165 citations · 531 across the 18 of their papers we have counts for
19 papers
State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis
Saiful Islam, Pitambar Khanra, Johan Nakuci +3
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating pr…
Reliability of energy landscape analysis of resting-state functional MRI data
Pitambar Khanra, Johan Nakuci, Sarah Muldoon +2
Energy landscape analysis is a data-driven method to analyze multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a us…
Skeleton coupling: a novel interlayer mapping of community evolution in temporal networks
Bengier Ülgen Kilic, Sarah Feldt Muldoon
Dynamic community detection (DCD) in temporal networks is a complicated task that involves the selection of a method and its associated hyperparameters. How to choose the most appr…
Computing brain networks with complex dynamics
Anca Radulescu, Johan Nakuci, Simone Evans +1
One important question in neuroscience is how global behavior in a brain network emerges from the interplay between network connectivity and the neural dynamics of individual nodes…
Synchronization and clustering in complex quadratic networks
Anca Radulescu, Danae Evans, Amani-Daisa Augustin +3
In continuation of prior work, we investigate ties between a network's connectivity and ensemble dynamics. This relationship is notoriously difficult to approach mathematically in…
Scale-specific dynamics of large-amplitude bursts in EEG capture behaviorally meaningful variability
Kanika Bansal, Javier O. Garcia, Nina Lauharatanahirun +3
Cascading large-amplitude bursts in neural activity, termed avalanches, are thought to provide insight into the complex spatially distributed interactions in neural systems. In hum…
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case
Cong Ye, Konstantinos Slavakis, Pratik V. Patil +3
This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: State cluste…
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian
Cong Ye, Konstantinos Slavakis, Pratik V. Patil +2
Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-n…
Cognitive chimera states in human brain networks
Kanika Bansal, Javier O. Garcia, Steven H. Tompson +3
The human brain is a complex dynamical system that gives rise to cognition through spatiotemporal patterns of coherent and incoherent activity between brain regions. As different r…
Optimizing state change detection in functional temporal networks through dynamic community detection
Michael Vaiana, Sarah F. Muldoon
Dynamic community detection provides a coherent description of network clusters over time, allowing one to track the growth and death of communities as the network evolves. However…
Resolution Limits for Detecting Community Changes in Multilayer Networks
Michael Vaiana, Sarah Muldoon
Multilayer networks capture pairwise relationships between the components of complex systems across multiple modes or scales of interactions. An important meso-scale feature of the…
Data-driven brain network models predict individual variability in behavior
Kanika Bansal, John D. Medaglia, Danielle S. Bassett +2
The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavi…
Personalized brain network models for assessing structure-function relationships
Kanika Bansal, Johan Nakuci, Sarah Feldt Muldoon
Many recent efforts in computational modeling of macro-scale brain dynamics have begun to take a data-driven approach by incorporating structural and/or functional information deri…
Multilayer Brain Networks
Michael Vaiana, Sarah Muldoon
The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the struct…
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case
Konstantinos Slavakis, Shiva Salsabilian, David S. Wack +5
This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis. Time-series data, collected sequentially over time and a…
Structure, Function, and Control of the Musculoskeletal Network
Andrew C. Murphy, Sarah F. Muldoon, David Baker +4
The human body is a complex organism whose gross mechanical properties are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musc…
The Energy Landscape of Neurophysiological Activity Implicit in Brain Network Structure
Shi Gu, Matthew Cieslak, Benjamin Baird +4
A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of human thought. How does large-scale brain circuitry const…
Stimulation-based control of dynamic brain networks
Sarah Feldt Muldoon, Fabio Pasqualetti, Shi Gu +4
The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing…
Small-World Propensity in Weighted, Real-World Networks
Sarah Feldt Muldoon, Eric W. Bridgeford, Danielle S. Bassett
Quantitative descriptions of network structure in big data can provide fundamental insights into the function of interconnected complex systems. Small-world structure, commonly dia…