Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function

Armin Iraji, Zening Fu, Eswar Damaraju, Thomas P. DeRamus, Noah Lewis, Juan R. Bustillo, Rhoshel K. Lenroot, Aysneil Belger, Judith M. Ford, Sarah McEwen, Daniel H. Mathalon, Bryon A Mueller, Godfrey D. Pearlson, Steven G. Potkin, Adrian Preda, Jessica A. Turner, Jatin G. Vaidya, Theo G.M. van Erp, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.

Original languageEnglish (US)
Pages (from-to)1969-1986
Number of pages18
JournalHuman Brain Mapping
Volume40
Issue number6
DOIs
StatePublished - Apr 15 2019

Bibliographical note

Funding Information:
U.S. Department of Veterans Affairs, Grant/ Award Number: I01 CX0004971; Department of Veterans Affairs, Grant/Award Number: I01 CX0004971; National Institute of Mental Health, Grant/Award Number: R01MH058262; National Science Foundation, Grant/Award Number: 1539067; National Institutes of Health, Grant/Award Numbers: 2R01EB005846, P20GM103472, R01REB020407

Funding Information:
This work was supported by grants from the National Institutes of Health grant numbers 2R01EB005846, R01REB020407, and P20GM103472; and National Science Foundation (NSF) grant 1539067 to Dr. V.D. C.; and the National Institute of Mental Health grant number R01MH058262 and the Department of Veterans Affairs Senior Research Career Scientist award I01 CX0004971 to Dr. J.M. F.

Publisher Copyright:
© 2018 Wiley Periodicals, Inc.

Keywords

  • brain dynamic
  • functional domain
  • functional module
  • high-order independent component analysis
  • intrinsic activity
  • resting state fMRI
  • schizophrenia
  • spatial domain state
  • spatial dynamics

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