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

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

doi:10.1162/netn_a_00247

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

Netw Neurosci. 2022 Jul 1;6(3):634-664. doi: 10.1162/netn_a_00247. eCollection 2022 Jul.

Authors

Haleh Falakshahi^{1

2}, Hooman Rokham^{1

2}, Zening Fu¹, Armin Iraji¹, Daniel H Mathalon^{3

4}, Judith M Ford^{3

4}, Bryon A Mueller⁵, Adrian Preda⁶, Theo G M van Erp^{6

7}, Jessica A Turner^{1

8}, Sergey Plis^{1

9}, Vince D Calhoun^{1

2

9}

Affiliations

¹ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.
² School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
³ Department of Psychiatry, University of California, San Francisco, CA, USA.
⁴ San Francisco VA Medical Center, San Francisco, CA, USA.
⁵ Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA.
⁶ Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.
⁷ Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA.
⁸ Department of Psychology, Georgia State University, Atlanta, GA, USA.
⁹ Department of Computer Science, Georgia State University, Atlanta, GA, USA.

Abstract

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. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.

Keywords: Brain graph; Functional connectivity; Gaussian graphical model; Joint estimation; Resting-state fMRI; Schizophrenia.