Individual-level brain morphological similarity networks: Current methodologies and applications

Abstract

Aims: The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)-based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI-based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual-level morphological networks.

Background: There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual-level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders.

Conclusion: This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions.

Keywords: brain network; individual level; morphological similarity network; structural magnetic resonance imaging.

FIGURE 1

A flowchart for constructing brain morphological, anatomical, and functional networks. The construction of human brain morphological, anatomical, and functional networks involves the utilization of sMRI, dMRI, and fMRI data, encompassing the following procedures: defining network nodes using a priori atlas to parcellate the whole brain, defining network edges of estimating relationship between nodes (morphological connectivity is defined as the interregional similarity of morphological features, anatomical connectivity is based on diffusion tractography, and functional connectivity is generally defined as the statistical correlation of BOLD signals across different regions), obtaining connectivity matrix and finally generating the morphological network (group level), anatomical as well as functional network (individual level). BOLD, blood oxygen level dependent; dMRI, diffusion magnetic resonance imaging; fMRI, functional magnetic resonance imaging; GMV, gray matter volume; sMRI, structural magnetic resonance imaging.

FIGURE 2

The construction of individual‐level brain morphological similarity networks. (A) The basis for constructing individual‐level morphological brain networks: morphological metrics extracted from individual structural MRI. (B) The single metric‐based methods for constructing individual morphological similarity networks, such as calculating the statistical correlation of morphological metric between different cubes (top‐left panel), estimating the similarity in the distribution of regional morphological indicator, , (top‐right panel), and the network template perturbation approach (bottom‐left panel). (C) The multiple metrics‐based methods for constructing morphological similarity networks, including (1) defining multivariate Euclidean distance to depict multiple metrics‐based interregional similarity, calculating the statistical correlation of (2) multiple morphological features (extracted from single‐modal or multimodal MRI), or (3) radiomics features between regions. (D) Each of the above methods ultimately generates the similarity matrix, which is subsequently used to generate the network graphs for further graph‐theoretic analyses. CT, cortical thickness; FA, fractional anisotropy; FI, folding index; GC, Gaussian curvature; GM, gray matter; GMV, gray matter volume; IC, intrinsic curvature; MC, mean curvature; MD, mean diffusivity; MRI, magnetic resonance imaging; MT, magnetization transfer; PD, probability density; PDF, probability density function; SA, surface area; SD, sulcal depth; WM, white matter.