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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)634-664
Number of pages31
JournalNetwork Neuroscience
Volume6
Issue number3
DOIs
StatePublished - Jul 1 2022

Bibliographical note

Funding Information:
Vince D. Calhoun, National Institutes of Health(https://dx.doi.org/10.13039/100000002), Award ID: R01MH118695. Vince D. Calhoun, National Science Foundation (https://dx.doi.org/10.13039/100000001), Award ID: 2112455.

Funding Information:
Data collection was supported by the National Center for Research Resources at the National Institutes of Health (grant numbers: NIH 1 U24 RR021992, NIH 1 U24 RR025736-01).

Publisher Copyright:
© 2022 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Keywords

  • Brain graph
  • Functional connectivity
  • Gaussian graphical model
  • Joint estimation
  • Resting-state fMRI
  • Schizophrenia

PubMed: MeSH publication types

  • Journal Article

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