The (un)conscious mouse as a model for human brain functions: key principles of anesthesia and their impact on translational neuroimaging

In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca(2+) imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species

Functional network antagonism and consciousness

Spontaneous brain activity changes across states of consciousness. A particular consciousness-mediated configuration is the anticorrelations between the default mode network and other brain regions. What this antagonistic organization implies about consciousness to date remains inconclusive. In this Perspective Article, we propose that anticorrelations are the physiological expression of the concept of segregation, namely the brain’s capacity to show selectivity in the way areas will be functionally connected. We postulate that this effect is mediated by the process of neural inhibition, by regulating global and local inhibitory activity. While recognizing that this effect can also result from other mechanisms, neural inhibition helps the understanding of how network metastability is affected after disrupting local and global neural balance. In combination with relevant theories of consciousness, we suggest that anticorrelations are a physiological prior that can work as a marker of preserved consciousness. We predict that if the brain is not in a state to host anticorrelations, then most likely the individual does not entertain subjective experience. We believe that this link between anticorrelations and the underlying physiology will help not only to comprehend how consciousness happens, but also conceptualize effective interventions for treating consciousness disorders in which anticorrelations seem particularly affected

On Arousal and the Internal Regulation of Brain Function: Theory and Evidence across Modalities and Species

The brain is an organ. It is subject to the same physiological regulatory processes that engage the rest of the body’s organs, sculpted over hundreds of millions of years to sustain life so effectively. The central message of this thesis is that the holistic functioning of the brain, rather than operating at some level above or independent from these systemic regulatory processes, is deeply related to them. In short, as our limited attention spans might suggest: brain function is internally regulated. I propose that this internal regulation is a primary function of intrinsic brain activity. Chapter 2 provides a theoretical treatment of this issue, recasting intrinsic activity as an internal regulatory process operating on the brain’s temporal “states” and spatial “networks”. After establishing this framework, Chapters 3 and 4 provide tests of specific predictions. Thus, Chapter 3 confirms, in humans and macaque monkeys, the presence of topographically organized traveling waves occurring in synchrony with ongoing arousal fluctuations, with propagation occurring in parallel within the neocortex, striatum, thalamus, and cerebellum. This process is argued to provide a heretofore lacking physiological account of “resting-state functional connectivity” and related phenomenology. Chapter 4 extends this observation by demonstrating a continuous and tightly coordinated temporal evolution of brain, body, and behavioral states along a latent arousal cycle. Across multiple recording techniques and species, this cyclic trajectory is shown to be coupled to the traveling wave process described in Chapter 3, thus providing a parsimonious and integrative account of intrinsic brain activity and its spatiotemporal dynamics. Taken together, this thesis argues for the existence of an intrinsic regulatory process for global brain function

UNDERSTANDING WHOLE BRAIN ACTIVITY THROUGH BRAIN NETWORK MODELS

The central nervous system coordinates many neural subpopulations connected via macroscale white matter architecture and surface cortical connections to produce complex behavior depending on environmental cues. The activity occurs over different scales, from information transfer between individual neurons at the synapse level, to macroscale coordination of neural populations used to maximize information transfer between specialized brain regions. The whole brain activity measured through functional Magnetic Resonance Imaging (fMRI), allows us to observe how these large neural populations interact over time. Researchers have developed a set of Brain Network Models (BNMs), that simulate brain activity using the macroscale structure and different mathematical models to represent populational neural activity. These simulations have been able to reproduce properties of fMRI especially those averaged over long periods of time. These models represent a step towards an individualized model of brain activity, which is of clinical interest, as they can be constructed from individual estimates of the structural network. To find a good BNM to fit the individual fMRI data, however, is a difficult problem as BNMs represent a large family of mathematical models. Moreover, a large set of BNMs have reproduced time averaged metrics that have been used so far to compare the models with the fMRI data. In this thesis, we extend previous work on BNM research by establishing new dynamic metrics that would allow us to better differentiate between BNMs simulations on how well they reproduce measured fMRI dynamics (Chapter 2). In Chapter 3, we directly compare transient short-term trajectories by synchronizing the outputs of a BNM in relation to observed fMRI timeseries using a novel Machine Learning Algorithm, Neural Ordinary Differential Equations (ODE). Finally, we show that the Neural ODE can be used as its own stand-alone generative model and is able to simulate more realistic fMRI signals as they are able to reproduce complex metrics that previous models have not been able to recapitulate (Chapter 4). In short, we demonstrate that we have made progress in developing and quantifying BNMs and advanced the research of more realistic whole brain simulations.Ph.D

DETECTING BRAIN-WIDE INTRINSIC CONNECTIVITY NETWORKS USING fMRI IN MICE

Functional neuroimaging methods in mice are essential for unraveling complex neuronal networks that underlie maladaptive behavior in neurological disorder models. By using fMRI to detect intrinsic connectivity networks in mice, we can examine large scale alteration in brain activity and functional connectivity to establish causal associations in brain network changes. The work presented in this dissertation is organized into five chapters. Chapter 1 provides the necessary background required to understand how functional neuroimaging tools such as fMRI detect signal changes attributed to spontaneous neuronal activity of intrinsic connectivity networks in mice. Chapter 2 describes the development of our isotropic fMRI acquisition sequence in mice and semi-automated pipeline for mouse fMRI data. Naïve mouse fMRI scans were used to validated the pipeline by reliably and reproducibly extracting intrinsic connectivity networks. Chapter 3 establishes the development and validation of a novel superparamagenetic iron-oxide nanoparticle to enhance fMRI signal sensitivity. Chapter 4 studies the effects norepinephrine released by locus coeruleus neurons on the default mode network in mice. Norepinephrine release selectively enhanced neuronal activity and connectivity in the Frontal module of the default mode network by suppressing information flow from the Retrosplenial-Hippocampal to the Association modules. Chapter 5 addresses the implications of our findings and addresses the limitations and future studies that can be conducted to expand on this research.Doctor of Philosoph

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Investigating the neural correlates of ongoing experience

Spontaneous thoughts are heterogeneous and inherently dynamic. Despite their time-variant properties, studies exploring spontaneous thoughts have identified thematic patterns that exhibit trait-like characteristics and are stable across time. Concurrently, structural and functional neuroimaging studies have shown unique and stable whole-brain network configurations linked to behaviour either through the static and dynamic intrinsic communication and activity of their core regions or through informational exchange with each other. This thesis aimed to explore how these within and between network interactions at different temporal scales might relate to variations in ongoing experience. We utilised different neuroimaging modalities (diffusion weighted and functional magnetic resonance imaging) and applied both static and dynamic analyses techniques. We found evidence of inter-individual variation in all cases associated with different patterns of spontaneous thoughts. Experiment 1 found that variation in white matter architecture projecting to the hippocampus, as well as the stable functional interaction of the hippocampus with the medial prefrontal cortex were linked to the tendency of experiencing thoughts related to the future or the past. Experiment 2 found that static functional connectivity of the precuneus and a lateral fronto-temporal network was related to visual imagery. Furthermore, we found that coupling of a lateral visual network with regions of the brainstem and cerebellum was associated with ruminative thinking, self-consciousness and attentional problems. Importantly, our results highlighted an interaction among these associations, where the brainstem visual network coupling moderated the relationship between parietal-frontal regions and reports of visual imagery. Finally, Experiment 3 used hidden Markov modelling to identify dynamic neural states linked to thoughts related to problem-solving and less intrusive thinking, as well as better physical and mental health. Collectively, these studies highlight the utility of using both static and dynamic measures of neural function to understand patterns of ongoing experience