Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model

TL;DR

This paper introduces a multi-site weighted LASSO framework that enhances the classification accuracy of major depressive disorder using distributed brain imaging data, addressing privacy and accessibility challenges.

Contribution

It presents a novel iterative weighted LASSO method for multi-site data integration, improving disease classification accuracy without sharing raw data.

Findings

Boosted MDD classification accuracy by 4.9% on average

Effective for large-scale distributed imaging data

Potential for privacy-preserving collaborative analysis

Abstract

Large-scale collaborative analysis of brain imaging data, in psychiatry and neu-rology, offers a new source of statistical power to discover features that boost ac-curacy in disease classification, differential diagnosis, and outcome prediction. However, due to data privacy regulations or limited accessibility to large datasets across the world, it is challenging to efficiently integrate distributed information. Here we propose a novel classification framework through multi-site weighted LASSO: each site performs an iterative weighted LASSO for feature selection separately. Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature. This new weight is used to guide the LASSO process at the next iteration. Only the fea-tures that help to improve the classification accuracy are preserved. In tests on da-ta from five sites (299 patients with major depressive disorder (MDD) and 258 normal controls), our method boosted classification accuracy for MDD by 4.9% on average. This result shows the potential of the proposed new strategy as an ef-fective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data.