arXiv subjects

Danielle S. Bassett

Publications and source records attributed to Danielle S. Bassett.

At least 109 records · Page 6Linked to original sources

arXiv2017

Individual Differences in Learning Social and Non-Social Network Structures

Learning about complex associations between pieces of information enables individuals to quickly adjust their expectations and develop mental models. Yet, the degree to which humans can learn higher-order information about complex associations is not well understood; nor is it known whether the learning process differs for social and non-social information. Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network structure. We examined individual differences in the ability to learn network topology for which stimuli were social versus non-social. Although participants were able to learn both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. Importantly, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social networks but not the learning of non-social networks. Taken together, our results suggest that the process of learning higher-order structure in social networks is independent from the process of learning higher-order structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes. Implications for how people learn and adapt to new social contexts that require integration into a new social network are discussed.

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arXiv2017

Knowledge gaps in the early growth of semantic networks

Understanding the features of and mechanisms behind language learning can provide insights into the general process of knowledge acquisition. Recent methods from network science applied to language learning have advanced the field, particularly by noting associations between densely connected words and acquisition. However, the importance of sparse areas of the network, or knowledge gaps, remains unexplored. Here we create a semantic feature network in which words correspond to nodes and in which connections correspond to semantic similarity. We develop a new analytical approach built on principles of applied topology to query the prevalence of knowledge gaps, which we propose manifest as cavities within the network. We detect topological cavities of multiple dimensions in the growing semantic feature network of children ages 16 to 30 months. The pattern of cavity appearance matches that of a constrained null model, created by predefining the affinity of each node for connections. Furthermore, when word acquisition time is computed from children of mothers with differing levels of education, we find that despite variation at the word level, the global organization as measured by persistent homology remains comparable. We show that topological properties of a node correlate with filling in cavities better than simple lexical properties such as the length and frequency of the corresponding word. Finally, we show that the large-scale architecture of the semantic feature network is topologically accommodating to many node orders. We discuss the importance of topology in language learning, and we speculate that the formation and filling of knowledge gaps may be a robust feature of knowledge acquisition.

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arXiv2017

Generative Models for Network Neuroscience: Prospects and Promise

Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and to identify principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modeling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, utility in intuiting mechanisms, and a short history on their use in network science broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including \emph{C. elegans}, \emph{Drosophila}, mouse, rat, cat, macaque, and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modeling approach for network neuroscience.

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arXiv2017

Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators

Synchronization of non-identical oscillators coupled through complex networks is an important example of collective behavior. It is interesting to ask how the structural organization of network interactions influences this process. Several studies have uncovered optimal topologies for synchronization by making purposeful alterations to a network. Yet, the connectivity patterns of many natural systems are often not static, but are rather modulated over time according to their dynamics. This co-evolution - and the extent to which the dynamics of the individual units can shape the organization of the network itself - is not well understood. Here, we study initially randomly connected but locally adaptive networks of Kuramoto oscillators. The system employs a co-evolutionary rewiring strategy that depends only on instantaneous, pairwise phase differences of neighboring oscillators, and that conserves the total number of edges, allowing the effects of local reorganization to be isolated. We find that a simple regulatory rule - which preserves connections between more out-of-phase oscillators while rewiring connections between more in-phase oscillators - can cause initially disordered networks to organize into more structured topologies that support enhanced synchronization dynamics. We examine how this process unfolds over time, finding both a dependence on the intrinsic frequencies of the oscillators and the global coupling. For large enough coupling and after sufficient adaptation, the resulting networks exhibit degree - frequency and frequency - neighbor frequency correlations. These properties have previously been associated with optimal synchronization or explosive transitions. By considering a time-dependent interplay between structure and dynamics, this work offers a mechanism through which emergent phenomena can arise in complex systems utilizing local rules.

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arXiv2017

Parsing spatiotemporal dynamical stability in ECoG during seizure onset, propagation, and termination

Understanding brain dynamics in epilepsy is critical for establishing rigorous control objectives that enable new therapeutic methods to mitigate seizure occurrence. In multichannel electrocorticography (ECoG) recordings acquired in 21 subjects during a total of 94 seizures, we apply dynamical systems stability analysis to assess the balance versus imbalance of seizure dynamics across different timescales and brain regions. Specifically, we consider a sliding time window multivariate autoregressive linear approximation of the data captured by the ECoG channels, where eigendecomposition of the estimated matrix of coefficients describes the contribution of different regions to the spatiotemporal process (eigenvectors) associated with a particular timescale (eigenvalues). Interestingly, we observe a pattern of eigenvalue evolution and slowly changing (or approximately time-invariant) eigenvectors across both seizures and subjects. The seizure-onset is marked by an increase in high frequency spatial information to which a few regions contribute for a long period. By contrast, the seizure termination is characterized by a sudden, small time period change in dynamics to which many regions contribute. As the seizure terminates, the relatively stable ictal dynamics rapidly transition into the post-ictal regime, marked by relatively fast-damping oscillations. Our methodology offers a subject-specific characterization of the spatiotemporal behavior of the seizure, providing new insights into the dynamic patterns and functional interactions between brain regions that occur over different timescales. More generally, our approach informs the development of engineering objectives that can be used to deploy new control strategies to prevent seizure evolution or to hasten seizure termination.

