Time resolved functional brain networks : a novel method and developmental perspective

Functional neuroimaging has helped elucidating the complexity of brain function in ever more detail during the last 30 years. In this time the concepts used to understand how the brain works has also developed from a focus on regional activation to a network based whole brain perspective (Deco et al., 2015). The understanding that the brain is not just merely responding to external demands but is itself a co-creator of its perceived reality is now the default perspective (Buzsáki and Fernández-Ruiz, 2019). This means that the brain is never resting and its intrinsic architecture is the basis for any task related modulation (Cole et al., 2014). As often in science, understanding and technological advances go hand in hand. For the advancement of the functional neuroimaging field during the last decade, methods that are able to track, capture and model time resolved connectivity changes has been essential (Lurie et al., 2020). This development is an ongoing process. Part of the work presented in this thesis is a small contribution to this collective endeavor. The first theme in the thesis is time resolved connectivity of functional brain networks. This theme is present in Study I which presents a novel method for analysis of time resolved connectivity using BOLD fMRI data. With this method, subnetworks in the brain are defined dynamically. It allows for connectivity changes to be tracked from time point to time point while respecting the temporal ordering of the data. It also provides relational properties in terms of differences in phase coherence between simultaneously integrated networks and their gradual change. The method can be used see how whole brain connectivity configurations recure in quasi-cyclic patterns. Finally, the method is able to estimate flexibility and modularity of individual brain areas. The method is applied in Study III in order to understand how premature birth effects flexibility and modularity of intrinsic functional brain networks. Beyond the purely scientific endeavor to understand how the brain creates cognition, consciousness, perception and supports motor function, neuroimaging research has also been helpful in elucidating normal brain development and neurodevelopmental disorders. The second theme in this thesis is brain development in extremely preterm born children at school age. This theme is the focus of Study II & III. Study II investigates the prevalence of discrete white matter abnormalities at school age in children born extremely preterm and the relationship to neuro-motor outcome. The prevalence of white matter abnormalities was high but there was no relationship to an unfavorable outcome. Also, a longitudinal association to neonatal white matter injury was seen. While discrete white matter abnormalities were not correlated to quantitative measures of white matter volume and white matter integrity, neonatal white matter injury was associated with lower volume and integrity at age 8- 11 years. Moreover, neonatal white matter injury was associated with lower processing speed at 12 years. The third and final study investigated flexibility and modularity as well as lateralization of intrinsic networks in children born extremely preterm at age 8-11 years. No significant differences in either flexibility or modularity was seen for any intrinsic network after correcting for multiple comparisons. However, at the level of individual brain areas, preterm children showed decreased flexibility in both the basal ganglia and thalamus. Also, children born extremely preterm had a decreased level of lateralization in most networks

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Exploring Changes in Functional Connectivity Associated with the Development of Cognitive Flexibility During Middle Childhood

Cognitive flexibility is a subset of executive function that involves flexibly adapting one’s behavior to meet the demands of a changing environment. In a cognitive task, this often entails shifts of attention between dimensions of a stimulus or flexibly changing response sets. This skill improves greatly throughout middle childhood and is supported by a frontal-parietal neural network. The level of synchrony in activation, or functional connectivity, between frontal and parietal regions has been reported to increase over development even after cognitive flexibility has stabilized. The current study aims to assess changes in functional connectivity across the age range when this ability most rapidly develops. fNIRS was used to measure synchrony in hemodynamic activation of the frontal and parietal cortices in children at age 5, 7 and 9. Functional connectivity was measured at a resting state and while children performed three tasks requiring cognitive flexibility. Task performance and connectivity strength were compared across age groups. Cognitive flexibility improved greatly with age, aligning with previous literature. Evidence was found for refinement of local connectivity within the frontal cortex, such that weaker connections decreased in strength with age and stronger connections increased in strength. Further, connectivity between frontal and parietal regions was greater for 9-year-olds when task demands increased, reflecting greater synchrony of this network with age. Understanding the neural dynamics associated with the development of flexibility promotes a better understanding of the brain-behavior relationship. This line of research can also allow us to make comparisons with atypically developing populations, such as those with Autism, who have impairments in this skill. By understanding how neural architecture develops to support executive function in typical populations, we can better understand how deficits arise from atypical trajectories

The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition

A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition

Network Approaches to Understand Individual Differences in Brain Connectivity: Opportunities for Personality Neuroscience

Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from noninvasive neuroimaging techniques such as functional magnetic resonance imaging (fMRI). An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior

Centralized and distributed cognitive task processing in the human connectome

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectomes (FC) . A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straight-forward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting-state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated to different functional brain networks, and use the proposed measure to infer changes in the information processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well grounded mathematical quantification of connectivity changes associated to cognitive processing in large-scale brain networks.Comment: 22 pages main, 6 pages supplementary, 6 figures, 5 supplementary figures, 1 table, 1 supplementary table. arXiv admin note: text overlap with arXiv:1710.0219