Dynamic organization of human brain function and its relevance for psychosis vulnerability

The brain is the substrate of a complex dynamic system providing a remarkably varied range of functionalities, going from simple perception to higher-level cognition. Disturbances in its complex dynamics can cause an equally vast variety of mental disorders. One such brain disorder is schizophrenia, a neurodevelopmental disease characterized by abnormal perception of reality that manifests in symptoms like hallucinations or delusions. Even though the brain is known to be affected in schizophrenia, the exact pathophysiology underlying its developmental course is still mostly unknown. In this thesis, we develop and apply methods to look into ongoing brain function measured through magnetic resonance imaging (MRI) and evaluate the potential of these approaches for improving our understanding of psychosis vulnerability and schizophrenia. We focus on patients with chromosome 22q11.2 deletion syndrome (22q11DS), a genetic disorder that comes with a 30fold increased risk for developing schizophrenia, thus enabling the study of developmental alterations and risk factors that precede the onset of full-blown psychosis. We first examine temporal variability of the blood oxygenation level dependent (BOLD) signal, a voxelwise measure of dynamic fluctuations. As static functional connectivity (sFC) is scaled for variance, we also compare BOLD signal variability with altered sFC. The broad pattern of altered BOLD signal variability in 22q11DS partly, but not entirely, overlaps with altered sFC, suggesting a complex non-linear relationship between the two measures. Further, testing for altered BOLD signal variability in patients with higher psychotic symptoms, we find reduced values in the dorsal anterior cingulate cortex (dACC), a central node of the salience network (SN). Going beyond a voxel-wise measure of brain dynamics, we next look into aberrant dynamics of large-scale functional brain networks. We use innovation-driven co-activation patterns (iCAPs), an approach that stands out by its ability to recover spatial and temporal overlap of functional brain networks. As in the original iCAPs framework such spatial overlap can introduce spurious temporal activations, we propose a novel spatio-temporal regression framework that relies on transient-based constraints to overcome this limitation. With this improved scheme, we probe into clinical risk factors of psychosis in 22q11DS and find aberrant activation and coupling of functional brain networks, again implicating the SN, as well as the amygdala and hippocampus. Finally, we explore the implications of structural network topology on functional dynamics, by combining iCAPs with network control theory, an approach that relies on a dynamic model to predict the energy required for controlling the brain's state given its structural connectivity. We find that the brain operates in an energetically optimal way, spending less time in brain states that require higher control energy. In patients with 22q11DS, this relationship is less pronounced, suggesting less efficient functional brain dynamics in these patients. In summary, our results confirm that the dynamic nature of brain function contains essential information and warrants further attention for the development of clinically relevant imaging markers for psychosis vulnerability. Moreover, we provide initial evidence for aberrant relationship between brain function and structure in patients with 22q11DS that merits further exploration.

Exploring dynamic functional connectivity by incorporating prior knowledge of brain structure

Anjali Bagunu Tarun

The synchronized firing of distant neuronal populations gives rise to a wide array of functional brain networks that underlie human brain function. Given the enormous perception, learning, and cognition potential of the human brain, it is not surprising that network-level representations of brain function reveal intrinsically rich organizational structure. What remains puzzling is how the human brain maintains its vast repertoire of functional connectivity (FC) states despite being constrained by the underlying fixed anatomical substrate. Advances in modern neuroimaging technologies, such as diffusion-weighted magnetic resonance imaging (DW-MRI), have made it possible to map the brain's anatomical scaffold, while functional MRI (fMRI) provides complementary information on neural activity. In this thesis, we develop and apply methods for combining DW-MRI and fMRI data into an integrated framework to analyze the interplay between brain structure and function. We first explored the dynamics of brain function during wakefulness and across the different non-rapid eye movement (NREM) sleep stages. We applied the innovation-driven co-activation pattern (iCAP) analysis to uncover the spatial and temporal organization of overlapping large-scale brain networks. Our results reveal new spatial patterns covering regions that support the physiological organization of sleep and arousal. Contrary to the previously observed decreasing FC that accompanies increasing sleep depth, we instead observe a surge of network activity and cross-network interactions during NREM stage 2, followed by an abrupt decrease in NREM stage 3. In the next step of the thesis, we propose a new method that models all brain voxels as nodes of a high-resolution voxel-level brain graph. We provide two ways to construct the graph and we characterize their properties by performing a spectral analysis of the graph Laplacian operator. Our findings show that despite the huge dimensionality of the proposed brain graphs, the majority of the structural information is captured by the Laplacian's lowest frequency eigenmodes. The lower end of the Laplacian spectra also captures about 85% of the energy content of functional MRI, suggesting that functional patterns are overall smooth over the structure, thus providing, for the first time, a direct and quantitative measure of how much brain function is shaped by the anatomy. Going beyond a scalar measure of the SC-FC link, we introduce a new framework that interpolates gray matter signals onto the white matter using the structure embedded in the voxel-level brain grid to guide the process. This enables visualization of key white matter structures that link temporally coherent gray matter areas. We found whole-brain structure-function networks that extend currently known spatial patterns that are limited within the gray matter only. Finally, we assessed the collective mediation of white matter pathways by giving a quantitative measure of the overall anatomical range between temporally coherent gray matter areas. We utilized a canonical model of graph diffusion to extract the anatomical range of functional network interactions. We find that this measure meaningfully differentiates brain regions according to a behaviorally relevant macroscale gradient that divides the cortex between low-level primary sensory areas and high-level cognitive functions.

