Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data

Sergey Plis | Mar 1, 2022| Publications

Authors : Shile Qi, Rogers F Silva, Daoqiang Zhang, Sergey M Plis, Robyn Miller, Victor M Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D Calhoun

Publication date : 2022/3/1

Journal : Human brain mapping

Volume : 43

Issue : 4

Pages : 1280-1294

Publisher : John Wiley & Sons, Inc.

Description

Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then …

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I received my Ph.D. degree in Computer Science in 2007 from The University of New Mexico, Albuquerque, NM, USA. Currently, I am an associate professor of CS at Georgia State University, and the Director of Machine Learning core at the TReNDS institute. My research interests within the fields of machine learning, AI, and data science lie in developing novel and applying existing techniques and approaches to analyzing large scale datasets. One of my key goals is to take advantage of the strengths of multiple data modalities and infer structure and patterns that are hard to obtain non-invasively and/or that are unavailable for direct observation. Ongoing work is focused on inferring multimodal probabilistic and causal descriptions based on fusion of fast and slow imaging modalities. This includes feature estimation via deep learning-based pattern recognition and learning causal graphical models. Besides academia, I’ve led AI research teams developing deep tech AI products including NLP and speech processing solutions that are currently shipped to customers. My research is supported by generous grants from NSF and NIH.

Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data

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