Neuroimage-based Phenotyping Project

This project is developing computational phenotyping methods that characterize association patterns among progressive preclinical brain changes in longitudinally acquired brain images in order to predict cognitive decline in individuals at risk for Alzheimer’s disease.

Related CPCP Publications

When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, L2-consistency and Neuroscience Applications. Zhou HH, Zhang Y, Ithapu VK, Johnson SC, Wahba G, Singh V. Proceedings of the International Conference on Machine Learning (ICML), 2017

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Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging. Kim HJ, Adluru N, Suri H, Vemuri BC, Johnson SC, Singh V. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

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Hypothesis testing in unsupervised domain adaptation with applications in neuroimaging. Zhou H, Ravi S, Ithapu V, Johnson S, Wahba G, Singh V. Advances in Neural Information Processing Systems (NIPS), 2016

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Adaptive signal recovery on graphs via harmonic analysis for experimental design in neuroimaging. Kim WH, Hwang SJ, Adluru N, Johnson SC, Singh V. Proceedings of the 14th European Conference on Computer Vision (ECCV), Volume 9910 Lecture Notes in Computer Science, 2016

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Abundant inverse regression using sufficient reduction and its applications. Kim H, Smith B, Adluru N, Dyer C, Johnson S, Singh V. Proceedings of the European Conference on Computer Vision (ECCV),Volume 9910 Lecture Notes in Computer Science, 2016

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Lead

Vikas Singh

Investigators

Barbara Bendlin

Sterling Johnson

Xiaojin "Jerry" Zhu

Nagesh Adluru

Ronak Mehta

Wei Zhang

Resources

CPCP 2017 Retreat: Connectivity Loss in Alzheimer's Disease Symposium Video

CPCP 2017 Retreat: Graph Completion - Adaptive Study Design for Cost Effective Neuroimaging Trials in Preclinical AD Symposium Video

CPCP Retreat 2016: Neuroimage-Based Phenotyping and the Problem of AD Symposium Video