia in pazienti con lesioni cerebrali focali di natura ischemica. In particolare, i miei obiettivi sono stati: 1) Dimostrare come la bistabilit\ue0 neuronale possa essere responsabile della perdita di funzione nei pazienti affetti da ischemia cerebrale e come questo potrebbe avere implicazioni nello studio della patofisiologia dell\u2019ischemia cerebrale e nella sua terapia; 2) Stabilire le basi per un modello di sonno locale presente nella vita di tutti i giorni: la sensazione di sonnolenza. Infatti, essa potrebbe riflettere la presenza di porzioni di corteccia in stato di sonno, ma durante lo stato di veglia; 3) Difendere il criterio biologico di identit\ue0, che troverebbe nell\u2019attivit\ue0 cerebrale la continuit\ue0 necessaria al mantenimento della nostra identit\ue0 nel tempo.Sleep and wakefulness are considered two mutually exclusive states. The alternation between those two states seems to be a defining characteristic of our life, a ubiquitous phenomenon demonstrated in every animal species investigated so far. However, during the last decade, advances in neurophysiology have blurred the boundaries between those states. The mechanisms of sleep have always intrigued neurophysiologists and great advances have been made over the last century in understanding them: we now know that the defining characteristic underlying sleep activity is a specific pattern of neuronal activity, namely the slow oscillation. The slow oscillation, which is characterized by the periodic alternation between periods of activity (ON-periods) and periods of hyperpolarization and neuronal silence (OFF-periods) is the default mode of activity of the sleeping cortex. This alternation is due to the tendency of neurons to fall into a silent period after an initial activation; such tendency is known as \u201cbistability\u201d. There is accumulating evidence that sleep-like bistability, and the ensuing OFF-periods, may occur locally in the awake human brain in some pathological conditions, in sleep transition, as well as after sleep deprivation. Therefore, to the extent that bistability and OFF periods represents the basic neuronal features of sleep, a paradigm shift is in place: from a neurophysiological perspective sleep can intrude into wakefulness. In this thesis, I explore the fluid boundaries between sleep and wakefulness and investigate their possible implications on the problem of personal persistence over time. Moreover, I study the clinical implications of the intrusion of sleep into wakefulness in patients with focal brain injury due to stroke. Specifically, I aim to: 1) show how the sleep-like bistability can be responsible for the loss of function in stroke patients. This may have implications for understanding the pathophysiology of stroke and helping to foster recovery; 2) establish the basis for a model of local sleep that might be present in the everyday life, id est the sensation of sleepiness. Indeed, sleepiness could reflect islands of sleep during wakefulness; 3) advocate the biological criterion of identity, in which the continuity necessary for maintaining ourselves over time could be represented by never resting activity in the brain

Learning cortical representations through perturbed and adversarial dreaming.

Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the characteristic creative nature of dreams suggests that learning semantic representations may go beyond merely replaying previous experiences. We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs). Learning in our model is organized across three different global brain states mimicking wakefulness, NREM and REM sleep, optimizing different, but complementary objective functions. We train the model on standard datasets of natural images and evaluate the quality of the learned representations. Our results suggest that generating new, virtual sensory inputs via adversarial dreaming during REM sleep is essential for extracting semantic concepts, while replaying episodic memories via perturbed dreaming during NREM sleep improves the robustness of latent representations. The model provides a new computational perspective on sleep states, memory replay and dreams and suggests a cortical implementation of GANs

A computational study of sleep and the hemispheres of the brain

Sleep and sleep cycles have been studied for over a century, and scientists have worked on modeling sleep for nearly as long as computers have existed. Despite this extensive study, sleep still holds many mysteries. Larger and more extensive sleep-wake models have been developed, and the circadian drive has been depicted in numerous fashions, as well as incorporated into scores of studies. With the ever-growing knowledge of sleep comes the need to find more ways to examine, quantify, and define it in the context of the most complex part of the human anatomy -- the brain. Presented here is the development of a computational model that explores the activity of individual neurons, modeled with coupled nonlinear ordinary differential equations, in key sleep-related brain regions. The activity patterns of the individual neurons are studied, as well as their synchronization with other neurons within the same region. The model is expanded into two separate interacting hemispheres, whose activity and synchronization reveal chimera-like activity. Multiple different perspectives on jetlag are presented, exploring the impact of circadian rhythm changes. Unihemispheric sleep, the unusual form of sleep exhibited by some ocean creatures and species of birds, is observed, as well as asymmetric sleep, which occurs in human subjects suffering from sleep apnea. These investigations provide a new perspective on the intricate balance between the neural activity in different brain regions that drives the essential phenomenon that is sleep --Abstract, page iii