Sunitha Basodi

NeuroFLAME provides a secure, scalable federated platform for multi-site neuroimaging analysis, achieving consistency with centralized methods while preserving data privacy and enabling institutional compliance.

This paper introduces a decentralized linear mixed-effects model for neuroimaging analysis, enabling large-scale collaboration without data pooling, and shows it achieves similar performance to centralized models.

This paper introduces COINSTAC Vaults to enhance collaborative neuroimaging research through federated analysis and reproducibility by hosting standardized datasets.

This study uses federated analysis in COINSTAC to examine functional network connectivity in adolescent brains linked to smoking and alcohol consumption, finding hypoconnectivity and reduced synchronization.

This work proposes a decentralized method for brain age estimation using MRI data and shows it achieves similar performance to centralized models.

The paper normalizes heterogeneous genomic data into images and applies machine learning to classify viral infection stages and detect transmission clusters with high accuracy.