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arXiv2017

Inter-regional ECoG correlations predicted by communication dynamics, geometry, and correlated gene expression

Electrocorticography (ECoG) provides direct measurements of synchronized postsynaptic potentials at the exposed cortical surface. Patterns of signal covariance across ECoG sensors have been associated with diverse cognitive functions and remain a critical marker of seizure onset, progression, and termination. Yet, a systems level understanding of these patterns (or networks) has remained elusive, in part due to variable electrode placement and sparse cortical coverage. Here, we address these challenges by constructing inter-regional ECoG networks from multi-subject recordings, demonstrate similarities between these networks and those constructed from blood-oxygen-level-dependent signal in functional magnetic resonance imaging, and predict network topology from anatomical connectivity, interregional distance, and correlated gene expression patterns. Our models accurately predict out-of-sample ECoG networks and perform well even when fit to data from individual subjects, suggesting shared organizing principles across persons. In addition, we identify a set of genes whose brain-wide co-expression is highly correlated with ECoG network organization. Using gene ontology analysis, we show that these same genes are enriched for membrane and ion channel maintenance and function, suggesting a molecular underpinning of ECoG connectivity. Our findings provide fundamental understanding of the factors that influence interregional ECoG networks, and open the possibility for predictive modeling of surgical outcomes in disease.

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arXiv2017

Benchmarking measures of network controllability on canonical graph models

Many real-world systems are composed of many individual components that interact with one another in a complex pattern to produce diverse behaviors. Understanding how to intervene in these systems to guide behaviors is critically important to facilitate new discoveries and therapies in systems biology and neuroscience. A promising approach to optimizing interventions in complex systems is network control theory, an emerging conceptual framework and associated mathematics to understand how targeted input to nodes in a network system can predictably alter system dynamics. While network control theory is currently being applied to real-world data, the practical performance of these measures on simple networks with pre-specified structure is not well understood. In this study, we benchmark measures of network controllability on canonical graph models, providing an intuition for how control strategy, graph topology, and edge weight distribution mutually depend on one another. Our numerical studies motivate future analytical efforts to gain a mechanistic understanding of the relationship between graph topology and control, as well as efforts to design networks with specific control profiles.

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arXiv2017

Mind Control as a Guide for the Mind

The human brain is a complex network that supports mental function. The nascent field of network neuroscience applies tools from mathematics to neuroimaging data in the hopes of shedding light on cognitive function. A critical question arising from these empirical studies is how to modulate a human brain network to treat cognitive deficits or enhance mental abilities. While historically a number of tools have been employed to modulate mental states (such as cognitive behavioral therapy and brain stimulation), theoretical frameworks to guide these interventions - and to optimize them for clinical use - are fundamentally lacking. One promising and as-yet underexplored approach lies in a sub-discipline of engineering known as network control theory. Here, we posit that network control fundamentally relates to mind control, and that this relationship highlights important areas for future empirical research and opportunities to translate knowledge in practical domains. We clarify the conceptual intersection between neuroanatomy, cognition, and control engineering in the context of network neuroscience. Finally, we discuss the challenges, ethics, and promises of mind control.

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arXiv2017

Multi-scale detection of hierarchical community architecture in structural and functional brain networks

Community detection algorithms have been widely used to study the organization of complex systems like the brain. A principal appeal of these techniques is their ability to identify a partition of brain regions (or nodes) into communities, where nodes within a community are densely interconnected. In their simplest application, community detection algorithms are agnostic to the presence of community hierarchies, but a common characteristic of many neural systems is a nested hierarchy. To address this limitation, we exercise a multi-scale extension of a community detection technique known as modularity maximization, and we apply the tool to both synthetic graphs and graphs derived from human structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neighboring topological scales, thereby becoming sensitive to conserved community organization across neighboring levels of the hierarchy. We demonstrate that this method allows for a better characterization of topological inhomogeneities of the graph's hierarchy by providing a local (node) measure of community stability and inter-scale reliability across topological scales. We compare the brain's structural and functional network architectures and demonstrate that structural graphs display a wider range of topological scales than functional graphs. Finally, we build a multimodal multiplex graph that combines structural and functional connectivity in a single model, and we identify the topological scales where resting state functional connectivity and underlying structural connectivity show similar versus unique hierarchical community architecture. Together, our results showcase the advantages of the multi-scale community detection algorithm in studying hierarchical community structure in brain graphs, and they illustrate its utility in modeling multimodal neuroimaging data.

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arXiv2017

Dynamic Graph Metrics: Tutorial, Toolbox, and Tale

The central nervous system is composed of many individual units -- from cells to areas -- that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.

