Optimal Organization of Functional Connectivity Networks for Segregation and Integration With Large-Scale Critical Dynamics in Human Brains.

Xinchun Zhou,Lianchun Yu,Guangyao Liu,Zhixi Wu,Liwei Liu,Ningning Ma,Jianfeng Feng,Benseng Song

doi:10.3389/fncom.2021.641335

Abstract

The optimal organization for functional segregation and integration in brain is made evident by the “small-world” feature of functional connectivity (FC) networks and is further supported by the loss of this feature that has been described in many types of brain disease. However, it remains unknown how such optimally organized FC networks arise from the brain's structural constrains. On the other hand, an emerging literature suggests that brain function may be supported by critical neural dynamics, which is believed to facilitate information processing in brain. Though previous investigations have shown that the critical dynamics plays an important role in understanding the relation between whole brain structural connectivity and functional connectivity, it is not clear if the critical dynamics could be responsible for the optimal FC network configuration in human brains. Here, we show that the long-range temporal correlations (LRTCs) in the resting state fMRI blood-oxygen-level-dependent (BOLD) signals are significantly correlated with the topological matrices of the FC brain network. Using structure-dynamics-function modeling approach that incorporates diffusion tensor imaging (DTI) data and simple cellular automata dynamics, we showed that the critical dynamics could optimize the whole brain FC network organization by, e.g., maximizing the clustering coefficient while minimizing the characteristic path length. We also demonstrated with a more detailed excitation-inhibition neuronal network model that loss of local excitation-inhibition (E/I) balance causes failure of critical dynamics, therefore disrupting the optimal FC network organization. The results highlighted the crucial role of the critical dynamics in forming an optimal organization of FC networks in the brain and have potential application to the understanding and modeling of abnormal FC configurations in neuropsychiatric disorders.

Highlights

Functional connectivity (FC) analysis of resting state human brain allows to understand how the functional networks are organized, how this organization is related to behavior, and how it changes in case of pathology
After preprocessing with the standard preprocessing procedure (“METHOD AND MATERIALS,” “functional magnetic resonance imaging (fMRI) Data Acquisition and Preprocessing”), the automated anatomical labeling atlas (AAL) (Tzourio-Mazoyer et al, 2002) was used to parcellate the brain into 90 brain regions, and the mean BOLD signals were extracted in each brain region by averaging the signals of all voxels within the region
Recent analysis of human brain functional networks derived from EEG/MEG and fMRI experiments showed that these networks exhibit prominent small-world organization

Summary

Introduction

Functional connectivity (FC) analysis of resting state human brain allows to understand how the functional networks are organized, how this organization is related to behavior, and how it changes in case of pathology (van den Heuvel and Hulshoff Pol, 2010; Lee et al, 2013; Yu et al, 2016). Small-world networks exhibit shorter characteristic path length but higher clustering coefficients (Watts and Strogatz, 1998) This specific organization of functional network is believed to benefit the higher-level cognitive functions requiring the integration of information from different brain regions (Watts and Strogatz, 1998), maximize efficiency at a minimal cost for effective information processing between multiple brain regions (Achard and Bullmore, 2007), and promote low wiring costs (Bassett and Bullmore, 2006). The small-world organization of brain functional network is likely to be related to human intellectual performance (van den Heuvel et al, 2009) and disrupted with normal aging (Wang et al, 2010). Little is known about the underlying dynamics based on which this optimal FC network is established, and how its disruption induced by disease is associated with changes in brain dynamics

Methods

Results

Conclusion