Intrinsic Functional Connectivity of the Brain in Adults with a Single Cerebral Hemisphere

A reliable set of functional brain networks is found in healthy people and thought to underlie our cognition, emotion, and behavior. Here, we investigated these networks by quantifying intrinsic functional connectivity in six individuals who had undergone surgical removal of one hemisphere. Hemispherectomy subjects and healthy controls were scanned with identical parameters on the same scanner and compared to a large normative sample (n = 1,482). Surprisingly, hemispherectomy subjects and controls all showed strong and equivalent intrahemispheric connectivity between brain regions typically assigned to the same functional network. Connectivity between parts of different networks, however, was markedly increased for almost all hemispherectomy participants and across all networks. These results support the hypothesis of a shared set of functional networks that underlie cognition and suggest that between-network interactions may characterize functional reorganization in hemispherectomy

The Structural Basis for Brain Health

Cardiovascular disease (CVD) remains the leading cause of mortality in the United States. Stroke and dementia are the leading causes of adult disability worldwide, and the 5th and 6th leading causes of mortality respectively in the United States. Furthermore, CVD annually accounts for approximately $330 billion in direct and indirect costs in the United States: approximately one in seven health care dollars is spent on CVD. While these diseases have different etiologies, and present with different clinical manifestations and prognosis, converging evidence increasingly supports the idea of CVD as a common pathophysiological origin of cerebrovascular disease, potentially indicating a complex interplay between brain health and cardiovascular health. In this thesis, we leverage methodological advancements in systems and computational neurosciences related to the human brain connectome to assess individual topological network organization and integrity in acute and chronic stroke cohorts, and in a non-stroke cohort with varying CV risk factor burden, using graph theory and network analysis. We propose measures that underly neuroanatomical mechanisms that constitute efficient transfer of information and brain health. We demonstrate the impact of cardiovascular risk factors on brain health, even before overt clinical manifestation, and the resulting impact on cognitive performance, and further determine the underlying pathophysiology relating white matter disease and post-stroke outcomes

Imaging-based parcellations of the human brain

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation — defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions — is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies

Layers Of Maturation In Cortical Hierarchies

Hierarchies form critical scaffolds for top-down processing but are often multiplex. In the brain, multiple layers of complex hierarchies intersect, dissociate, and re-converge over the lifespan. Although aspects of local hierarchical organizations are well-mapped for sensory systems, the fashion by which hierarchical organization extends globally is unknown. Human neuroimaging provides a means by which to observe both the developmental emergence and functions of global neurohierarchical organization. Here, we leveraged these advances to distill multiple layers of hierarchical formation across diverse brain-tissue quantifications. We demonstrate that these layers form common and dissociable biomarkers of the developmental emergence of complex cognition. Our results indicate that multiplex neurocognitive development both processes across a normative hierarchical pattern and contributes to engraining the pattern into cortical function. Further, our results suggest that neurocognitive development is largely contemporaneous with neurocognitive aging in an integrated, flexible lifespan sequence

Machine Learning Techniques for Improved Functional Brain Parcellation

Brain parcellation studies are fundamental for neuroscience as they serve as a bridge between anatomy and function, helping researchers interpret how functions are distributed across different brain regions. However, two substantial challenges exist in current imaging-based brain parcellation studies: large variations in the functional organization across individuals and the intrinsic spatial dependence which causes nearby brain locations to have a similar function. This thesis presents a series of projects aimed to tackle these challenges from different perspectives by using advanced machine learning techniques. To handle the challenge of individual variability in building precise individual parcellations, Chapter 3 introduces a novel hierarchical Bayesian brain parcellation framework. This framework learns a brain probabilistic parcellation by integrating across diverse datasets. For single individuals, the framework optimally combines the limited individual data with the group probability map, resulting in improved individual maps. We found that the resultant individual parcellation based on only 10 minutes of imaging scans can achieve an equivalent performance to the ones using 100 minutes of data alone. These improved individual parcellations are essential to accurately capture functional variations across studied populations. The intrinsic spatial dependence between brain locations poses a significant challenge in both evaluating and generating brain parcellations. To address this, Chapter 2 presents a bias-free method for evaluating different brain parcellations, the distance-controlled boundary coefficient (DCBC). Compared to existing evaluation metrics that bias toward finer and spatial contiguous parcellations due to spatial smoothness, DCBC provides a fair evaluation by controlling the distance of brain location pairs, ensuring a direct comparison of parcellations in different resolutions. To address the intrinsic spatial dependence when learning parcellations, I propose a new model in Chapter 4, the multinomial restricted Boltzmann machine (m-RBM), that can be incorporated into the learning framework in Chapter 3. This model captures spatial structure between brain locations in its architecture. While simulations showed the utility of this type of model in estimating individual parcellations, we could not demonstrate better performance using real imaging data. Together, this thesis significantly advances the technical toolkit for deriving brain parcellations from functional imaging data. The developments open up new avenues for future research into human brain organization

