Lab interests…

We study biological, physical, and social systems by using and developing tools from network science and complex systems theory. Our broad goal is to isolate problems at the intersection of basic science, engineering, and clinical medicine that can be tackled using systems-level approaches.

Knowledge Networks: Architecture, Learnability, & Curiosity


Network Architecture of Knowledge:

Graph Learnability:

Controllability of Brain Networks 

The network architecture of the structural connectome forms a critical backbone on which cognitive processes evolve. In this project, we seek to build a theoretical framework in which to understand the impact of white matter microstructure and its large-scale interconnections patterns on the cognitive roles of brain areas, individual differences in cognitive function, and the effects of external stimulation on brain dynamics. In these efforts, we draw on both classical and cutting-edge tools in network control theory.

Recent Reviews


Network Control Theory:

Network Control and Cognitive Neuroscience:

Network Control and Clinical Neuroscience:

Network Control and Stimulation

Network Control Applied to Cellular-Level Neural Circuits

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Dynamic Networks in Neuroscience

Neurophysiological processes -- from learning and memory to disease and response to injury -- are inherently dynamic processes. A major thrust of our group lies in developing mathematical tools for understanding how brain networks reconfigure over multiple time scales. We apply these tools to understand executive function, human learning, lexical processing, motor behavior, and psychiatric disease. 

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Dynamic Network Methods

Dynamic Networks and Mood:

Dynamic Networks in Executive Function:

Dynamic Networks in Learning:

Dynamic Networks in Multi-Task Scenarios:

Dynamic Networks in Lexical Processing: 

Dynamic Networks in Motor Behavior:

Dynamic Networks in Psychiatric Disease:

Dynamic Networks in Neurological Disorders:

Network Physics of Soft (and Biological) Materials

Granular materials have been modeled from continuum and particulate perspectives but neither approach explains their complex behaviors. A network representation allows one to consider both microscopic and long-range features in the form of force chains.  We study granular materials as spatially-embedded networks in which the nodes (particles) are connected by weighted edges (contact forces). Using photoelastic particles, we examine the importance of meso-scale network features to the understanding of sound propagation in this medium. Our work demonstrates how the pressure state of a granular system is imprinted on transmitted waves. This fundamental physics can be used for material characterization and non-destructive testing. 

Granular Materials

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Biological Materials

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Organization of Large-Scale Brain Circuitry

The human brain is a complex system for which a combination of mathematical modeling and time series analysis enables the prediction of system behavior, facilitating a direct feedback loop between theory and experiment. We examine structural and functional brain networks using data from non-invasive neuroimaging techniques  (fMRI, MEG, MRI, DTI, DSI). Our goal is to determine fundamental organizational principles of both underlying structure and functional dynamics. Our results collectively point to principles of structural network efficiency, spatial and temporal scaling of network organization, and network adaptability in response to increasing cognitive demands or in the context of learning. Importantly, complementary evidence from psychiatric disease, specifically schizophrenia, highlights the disruption of normal connectivity patterns, the prevalence of wiring inefficiency, and the relationship between efficient network organization and cognitive fitness. 

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Recent Papers

Large-Scale Brain Circuitry in Psychiatric Disease

Schizophrenia is a devastating psychiatric disorder characterized by alterations in a distributed set of brain areas. We use network methods to uncover changes in large-scale brain circuitry that impact on cognitive function and behavior with the goal of identifying underlying neurophysiological processes of the disease and informing clinical interventions including cognitive remediation. 

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Recent Papers

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Probing Multiscale Drivers of Social Decision-Making


Collective dynamics can create complex patterns of population-based behaviors in a range of real events from economic unrest to political upheaval to evacuation in a natural disaster. These dynamics are critically shaped by social information diffusion among individuals who need to make a binary decision such as whether to stay or go in a disaster event. In these situations, the behavior of large populations is usually also shaped by a systemic information source available to the majority of individuals, such as the news media, advertisements, or scientific research. In this work, we build a sequence of models that systematically characterize information diffusion on networks of individuals who glean information from multiple sources. 

Recent Papers