Eric W. Bridgeford
Johns Hopkins University
45 Papers
95 Citations
Eric W. Bridgeford is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Computer science & Connectome. The author has an hindex of 10, co-authored 36 publications. Previous affiliations of Eric W. Bridgeford include University of Pennsylvania.
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Papers
Small-World Propensity and Weighted Brain Networks.
TL;DR: This work develops a standardized procedure for generating weighted small-world networks, a weighted extension of the SWP, and a method for mapping observed brain network data onto the theoretical model, and uncovers the surprising fact that the canonical biological small- world network, the C. elegans neuronal network, has strikingly low SWP.
A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability
Gregory Kiar,Eric W. Bridgeford,Gray Roncal Wr,Chandrashekhar,Disa Mhembere,Sephira G. Ryman,Xi-Nian Zuo,Margulies Ds,Margulies Ds,Craddock Rc,Priebe Ce,Rex E. Jung,Vince D. Calhoun,Brian Caffo,Randal Burns,Milham Mp,Joshua T. Vogelstein +16 more
TL;DR: NeuroData’s MRI to Graphs (NDMG) pipeline is developed using several functional and diffusion studies, including the Consortium for Reliability and Reproducability, to estimate connectomes and provides a set of principles to guide the development of pipelines capable of pooling data across studies while maintaining biological variability and minimizing measurement error.
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Standardizing human brain parcellations.
Ross Lawrence,Eric W. Bridgeford,Patrick E. Myers,Ganesh C. Arvapalli,Sandhya Ramachandran,Derek Pisner,Paige F. Frank,Allison D. Lemmer,Aki Nikolaidis,Joshua T. Vogelstein +9 more
TL;DR: This group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlase standardization.
Supervised dimensionality reduction for big data.
Joshua T. Vogelstein,Eric W. Bridgeford,Minh Tang,Da Zheng,Christopher Douville,Randal Burns,Mauro Maggioni +6 more
TL;DR: Linear Optimal Low-Rank Projection (LOP) as discussed by the authors extends principal component analysis (PCA) by incorporating class-conditional moment estimates into the low-dimensional projection.
Discovering and deciphering relationships across disparate data modalities
Joshua T. Vogelstein,Eric W. Bridgeford,Qing Wang,Carey E. Priebe,Mauro Maggioni,Cencheng Shen +5 more
TL;DR: The Multiscale Graph Correlation (MGC) algorithm as discussed by the authors combines hypothesis testing, machine learning, and data science to find possible relationships between two sets of properties. And it is shown that it is more effective at finding possible relationships than other commonly used independence methods.
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