The analysis of time-varying connectivity in functional magnetic resonance imaging (fMRI) has become an important part of ongoing neuroscience discussions. The majority of such studies have focused primarily on temporal variations of functional connectivity among fixed regions of interest (ROIs) or networks of the brain. However, the brain reorganizes its activity on both temporal and spatial scales.Thus an approach which captures time-varying characteristics of brain functional organizations is essential to improving our understanding of brain function. Here, we propose an approach that uses blood oxygenation-level dependent (BOLD) signal to capture spatiotemporal variations of the functional domains of the brain. The approach is based on the well-accepted assumption that the brain can be modeled as a hierarchical functional architecture with different levels of granularity, and as we move to lower levels of this architecture, the complexity associated with each element of this hierarchy decreases.In other words, lower levels have less dynamic behavior and higher functional homogeneity, and there is a level at which its elements can be approximated as "functional units". A functional unit is a pattern of regions with very similar functional activity over time. At the macro-scale, we suggest high-order intrinsic connectivity networks (hICNs) obtained from a high-order spatial independent component analysis (ICA) provide a way to approximate functional units. We consider hICNs to comprise functional domains, elements of a higher level of the brain functional hierarchy which are being investigated in this study. Focusing on time-varying characteristics of functional domains, our approach captures the unique information of each functional domain at every timepoint and has the capacity to examine spatiotemporal variations of the functional domains up to the maximum temporal and spatial resolutions that exist in the data. Here, k-means clustering was deployed to summarize time-varying characteristics of each functional domain into a set of representatives called spatial domain states. Furthermore, the functional connectivity between domains was examined to identify functional modules. Each functional module contains states of functional domains with higher connectivity with each other than with other states. Our proposed approach was further evaluated using a multi-site dataset of resting-state BOLD/fMRI data collected from . CC-BY-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/391094 doi: bioRxiv preprint first posted online Aug. 13, 2018; 3 160 healthy controls and 149 patients with schizophrenia (SZ). The findings demonstrate that functional domains are spatially fluid over time. Results highlight a negative relationship between the default mode domain and the attention domain in functional module 3. The ...