Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline.

Xiaowen Xu,Shifu Xiao,Ling Yue,Min Zhang,Weikai Li,Peijun Wang,Tao Wang,Boyan Xu,Hai Li

doi:10.3389/fnagi.2021.688113

Abstract

Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer’s Disease (AD). Previous studies have suggested that multimodal brain networks play an important role in the early diagnosis and mechanisms underlying SCD. However, most of the previous studies focused on a single modality, and lacked correlation analysis between different modal biomarkers and brain regions. In order to further explore the specific characteristic of the multimodal brain networks in the stage of SCD, 22 individuals with SCD and 20 matched healthy controls (HCs) were recruited in the present study. We constructed the individual morphological, structural and functional brain networks based on 3D-T1 structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. A t-test was used to select the connections with significant difference, and a multi-kernel support vector machine (MK-SVM) was applied to combine the selected multimodal connections to distinguish SCD from HCs. Moreover, we further identified the consensus connections of brain networks as the most discriminative features to explore the pathological mechanisms and potential biomarkers associated with SCD. Our results shown that the combination of three modal connections using MK-SVM achieved the best classification performance, with an accuracy of 92.68%, sensitivity of 95.00%, and specificity of 90.48%. Furthermore, the consensus connections and hub nodes based on the morphological, structural, and functional networks identified in our study exhibited abnormal cortical-subcortical connections in individuals with SCD. In addition, the functional networks presented more discriminative connections and hubs in the cortical-subcortical regions, and were found to perform better in distinguishing SCD from HCs. Therefore, our findings highlight the role of the cortical-subcortical circuit in individuals with SCD from the perspective of a multimodal brain network, providing potential biomarkers for the diagnosis and prediction of the preclinical stage of AD.

Highlights

Alzheimer’s Disease (AD) is the most common cause of dementia, characterised by irreversible neurodegeneration and continuous cognitive function decline (Bonte et al, 1986; Scheltens et al, 2016)
For the brain network based on resting-state functional magnetic resonance imaging, the identified connectivity disruption of Subjective cognitive decline (SCD) focused on the middle frontal gyrus, precuneus, and cingulate gyrus, which corresponded to the default mode network (DMN) (Greicius et al, 2004; Hafkemeijer et al, 2013; Xu et al, 2020b)
Given that individuals with SCD are often associated with abnormal multimodal brain network connectivity and the involvement of multiple brain regions, alongside the advantages of machine learning, we sought to apply multi-kernel support vector machine (SVM) (MK-SVM) for the integration of morphological, structural and functional brain networks based on structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and fMRI

Summary

Introduction

Alzheimer’s Disease (AD) is the most common cause of dementia, characterised by irreversible neurodegeneration and continuous cognitive function decline (Bonte et al, 1986; Scheltens et al, 2016). The graph theoretic analysis of the topological properties of the morphological network based on structural magnetic resonance imaging (sMRI) showed that patients with SCD exhibiting lower network parameter values were associated with an increased risk of disease progression (Tijms et al, 2018). These results demonstrated that patients with SCD have altered connectivity involving multimodal brain networks. The relationship between grey matter (GM) morphology, white matter structure and functional connectivity in SCD remains unclear

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