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The spatial chronnectome reveals a dynamic interplay between functional segregation and integration
Author(s) -
Iraji Armin,
Deramus Thomas P.,
Lewis Noah,
Yaesoubi Maziar,
Stephen Julia M.,
Erhardt Erik,
Belger Aysneil,
Ford Judith M.,
McEwen Sarah,
Mathalon Daniel H.,
Mueller Bryon A.,
Pearlson Godfrey D.,
Potkin Steven G.,
Preda Adrian,
Turner Jessica A.,
Vaidya Jatin G.,
Erp Theo G. M.,
Calhoun Vince D.
Publication year - 2019
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24580
Subject(s) - voxel , computer science , spatial analysis , set (abstract data type) , dynamic functional connectivity , coupling (piping) , transient (computer programming) , neuroscience , artificial intelligence , resting state fmri , psychology , geography , mechanical engineering , remote sensing , engineering , programming language , operating system
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.

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