  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.
