(Georgia Institute of Technology, 2023-12-13)
Saha, Debbrata Kumar
In the neuroimaging domain, the data collection process is expensive, and attempting to pool data from multiple imaging sites faces numerous challenges, including variations in data acquisition protocols from site to site. There is also concern associated with revealing the identities of rare disease subjects. The challenges of data sharing associated with these issues prevent the datasets from being as large as desired, ultimately hindering the benefits of utilizing large-scale datasets in research operations. This dissertation aims to address these potential challenges. First, we develop a federated embedding algorithm to assess the quality control of neuroimaging datasets. Our algorithm has demonstrated superior performance in overcoming challenges that some notable existing algorithms struggle to solve. Subsequently, we introduce a privacy-preserving algorithm tailored to the neuroimaging domain, ensuring formal mathematical privacy guarantees during message passing in federated computation. The integration of this algorithm with the existing software platform for federated neuroimaging has been demonstrated, making our methods readily available as tools for neuroimaging users worldwide.
Our third proposed approach emphasizes fast federated communication with more stringent privacy assurances. Lastly, we design a federated algorithm to extract multivariate patterns (covarying networks) from structural magnetic resonance imaging (sMRI) data for the analysis of brain morphometry. These four proposed methods enable neuroimaging users to perform operations in a federated environment where it is not possible to run operations centrally in typical scenarios.