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

Abstract

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 ...

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

“…At the network level, networks that are engaged in a wide range of cognitive functions, such as the FPN, seem to have the highest level of dynamism (Zalesky et al, 2014;Iraji et al, 2019b). The relevance of dFC is also supported by other studies that evaluate the relationship between dFC properties and biological features such as age, gender, and cognitive processes (Thompson et al, 2013a;Leonardi et al, 2014;Elton and Gao, 2015;Hutchison and Morton, 2015;Qin et al, 2015;Yaesoubi et al, 2015a;Yaesoubi et al, 2015b;Shine et al, 2016a;Shine et al, 2016b;Kucyi et al, 2017;Kucyi, 2018).…”

“…Interestingly, regions with different cognitive and processing demands represent different levels of dynamism. The brain networks/areas that are known to be involved in higher cognitive processing show a higher level of dynamism measured by dFC than networks/areas engaged in primary processing (Zalesky et al, 2014;Chen et al, 2016;Iraji et al, 2019b).…”

“…Patients with SZ spend less time in a dFC state with strong connectivity, and the strengths of dFC between subcortical and sensory networks are weaker in these patients . Spatial dynamics studies also have revealed weaker dFC strength (transient hypoconnectivity) within particular networks (Iraji et al, 2019a;Iraji et al, 2019b) in SZ. It has been shown that the decrease in dFC strength is accompanied by higher fluctuations in dFC between brain regions (Yue et al, 2018) and within and between certain brain networks (Ma et al, 2014;Iraji et al, 2019a) in patients with SZ.…”

“…One can also explore dFC at different frequencies which is similar to adapting the window size to the frequency scale (Chang and Glover, 2010;Yaesoubi et al, 2015a). Another solution is to estimate FC for single time points (instantaneous) FC and reduce or even eliminate the need of choosing a window Yaesoubi et al, 2018;Faghiri et al, 2019;Iraji et al, 2019b;Faghiri et al, 2020). The next step after defining our window parameters is to select the node(s) of interest.…”

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

“…Using SWPC, Damaraju et al, found that SZ compared to TC tend to stay less in states which shows strong overall connectivity while they tend to spend more time in states showing weak connectivity between different domains . Other studies also reported transient reductions in both functional connectivity and networks activities (Iraji, et al, 2019a;Iraji, et al, 2019b). Miller et al, reported less dynamism (e.g.…”