Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data.
Brain graph
Functional connectivity
Gaussian graphical model
Joint estimation
Resting-state fMRI
Schizophrenia
Journal
Network neuroscience (Cambridge, Mass.)
ISSN: 2472-1751
Titre abrégé: Netw Neurosci
Pays: United States
ID NLM: 101719149
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
03
06
2021
accepted:
23
03
2022
entrez:
7
10
2022
pubmed:
8
10
2022
medline:
8
10
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36204419
doi: 10.1162/netn_a_00247
pii: netn_a_00247
pmc: PMC9531579
doi:
Types de publication
Journal Article
Langues
eng
Pagination
634-664Informations de copyright
© 2022 Massachusetts Institute of Technology.
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