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Hum Brain Mapp. 2019 Apr 15;40(6):1969-1986. doi: 10.1002/hbm.24505. Epub 2018 Dec 26.

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

Author information

1
The Mind Research Network, Albuquerque, New Mexico.
2
Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico.
3
Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina.
4
Department of Psychiatry, University of California San Francisco, San Francisco, California.
5
Psychiatry Service, San Francisco VA Medical Center, San Francisco, California.
6
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California.
7
Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota.
8
Departments of Psychiatry and Neurobiology, Yale University, School of Medicine, New Haven, Connecticut.
9
Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California.
10
Department of Psychology, Georgia State University, Atlanta, Georgia.
11
Department of Psychiatry, University of Iowa, Iowa, Iowa.
12
Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California.
13
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico.

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.

KEYWORDS:

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

PMID:
30588687
DOI:
10.1002/hbm.24505

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