Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Santa Barbara Electronic Theses and Dissertations (2017)

The human brain is a complex system in which interactions of billions of neurons give rise to behavior. fMRI allows researchers to measure the functional activity of the working brain, allowing both the localization of specific functions within the brain and the investigation of multivariate patterns of functional activation. These patterns have been found to correspond both to short-term brain states such as focused attention or daydreaming, and to characteristics such as age or disease. Functional patterns also show substantial variation across individuals. Understanding the correspondence of distributed functional activity to these various factors is an ongoing research area.

Network science is a valuable tool for representing complex brain function, providing a framework for quantifying multivariate activity as a network of interactions. Here, we build upon recent advances in dynamic network science, using time-evolving networks to investigate how the organization of brain dynamics is related to demographics and brain states.

We use \textit{hypergraphs} to analyze brain network dynamics during different cognitive tasks and the transitions between them. We identify the presence of \textit{hyperedges}, groups of functional interactions that fluctuate coherently in strength over time both within and across brain states. We develop metrics to quantify the variation of hyperedge structure between tasks and across individuals. We find that the spatial location of hyperedges is relatively consistent across individuals, serving as a signature of a cognitive task, while hyperedge size exhibits variation across individuals but remains consistent between tasks.

We also investigate the variation of brain dynamics across the human lifespan, using both hypergraphs and dynamic clusters, or \textit{communities}, of brain regions with similar activity. We find significant relationships between age and dynamic organization: younger subjects tend to have larger hyperedges, as well as less fragmented and more coherent communities, and their brain regions tend to switch between communities less often. Further, the dynamics of different cognitive brain systems respond differently to aging.

Finally, we propose and evaluate a method of targeted node removal during the data-driven detection of communities, using synthetic and fMRI-derived networks to show that the method can improve identification of multi-scale community structure, and help to resolve key features of community dynamics.

    Cover page: Network-Based Investigations of Human Functional Brain Dynamics
    • Article
    • Peer Reviewed
    UC Santa Barbara Previously Published Works (2017)
    Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined "ground truth" communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters.
      Cover page: Improving resolution of dynamic communities in human brain networks through targeted node removal
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2018)
      Osteoporosis, characterized by increased fracture risk and bone fragility, impacts millions of adults worldwide, but effective, non-invasive and easily accessible diagnostic tests of the disease remain elusive. We present a magnetic resonance (MR) technique that overcomes the motion limitations of traditional MR imaging to acquire high-resolution frequency-domain data to characterize the texture of biological tissues. This technique does not involve obtaining full two-dimensional or three-dimensional images, but can probe scales down to the order of 40 μm and in particular uncover structural information in trabecular bone. Using micro-computed tomography data of vertebral trabecular bone, we computationally validate this MR technique by simulating MR measurements of a 'ratio metric' determined from a few k-space values corresponding to trabecular thickness and spacing. We train a support vector machine classifier on ratio metric values determined from healthy and simulated osteoporotic bone data, which we use to accurately classify osteoporotic bone.
        Cover page: Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone
        • Article
        • Peer Reviewed
        UC Santa Barbara Previously Published Works (2015)
        Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.
          Cover page: Brain Network Adaptability across Task States
          • Article
          • Peer Reviewed
          UC Santa Barbara Previously Published Works (2016)
          Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory"), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.
            Cover page: Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
            Top