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

The Molecular-enriched Functional Circuits Underlying Consciousness and Cognition

Homo Sapiens consist of trillions of atoms, each inanimate, yet somehow collectively constituting a conscious being. The fundamental question of how organisms are organised to beget consciousness and cognition has largely been approached through independent examination of the structure and function of the nervous system at varying levels of granularity. As neuroscience progresses, it has thus increasingly fragmented into separate streams of research which study the brain at these different scales. This has resulted in the field becoming “data rich, but theory poor”, which is largely attributable to the paucity of methods which bridge these levels of analysis to provide novel trans-hierarchical insights and inform unified theories. The research in this doctoral thesis therefore aims to explore how a specific type of multimodal analysis - Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) – can begin to bridge the theoretic void between molecular level mechanisms and systems levels dynamics to provide novel perspectives on the function and dysfunction of the brain. First, I provide a narrative synthesis of the challenges precluding a meaningful understanding of the human brain utilising conventional functional neuroimaging and outlining how incorporation of molecular information may help overcome these limitations. Specifically, by embedding functional dynamics in the molecular landscape of the brain, we can begin to move from the simple characterisation of “where” cognitive phenomena may be within the brain towards mechanistic accounts of “how” they are produced. Additionally, this offers enticing opportunities to link pharmacological treatments to novel molecular-network based biomarkers. Second, I explore how networks enriched with the spatial configurations of serotonergic and dopaminergic receptor subtypes are modulated by lysergic acid diethylamide (LSD) as compared to placebo in healthy participants. The results highlight the challenges of disentangling pharmacodynamics of drugs exhibiting rich pharmacology as well as identifying differential relationship between serotonergic and dopaminergic networks and phenomenological sub- components of psychedelic state. Third, I expand the remit of molecular-enriched network analyses beyond pure psychopharmacology to examine the direct and indirect actions of propofol anaesthesia on inhibitory and modulatory neurotransmission at both rest as well as during a naturalistic listening task. This work demonstrates for the first time that these molecular-networks can capture broader perceptual and cognitive-driven network reconfigurations as well as indirect pharmacological actions on neuromodulatory systems. Moreover, it provides evidence that the effects of propofol on consciousness are enacted through both direct inhibitory as well as indirect neuromodulatory mechanisms.Finally, I produce normative models of networks enriched with the principal neuromodulatory, excitatory, and inhibitory transmitter systems, testing their capacity to characterise neural dysfunction within and across several neuropsychiatric disorders. This work provides a computational foundation for large scale integration of molecular mechanisms and functional imaging to provide novel individualised biomarkers for neuropsychiatric disorders. Collectively, this thesis offers methodological and theoretical progress towards a trans-hierarchical characterisation of the human brain, providing insights into the neural correlates of both conscious contents and level as well as the perturbations underlying key neuropsychiatric conditions

Latent Factor Analysis of High-Dimensional Brain Imaging Data

Recent advances in neuroimaging study, especially functional magnetic resonance imaging (fMRI), has become an important tool in understanding the human brain. Human cognitive functions can be mapped with the brain functional organization through the high-resolution fMRI scans. However, the high-dimensional data with the increasing number of scanning tasks and subjects pose a challenge to existing methods that wasn’t optimized for high-dimensional imaging data. In this thesis, I develop advanced data-driven methods to help utilize more available sources of information in order to reveal more robust brain-behavior relationship. In the first chapter, I provide an overview of the current related research in fMRI and my contributions to the field. In the second chapter, I propose two extensions to the connectome-based predictive modeling (CPM) method that is able to combine multiple connectomes when building predictive models. The two extensions are both able to generate higher prediction accuracy than using the single connectome or the average of multiple connectomes, suggesting the advantage of incorporating multiple sources of information in predictive modeling. In the third chapter, I improve CPM from the target behavioral measure’s perspective. I propose another two extensions for CPM that are able to combine multiple available behavioral measures into a composite measure for CPM to predict. The derived composite measures are shown to be predicted more accurately than any other single behavioral measure, suggesting a more robust brainbehavior relationship. In the fourth chapter, I propose a nonlinear dimensionality reduction framework to embed fMRI data from multiple tasks into a low-dimensional space. This framework helps reveal the common brain state in the multiple available tasks while also help discover the differences among these tasks. The results also provide valuable insights into the various prediction performance based on connectomes from different tasks. In the fifth chapter, I propose an another hyerbolic geometry-based brain graph edge embedding framework. The framework is based on Poincar´e embedding and is able to more accurately represent edges in the brain graph in a low-dimensional space than traditional Euclidean geometry-based embedding. Utilizing the embedding, we are able to cluster edges of the brain graph into disjoint clusters. The edge clusters can then be used to define overlapping brain networks and the derived metrics like network overlapping number can be used to investigate functional flexibility of each brain region. Overall, these work provide rich data-driven methods that help understand the brain-behavioral relationship through predictive modeling and low-dimensional data representation