EPFL2020

Quantitative Susceptibility Mapping in the Human Brain

Diana Khabipova

Magnetic resonance imaging (MRI) offers a good tissue contrast and the ability to visualize many disease related morphologies. The work presented in this thesis investigates the study of underlying structure of the brain using quantitative methods with a special emphasis on quantitative susceptibility mapping (QSM). Magnetic susceptibility reflects the interaction of a material to the magnetic field and measures in biological tissues the magnetic susceptibility of inclusions. The reconstruction of QSM requires further processing steps as the magnetic field produced by the sources needs to be disentangled from the orders of magnitude bigger background field. The produced field also depends not only on the shape and the orientation, but also on the anisotropy of susceptibility and the microstructural compartmentalization of the biological source. For this reason, reconstruction methods need to be capable to calculate accurate values for different brain regions as well as applicable in the everyday clinical diagnosis. Within the framework of the thesis a data acquisition protocol based on a multiple-echo gradient echo sequence as well as a post-processing protocol was implemented. One of the processing steps, the background removal method, was applied to preserve the brain regions close to the cerebrospinal fluid (CSF). This method outperforms state of the art methods in this regions but is computationally intensive. Different brain regions were studied using quantitative methods with special emphasis on the QSM. A new method, modulated closed form solution, with extremely fast computational time is proposed. The comparison with other single orientation methods revealed similar results and the highest correlation to the state-of-the-art method (COSMOS) in the deep gray matter. The R2* maps calculated from the same dataset are also able to distinguish the deep gray matter structures with a similar quality. However, QSM shows a higher sensitivity in early stage multiple sclerosis lesions as well as white matter-gray matter structures. In the human cortex the obtained cortical maps show enhancement of primary sensory cortex, which is known to be highly myelinated, on three evaluated quantitative contrasts R1,R2* and susceptibility. The contrasts based on the relaxation rates, R1 and R2*, show a monotonically decrease from the white matter to the CSF imitating the decrease in iron and myelin. The susceptibility behaviour is more complex as iron and myelin content introduce an opposing sensitivity, allowing to study iron and myelin content when combining the three contrasts. The microstructural organization of white matter influences the R2*, R2 as well as field map from which QSM is calculated. This structure leads to an orientation dependence of the studied contrasts and for QSM the spherical assumption is not valid anymore. Therefore a new QSM method is introduced, which includes the Lorentzian correction in white matter. Main fibres such as forceps major and minor were analysed for the three different quantitative contrasts. The anisotropic component associated with susceptibility is similar for the relaxation rates whereas the isotropic component of R2* shows a higher variability. The resulting deep gray matter structure of the new QSM method remained similar to the state-of-the-art method when comparing the isotropic component but calculates physically meaningful susceptibility maps with improved contrast between known fibre bundles.

EPFL2016

Uncovering the neural substrates of communication between the brain and ventral nerve cord in Drosophila melanogaster (italic)

Florian Aymanns

Despite a long history of research in motor control, the exact mechanism of how the brain communicates with the the invertebrate ventral nerve cord (VNC) and the vertebrate spinal cord on a single neuron basis remains largely elusive.Drosophila melanogaster is an ideal model organism to address the role of individual neurons, thanks to its stereotyped nervous system.Descending neurons (DNs), interneurons in the brain that project to the invertebrate ventral nerve cord or vertebrate spinal cord, have been shown to elicit specific behaviors upon optogenetic activation in Drosophila.These results suggest that behavior is controlled by sparse sets of command neurons, each eliciting a particular behavior.However, it remains to be seen how many DNs are involved in the control of naturalistic behaviors and what other information they might encode.In order to avoid commands leading to physically impossible or destabilizing actions, the brain has to be aware of the current behavior state.Ascending neurons (ANs), interneurons in the invertebrate ventral nerve cord or vertebrate spinal cord projecting to the brain, are likely conveying this information to the brain.To which degree of fidelity and in what way ANs encode behavior state remains unclear.To address these questions, we developed a dissection approach that allows functional imaging of ANs and DNs in the cervical connective.This dissection gives us optical access to the cervical connective and parts of the VNC in behaving adult Drosophila such that a two-photon fluorescence microscope can be used to image neural activity via calcium indicators.We began by imaging a new library of sparse split GAL4 driver lines targeting ANs.Our recordings from 247 genetically identifiable ANs revealed neurons encoding walking, resting, turning, eye grooming, and foreleg movements.Anatomical characterization of these ANs showed that they predominantly project to two brain regions: the anterior ventrolateral protocerebrum (AVLP) and the gnathal ganglion (GNG).Our results suggest that ANs encoding the motion of the animal with respect to the surrounding environment (self-motion) predominantly project to the AVLP, an integrative sensory hub. ANs projecting to the GNG mostly encode discrete actions.We then proceeded to image populations of DNs.The majority of DNs we recorded encoded walking behavior, with smaller fractions encoding resting and head grooming.Some of these neurons are likely to drive walking, but we also identified neurons encoding walking speed and turning.This suggests that a core set of DNs drives a behavior whose features can be modulated by additional DNs.Besides encoding behaviors, some DNs encode sensory information, including the presence of odors and deflection of the antennae.

EPFL2022