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

Statistical tests and identifiability conditions for pooling and analyzing multisite datasets. Zhou HH, Singh V, Johnson SC, Wahba G, and the Alzheimer’s Disease Neuroimaging Initiative. Proceedings of the National Academy of Sciences USA, 2018

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A geometric framework for statistical analysis of trajectories with distinct temporal spans. Chakraborty R, Singh V, Adluru N, Vemuri B. Proceedings of the International Conference on Computer Vision (ICCV), 2017

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Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM. Gutierrez-Barragan F, Ithapu VK, Hinrichs C, Maumet C, Johnson SC, Nichols TE, Singh V. Neuroimage 159(1):79-98, 2017

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Modeling cognitive trends in preclinical Alzheimer's disease (AD) via distributions over permutations. Plumb G, Clark LR, Johnson SC, Singh V. Proceedings of Medical Image Computing And Computer Assisted Intervention (MICCAI), 2017

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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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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