The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline
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Published:2020-05-27
Issue:1
Volume:9
Page:
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ISSN:2047-9158
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Container-title:Translational Neurodegeneration
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language:en
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Short-container-title:Transl Neurodegener
Author:
Chen Haifeng, ,Sheng Xiaoning,Luo Caimei,Qin Ruomeng,Ye Qing,Zhao Hui,Xu Yun,Bai Feng
Abstract
Abstract
Background
Subjective cognitive decline (SCD) is a preclinical stage along the Alzheimer’s disease (AD) continuum. However, little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD.
Methods
Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls (HC). Pearson correlation analysis was computed to assess the relationships among network metrics, neuropsychological performance and pathological biomarkers. Finally, we used the multiple kernel learning-support vector machine (MKL-SVM) to differentiate the SCD and HC individuals.
Results
SCD individuals showed higher nodal topological properties (including nodal strength, nodal global efficiency and nodal local efficiency) associated with amyloid-β levels and memory function than the HC, and these regions were mainly located in the default mode network (DMN). Moreover, increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus (PHG) and other DMN-related regions was found in SCD individuals compared with HC individuals. These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%.
Conclusion
The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD. The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.
Funder
National Natural Science Foundation of China Jiangsu Provincial Key Medical Talents Key Research and Development Program of Jiangsu Province of China National Key Research and Development Program of China Jiangsu Province Key Medical Discipline
Publisher
Springer Science and Business Media LLC
Subject
Cellular and Molecular Neuroscience,Cognitive Neuroscience,Neurology (clinical)
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