Author

Ben A. Duffy

University of Southern CaliforniaORCID 0000-0002-7552-9254Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Neuroscience· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 3 Assay:imaging· 3 Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:genome assembly· 1 Research Focus:GWAS / association mapping· 1 Approach:statistical model· 1

Frequent Co-Authors

Arthur W. TogaiD· 2 shared Hosung KimiD· 2 shared Mengting LiuiD· 2 shared A. James BarkovichiD· 1 shared Cameron Akhavan· 1 shared Duan XuiD· 1 shared Fengzhu SuniD· 1 shared James Howard ColeiD· 1 shared

Activity by Year

2021-2021 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersNeuroscience

Arthur W. TogaORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Whole-brain functional connectivity neuromarkers uncover the cognitive recovery scheme for overt hepatic encephalopathy after liver transplantation

Yue Cheng, Wen Shen, Junhai Xu, Rachel C. Amey, Lixiang Huang, Xiaodong Zhang, Jingli Li, Cameron Akhavan, Ben A. Duffy, Julia Pia Simon, Wenjuan Jiang, Mengting Liu, Hosung Kim

This study identifies functional connectivity neuromarkers that reveal distinct cognitive recovery patterns in patients with overt hepatic encephalopathy after liver transplantation.

eNeuro · Jul 1, 2021Peer-reviewedNeuroscience

Brain age predicted using graph convolutional neural network explains developmental trajectory in preterm neonates

Mengting Liu, Sharon Y. Kim, Ben A. Duffy, Shiyu Yuan, James Howard Cole, Arthur W. Toga, Neda Jahanshad, A. James Barkovich, Duan Xu, Hosung Kim

This paper uses a graph convolutional neural network to predict brain age in preterm neonates, showing it outperforms other methods and links brain age to future neurodevelopmental outcomes.

bioRxiv · May 15, 2021 · PreprintNeuroscience

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging

Kaida Ning, Ben A. Duffy, Meredith Franklin, William J. Matloff, Lu Zhao, Nibal Arzouni, Fengzhu Sun, Arthur W. Toga

This study improves brain age prediction using deep learning and identifies four genetic loci associated with brain aging, three of which are novel.

Neurobiology of Aging · Apr 20, 2021Peer-reviewedNeuroscience