Time：2020/9/30 14:00

Form：Tencent Meeting

Topic：Distributional Independent Component Analysis for Diverse Neuroimaging Modalities

Abstract：

Recent advances in neuroimaging technologies have provided opportunities to acquire brain images of different modalities for studying human brain organization from both functional and structural perspectives. Analysis of images derived from various modalities involves some common goals such as dimension reduction, denoising, and feature extraction. However, since these modalities have vastly different data characteristics, the current analysis is usually performed using distinct analytical tools that are only suitable for a specific imaging modality. In this paper, we present a Distributional Independent Component Analysis (DICA) that represents a new approach that performs decomposition on the distribution level, providing a unified framework for extracting features across imaging modalities with different scales and representations. When applying DICA to fMRI images, we successfully recover well-established brain functional networks in neuroscience literature, providing empirical validation that DICA delivers neurologically relevant findings. More importantly, we discover several structural network components when applying DICA to DTI images. Through fiber tracking, we find these DICA-derived structural components correspond to several major white fiber bundles. To the best of our knowledge, this is the first time these fiber bundles are successfully identified via blind source separation on single subject DTI images. We also evaluate the performance of DICA as compared with classical ICA methods through extensive simulation studies.

Resume：

Dr. Ben Wu is an Assistant Professor in the Department of Mathematical Statistics in the School of Statistics at Renmin University of China. He spent two years at Emory University and University of Michigan as a postdoctoral fellow, after he received his Ph.D. at Renmin University of China. His primary research interests focus on Bayesian statistics, independent component analysis and network analysis, with application in neuroimaging data and financial high-frequency data.