Deciphering fMRI Time Series with Interpretable AI: Towards Reliable Biomarker Discovery

Music City Center 

Deep learning models have demonstrated strong predictive performance in neuroimaging tasks, but their "black box" nature often limits their utility in scientific interpretation and statistical inference. In this talk, I will present our recent developments in interpretable AI models tailored for functional MRI (fMRI) time series data. Our approach combines high predictive accuracy with mechanisms for identifying clinically relevant brain regions at multiple levels of analysis. We demonstrate that the identified region importance is robust across datasets and experimental conditions, offering a promising path toward more transparent and reliable use of AI in neuroimaging research.

Network data

Symmetric positive definite matrix

Brain functional connectivity