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Hum Brain Mapp. 2019 Mar 18. doi: 10.1002/hbm.24580. [Epub ahead of print]

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.

Author information

1
The Mind Research Network, Albuquerque, New Mexico.
2
Department of Mathematics and Statistics, 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
Department of Psychiatry, 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 City, 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 brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.

KEYWORDS:

brain spatial dynamics; dynamic segregation and integration; large-scale networks; resting state fMRI (rsfMRI); schizophrenia; spatial chronnectome; spatial coupling; spatial states; spatiotemporal transition matrix

PMID:
30884018
DOI:
10.1002/hbm.24580

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