Construction of multi-scale consistent brain networks: methods and applications.

Bao Ge,Tianming Liu,Xintao Hu,Yin Tian,Hanbo Chen,Junwei Han,Dajiang Zhu,Tuo Zhang,Lei Guo

doi:10.1371/journal.pone.0118175

Abstract

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

Highlights

Construction and modeling of structural and functional brain networks has gained great interest recently, due to its significant importance in revealing the brain’s structural architecture and functional dynamic behaviors [1,2,3,4]
Without the correspondences between those multi-scale brain networks, we cannot effectively compare brain networks across individuals [23] and many meaningful statistical measurements of network behaviors cannot be accurately performed. To address these two challenges that have not been sufficiently addressed by the above previous methods in defining network nodes to construct brain networks, this paper aims to present a novel approach to constructing multi-scale common structural brain networks with intrinsically-established correspondences across individuals and populations
We examined the sixth sub-network whose DICCCOL landmarks were contained in 45 experiments of the BrainMap database [42], we searched its original functional MRI (fMRI) experiment and the corresponding behavioral domain and found that most of them are located in the regions of cognition and execution speech

Summary

Introduction

Construction and modeling of structural and functional brain networks has gained great interest recently, due to its significant importance in revealing the brain’s structural architecture and functional dynamic behaviors [1,2,3,4]. Previous methods in defining network nodes to construct whole brain structural or functional networks can be roughly classified into three broad categories. The first group of methods divided the brain into nodes according to an existing anatomical brain atlas [5,9,10] These methods typically depend on the brain atlas used and image registration techniques. The third school of methods is data-driven, which uses morphological [15], structural [16] or functional features [17] to define nodes. These methods do not depend on specific models, and have the optimal division within individual brains

Objectives

Methods

Conclusion