This paper explores privacy-preserving visualization techniques for brain functional connectivity using differential privacy to protect participant data while maintaining visual utility.

NeuroImage · Sep 1, 2026Peer-reviewedNeuroscience

NeuroFLAME: A Scalable, Privacy-Preserving Federated Framework for Secure, Reproducible, and Multi-Site Neuroimaging Analysis

Dylan Martin, Sandeep R. Panta, Ross Kelly, Javier Romero, Sunitha Basodi, Bradley Baker, Ravi Teja Girijala, Jessica Turner, Kun Yang, Akira Sawa, Frank Hillary, Anand D. Sarwate, Vince D. Calhoun

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.

bioRxiv · Aug 27, 2026 · PreprintNeuroscience

Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from fMRI data

Xing Meng, Armin Iraji, Zening Fu, Peter V Kochunov, Ayşenil Belger, Judy M. Ford, Sara McEwen, Daniel H. Mathalon, Bryon A. Mueller, Godfrey D. Pearlson, Steven G. Potkin, Adrian Preda, Jessica Turner, Theo G.M. van Erp, Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, not Qingdao University of Science and Technology, please correct the current affiliation. Thank you, Vince D. Calhoun

This paper introduces a multi-scale spatially constrained ICA approach to analyze fMRI data, revealing consistent group differences and predictive patterns in schizophrenia.

bioRxiv · Nov 2, 2022 · PreprintNeuroscience