TY - GEN
T1 - Patch-based surface morphometry feature selection with federated group lasso regression
AU - Wu, Jianfeng
AU - Zhang, Jie
AU - Li, Qingyang
AU - Su, Yi
AU - Chen, Kewei
AU - Reiman, Eric M.
AU - Wang, Jie
AU - Lepore, Natasha
AU - Ye, Jieping
AU - Thompson, Paul M.
AU - Wang, Yalin
N1 - Funding Information:
The work was supported in part by NIH grants (R21AG065942, RF1AG051710, R01EB025032, U54EB020403, R01AG031581 and P30AG19610) and by the Arizona Alzheimer Consortium.
Publisher Copyright:
© 2020 SPIE
PY - 2020
Y1 - 2020
N2 - Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer’s disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.
AB - Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer’s disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.
KW - Alzheimer’s Disease
KW - Amyloid Burden
KW - Feature Selection
KW - Federated Learning
KW - Group Lasso
KW - Surface-Based Morphometry
UR - http://www.scopus.com/inward/record.url?scp=85096851682&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096851682&partnerID=8YFLogxK
U2 - 10.1117/12.2575984
DO - 10.1117/12.2575984
M3 - Conference contribution
AN - SCOPUS:85096851682
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - 16th International Symposium on Medical Information Processing and Analysis
A2 - Romero, Eduardo
A2 - Lepore, Natasha
A2 - Brieva, Jorge
A2 - Linguraru, Marius
PB - SPIE
T2 - 16th International Symposium on Medical Information Processing and Analysis 2020
Y2 - 3 October 2020 through 4 October 2020
ER -