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.
Dajiang Zhu, Qingyang Li, Brandalyn Riedel, Neda Jahanshad, Derrek Hibar, Ilya Veer, Henrik Walter, Lianne Schmaal, Dick Veltman, Dominik Grotegerd, Udo Dannlowski, Matthew Sacchet, Ian Gotlib, Jieping Ye, Paul Thompson
KEYWORDS: Data centers, Brain, Magnetic resonance imaging, Feature selection, Data analysis, Control systems, Psychiatry, Diagnostics, Neuroimaging, Data modeling
Compared to many neurological disorders, for which imaging biomarkers are often available, there are no accepted imaging biomarkers to assist in the diagnosis of major depressive disorder (MDD). One major barrier to understanding MDD has been the lack of a practical and efficient platform for collaborative efforts across multiple data centers; integrating the knowledge from different centers should make it easier to identify characteristic measures that are consistently associated with the illness. Here we applied our newly developed “distributed Lasso” method to brain MRI data from multiple centers to perform feature selection and classification. Over 1,000 participants were involved in the study; our results indicate the potential of the proposed framework to enable large-scale collaborative data analysis in the future.
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