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.
Bibliographical noteFunding 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. Vince Cal-houn. The authors thank Srinivas Rachakonda and Eswar Damaraju for their input.
National Institute of Mental Health, Grant/ Award Number: R01MH058262; National Institutes of Health, Grant/Award Numbers: 2R01EB005846, P20GM103472, R01REB020407; National Science Foundation, Grant/Award Number: 1539067; U.S. Department of Veterans Affairs, Grant/Award Number: I01 CX0004971
© 2019 Wiley Periodicals, Inc.
- brain spatial dynamics
- dynamic segregation and integration
- large-scale networks
- resting state fMRI (rsfMRI)
- spatial chronnectome
- spatial coupling
- spatial states
- spatiotemporal transition matrix