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arXiv2017

Diversity of meso-scale architecture in human and non-human connectomes

The brain's functional diversity is reflected in the meso-scale architecture of its connectome, i.e. its division into clusters and communities of topologically-related brain regions. The dominant view, and one that is reinforced by current analysis techniques, is that communities are strictly assortative and segregated from one another, purportedly for the purpose of carrying out specialized information processing. Such a view, however, precludes the possibility of non-assortative communities that could engender a richer functional repertoire by allowing for a more complex set of inter-community interactions. Here, we use weighted stochastic blockmodels to uncover the meso-scale architecture of \emph{Drosophila}, mouse, rat, macaque, and human connectomes. We confirm that while many communities are assortative, others form core-periphery and disassortative structures, which in the human better recapitulate observed patterns of functional connectivity and in the mouse better recapitulate observed patterns of gene co-expression than other community detection techniques. We define a set of network measures for quantifying the diversity of community types in which brain regions participate. Finally, we show that diversity is peaked in control and subcortical systems in humans, and that individual differences in diversity within those systems predicts cognitive performance on Stroop and Navon tasks. In summary, our report paints a more diverse portrait of connectome meso-scale structure and demonstrates its relevance for cognitive performance.

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arXiv2017

Topological Principles of Control in Dynamical Network Systems

Networked systems display complex patterns of interactions between a large number of components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding the relationship between network topology and system behavior, and harnessing that relationship to optimally control the system's function. Here, we use linear network control theory to analytically relate the topology of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. As opposed to the numerical computations of control energy, our accurate closed-form expressions yield general structural features in networks that require significantly more or less energy to control, providing topological principles for the design and modification of network behavior. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from drosophila, mouse, and human brains. We use these principles to show that connectomes of increasingly complex species are wired to reduce control energy. We then use the analytical expressions we derive to perform targeted manipulation of the brain's control profile by removing single edges in the network, a manipulation that is accessible to current clinical techniques in patients with neurological disorders. Cross-species comparisons suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs, while remaining unexpectedly robust to perturbations. Our results ground the expectation of a system's dynamical behavior in its network architecture.

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arXiv2017

Process reveals structure: How a network is traversed mediates expectations about its architecture

Network science has emerged as a powerful tool through which we can study the higher-order architectural properties of the world around us. How human learners exploit this information remains an essential question. Here, we focus on the temporal constraints that govern such a process. Participants viewed a continuous sequence of images generated by three distinct walks on a modular network. Walks varied along two critical dimensions: their predictability and the density with which they sampled from communities of images. Learners exposed to walks that richly sampled from each community exhibited a sharp increase in processing time upon entry into a new community. This effect was eliminated in a highly regular walk that sampled exhaustively from images in short, successive cycles (i.e., that increasingly minimized uncertainty about the nature of upcoming stimuli). These results demonstrate that temporal organization plays an essential role in how robustly knowledge of network architecture is acquired.

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arXiv2017

Brain State Flexibility Accompanies Motor-Skill Acquisition

Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging -- and to assess their dynamics during learning -- remain limited. Here, we describe an approach based on a novel application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning.

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arXiv2017

Brain and Cognitive Reserve: Translation via Network Control Theory

Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive 'reserve,' associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention.

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arXiv2017

Optimal Trajectories of Brain State Transitions

The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how the organization of white matter architecture constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question from a computational perspective by defining a brain state as a pattern of activity across brain regions. Drawing on recent advances in network control theory, we model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. Specifically, we examine how the brain moves from a specified initial state (characterized by high activity in the default mode) to a specified target state (characterized by high activity in primary sensorimotor cortex) in finite time. Across all state transitions, we observe that the supramarginal gyrus and the inferior parietal lobule consistently acted as efficient, low energy control hubs, consistent with their strong anatomical connections to key input areas of sensorimotor cortex. Importantly, both these and other regions in the fronto-parietal, cingulo-opercular, and attention systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional notions of control common in the engineering literature. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to damage-induced noise in neurophysiological activity. These results offer fundamentally new insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury.

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arXiv2017

Network Analyses and Nervous System Disorders

Network analyses in nervous system disorders involves constructing and analyzing anatomical and functional brain networks from neuroimaging data to describe and predict the clinical syndromes that result from neuropathology. A network view of neurological disease and clinical syndromes facilitates accurate quantitative characterizations and mathematical models of complex nervous system disorders with relatively simple tools drawn from the field of graph theory. Networks are predominantly constructed from in vivo data acquired using physiological and neuroimaging techniques at the macroscale of nervous system organization. Studies support the emerging view that neuropsychiatric and neurological disorders result from pathological processes that disrupt the brain's economically wired small-world organization. The lens of network science offers theoretical insight into progressive neurodegeneration, neuropsychological dysfunction, and potential anatomical targets for intervensions ranging form pharmacological agents to brain stimulation.

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arXiv2016

Functional Hypergraph Uncovers Novel Covariant Structures over Neurodevelopment

Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large-scale functional networks. Here, we apply a recently-developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting-state functional MRI data reveals three distinct classes of sub-networks (hyperedges): clusters, bridges, and stars, which represent spatially distributed, bipartite, and focal architectures, respectively. Cluster hyperedges show a strong resemblance to the functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core-periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core-periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results emphasize that the network organization of human brain emerges across multiple scales and evolves substantially through the adolescent period.

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