Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder

Sergey Plis | Jul 11, 2022| Publications

Authors : Xinhui Li, Eloy Geenjaar, Zening Fu, Sergey Plis, Vince Calhoun

Publication date : 2022/7/11

Conference : 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Pages : 1477-1480

Publisher : IEEE

Description

Mental disorders such as schizophrenia have been challenging to characterize due in part to their heterogeneous presentation in individuals. Most studies have focused on identifying groups differences and have typically ignored the heterogeneous patterns within groups. Here we propose a novel approach based on a variational autoencoder (VAE) to interpolate static functional network connectivity (sFNC) across individuals, with group-specific patterns between schizophrenia patients and controls captured simultaneously. We then visualize the original sFNC in a 2D grid according to the samples in the VAE latent space. We observe a high correspondence between the generated and the original sFNC. The proposed framework facilitates data visualization and can potentially be applied to predict the stage that a subject falls within a disorder continuum as well as characterize individual heterogeneity within and …

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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.

Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder

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