Join us Friday, October 27th, 2023, at 1:00 PM ET for an exciting virtual talk by Dr. Ying Guo entitled: “A Regularized Blind Source Separation Framework for Unveiling Hidden Sources of Brain Functional Connectome” as part of the activities of the Brain Space Initiative, co-sponsored by the Center for Translational Research in Neuroimaging and Data Science (TReNDS) and the Data Science Initiative, IEEE Signal Processing Society.

A Regularized Blind Source Separation Framework for Unveiling Hidden Sources of Brain Functional Connectome

Brain connectomics has become increasingly important in neuroimaging studies to advance understanding of neural circuits and their association with neurodevelopment, mental illnesses, and aging. These analyses often face major challenges, including the high dimensionality of brain networks, unknown latent sources underlying the observed connectivity, and the large number of brain connections leading to spurious findings. In this talk, we will introduce a novel regularized blind source separation (BSS) framework for reliable mapping of neural circuits underlying static and dynamic brain functional connectome. The proposed LOCUS methods achieve more efficient and reliable source separation for connectivity matrices using low-rank factorization, a novel angle-based sparsity regularization, and a temporal smoothness regularization. We develop a highly efficient iterative Node-Rotation algorithm that solves the non-convex optimization problem for learning LOCUS models. Simulation studies demonstrate that the proposed methods have consistently improved accuracy in retrieving latent connectivity traits. Application of LOCUS methods to the Philadelphia Neurodevelopmental Cohort (PNC) neuroimaging study generates considerably more reproducible findings in revealing underlying neural circuits and their association with demographic and clinical phenotypes, uncovers dynamic expression profiles of the circuits and the synchronization between them, and generates insights on gender differences in the neurodevelopment of brain circuits.

Biosketch: Dr. Ying Guo is Professor in the Department of Biostatistics and Bioinformatics at Emory University, an appointed Graduate Faculty of the Emory Neuroscience Program and an Associate Faculty in Emory Department of Computer Science. She is a Founding Member and current Director of the Center for Biomedical Imaging Statistics (CBIS) at Emory University.  Dr. Guo’s research focus on developing analytical methods for neuroimaging and mental health studies. Her main research areas include statistical methods for agreement and reproducibility studies, brain network analysis, multimodal neuroimaging and imaging-based prediction methods. Dr. Guo is a Fellow of American Statistical Association (ASA) and 2023 Chair for the ASA Statistics in Imaging Section. She is a Standing Member of NIH Emerging Imaging Technologies in Neuroscience (EITN) Study Section and has served on the editorial boards of several scientific journals in statistics and psychiatry.

Recommended Article:

• Wang, Y. and Guo, Y. (2023).   LOCUS: A regularized blind source separation method with low-rank structure for investigating brain connectivity. Annals of Applied Statistics, 17(2): 1307-1332. (Link to Paper)

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