A5073266637- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Advanced Neuroimaging Techniques and Applications
- EEG and Brain-Computer Interfaces
- Complex Network Analysis Techniques
- Advanced MRI Techniques and Applications
- Mental Health Research Topics
- Neural Networks and Applications
- Child and Animal Learning Development
- Memory and Neural Mechanisms
- Advanced Memory and Neural Computing
- Musculoskeletal pain and rehabilitation
- Cognitive Science and Mapping
- Advanced Text Analysis Techniques
- Pain Management and Opioid Use
- Glioma Diagnosis and Treatment
- Neuroscience and Neuropharmacology Research
- Design Education and Practice
- Pain Management and Placebo Effect
- Data Visualization and Analytics
- Opinion Dynamics and Social Influence
- Cognitive Abilities and Testing
- Cognitive Science and Education Research
- Misinformation and Its Impacts
- Innovative Teaching and Learning Methods
Princeton University
2021-2025
Texas Advanced Computing Center
2023-2024
The University of Texas at Austin
2022-2024
California University of Pennsylvania
2018-2023
University of Pennsylvania
2015-2023
University of Iowa
2021-2022
Philadelphia University
2022
DEVCOM Army Research Laboratory
2016-2019
Texas State University
2000
Abstract Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these network processes have remained elusive. Here we use tools from control and theories to offer a mechanistic explanation for how the brain moves cognitive states drawn organization of white matter microstructure. Our results suggest that densely connected areas, particularly in default mode system, facilitate...
Encoding brain regions and their connections as a network of nodes edges captures many the possible paths along which information can be transmitted humans process perform complex behaviors. Because cognitive processes involve large, distributed networks areas, principled examinations multi-node routes within larger connection patterns offer fundamental insights into complexities function. Here, we investigate both densely connected groups that could local computations well interactions...
As the human brain develops, it increasingly supports coordinated control of neural activity. The mechanism by which white matter evolves to support this coordination is not well understood. We use a network representation diffusion imaging data from 882 youth ages 8 22 show that connectivity becomes optimized for diverse range predicted dynamics in development. Notably, stable controllers subcortical areas are negatively related cognitive performance. Investigating structural mechanisms...
Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding its physical propagation through brain tissue. Here, we use network control theory predict how spreads white matter influence spatially distributed dynamics. We test theory's predictions using a unique dataset comprising diffusion weighted imaging and electrocorticography in epilepsy patients undergoing grid stimulation. find statistically significant shared...
Objective. Predicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding relationship between neural connectivity and activity. Network theory is powerful tool from physical engineering sciences that provide insights regarding relationship; it formalizes study dynamics complex system arise its underlying structure interconnected units. Approach. Given recent use network in neuroscience, now timely offer practical guide...
How do people acquire knowledge about which individuals belong to different cliques or communities? And what extent does this learning process differ from the of higher-order information complex associations between nonsocial bits information? Here, authors use a paradigm in order stimulus presentation forms temporal stimuli, collectively constituting network. They examined individual differences ability learn community structure networks composed social versus stimuli. Although participants...
Abstract Humans are adept at uncovering abstract associations in the world around them, yet underlying mechanisms remain poorly understood. Intuitively, learning higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that instead arise from natural errors and memory. Using free energy principle, which bridges information theory Bayesian inference, derive a maximum entropy model people’s internal representations...
Animals frequently make decisions based on expectations of future reward ("values"). Values are updated by ongoing experience: places and choices that result in assigned greater value. Yet, the specific algorithms used brain for such credit assignment remain unclear. We monitored accumbens dopamine as rats foraged rewards a complex, changing environment. observed brief pulses both at receipt (scaling with prediction error) novel path opportunities. Dopamine also ramped up ran toward ports,...
Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory examine dynamic reconfiguration functionally neuronal ensembles during targeted cortical subcortical structures. By integrating multimodal intracranial...
Abstract How do people model the world’s dynamics to guide mental simulation and evaluate choices? One prominent approach, Successor Representation (SR), takes advantage of temporal abstraction future states: by aggregating trajectory predictions over multiple timesteps, brain can avoid costs iterative, multi-step simulation. Human behavior broadly shows signatures such abstraction, but finer-grained characterization individuals’ strategies their dynamic adjustment remains an open question....
Abstract Network science has emerged as a powerful tool through which we can study the higher-order architectural properties of world around us. How human learners exploit this information remains an essential question. Here, focus on temporal constraints that govern such process. Participants viewed continuous sequence images generated by three distinct walks modular network. Walks varied along two critical dimensions: their predictability and density with they sampled from communities...
Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how acquire knowledge about these structures successfully navigate relationships. Here we address this gap with an interdisciplinary neuroimaging study drawing on recent advances network science and statistical learning. Specifically, collected BOLD MRI data while participants learned community structure of both non-social networks, order examine whether learning two...
Despite mounting evidence that human learners are sensitive to community structure underpinning temporal sequences, this phenomenon has been studied using an extremely narrow set of network ensembles. The extent which behavioral signatures learning robust changes in size and number is the focus present work. Here we adult participants with a continuous stream novel objects generated by random walk along graphs 1, 2, 3, 4, or 6 communities comprised N = 24, 12, 8, 6, 4 nodes, respectively....
Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals the neural processes that enable humans achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) magnetoencephalographic (MEG) recordings a motor imagery-based BCI training involving group of healthy...
Human skill learning requires fine-scale coordination of distributed networks brain regions linked by white matter tracts to allow for effective information transmission. Yet how individual differences in these anatomical pathways may impact remains far from understood. Here, we test the hypothesis that structural organization supporting task performance predict rate at which humans learn a visuomotor skill. Over course 6 weeks, 20 healthy adult subjects practiced discrete sequence...
By building a mental model of how the world works and using it to forecast outcomes different actions, learner can make flexible choices in changing environments. However, while children adolescents readily acquire structured knowledge about their environments, relative adults, they tend demonstrate weaker signatures leveraging this plan actions. One explanation for these developmental differences is that prospectively simulate potential computationally costly, taxing cognitive control...
From sequences of discrete events, humans build mental models their world. Referred to as graph learning, the process produces a model encoding event-to-event transition probabilities. Recent evidence suggests that some networks are easier learn than others, but neural underpinnings this effect remain unknown. Here we use fMRI show even over short timescales network structure temporal sequence stimuli determines fidelity event representations well dimensionality space in which those encoded:...
By building a mental model of how the world works and using it to forecast outcomes different actions, learner can make flexible choices in changing environments. However, while children adolescents readily acquire structured knowledge about their environments, relative adults, they tend demonstrate weaker signatures leveraging this plan actions. One explanation for these developmental differences is that prospectively simulate potential computationally costly, taxing cognitive control...
By building a mental model of how the world works and using it to forecast outcomes different actions, learner can make flexible choices in changing environments. However, while children adolescents readily acquire structured knowledge about their environments, relative adults, they tend demonstrate weaker signatures leveraging this plan actions. One explanation for these developmental differences is that prospectively simulate potential computationally costly, taxing cognitive control...