报告时间：2020年9月30 日（周三）14:00

报告形式：腾讯会议

报告嘉宾：吴奔

报告主题：Distributional Independent Component Analysis for Diverse Neuroimaging Modalities

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.

吴奔，中国人民大学统计学院讲师，Emory大学生物统计与生物信息系博士后，Michigan大学生物统计系博士后。主要研究方向为贝叶斯统计、独立成分分析、脑图像数据分析、金融高频数据分析等。