Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data

Sergey Plis | Jan 1, 2022| Publications

Authors : Haleh Falakshahi, Hooman Rokham, Zening Fu, Armin Iraji, Daniel H Mathalon, Judith M Ford, Bryon A Mueller, Adrian Preda, Theo GM van Erp, Jessica A Turner, Sergey Plis, Vince D Calhoun

Publication date : 2022/1

Journal : Network Neuroscience

Pages : 1-45

Description

Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks …

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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.

Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data

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