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. 2020 May 27;9(1):21.
doi: 10.1186/s40035-020-00201-6.

The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline

Haifeng Chen  1   2   3   4 , Xiaoning Sheng  1   2   3   4 , Caimei Luo  1   2   3   4 , Ruomeng Qin  1   2   3   4 , Qing Ye  1   2   3   4 , Hui Zhao  1   2   3   4 , Yun Xu  1   2   3   4 , Feng Bai  5   6   7   8 ; Alzheimer’s Disease Neuroimaging Initiative
Affiliations

The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline

Haifeng Chen et al. Transl Neurodegener. .

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.

Keywords: Compensatory mechanism; Machine learning; Subjective cognitive decline; rs-fMRI.

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Conflict of interest statement

The authors have declared that no competing interest exists.

Figures

Fig. 1
Fig. 1
The altered nodal strength, nodal global efficiency and nodal local efficiency between SCD and HC. a The SCD group showing significantly increased strength in four brain regions; b The SCD group showing significantly increased nodal global efficiency in 28 brain regions; c The SCD group showing significantly increased nodal local efficiency in 27 brain regions. Abbreviations: SCD, subjective cognitive decline; HC, healthy control; The color bar represents the label of brain regions in AAL-90 atlas
Fig. 2
Fig. 2
The altered rich club organization between SCD and HC. a and b The top 14 (15%) highest-degree nodes were chosen to represent rich club nodes based on the averaged nodal degree across all participants; c The sketch map of rich club organization; d Significant differences in the strength, degree and average strength of the feeder and local connections were identified, while no significant differences were found in rich club connections. Abbreviations: SCD, subjective cognitive decline; HC, healthy control; * indicates a statistical difference between groups, p < 0.05
Fig. 3
Fig. 3
The altered connected subnetwork based on the NBS analysis. A single connected subnetwork with 30 nodes and 35 connections, which exhibited higher connection strength in the SCD group compared with the HC group (p < 0.001, corrected); b The 28 out of 30 node within the subnetwork were classified into non-hub regions and the 33 out of 35 connections belonged to the local connections; c The increased connectivity was primarily involved in the medium-range connections based on the Euclidean distance. Abbreviations: SCD, subjective cognitive decline; HC, healthy control; NBS, network-based statistic
Fig. 4
Fig. 4
Relationships among altered network metrics, biomarkers and neuropsycholohical performance. a The scores on the LM-immediate were negatively associated with nodal local efficiency of the DCG.L (r = − 0.303, P = 0.015) in the HC group; b and c The scores on the LM-immediate were negatively associated with nodal global efficiency of the SFGdor.R (r = − 0.279, P = 0.023) (b) and the SFGmed.L (r = − 0.294, P = 0.017) (c) in the SCD group; d, e and f The CSF Aβ1–42 was negatively related to the nodal strength of PHG.L (r = − 0.671, P = 0.024) (d), nodal global efficiency of the TPOsup.R (r = − 0.642, P = 0.033) (e) and nodal local efficiency of the IFGoperc.R (r = − 0.654, P = 0.029) (f) in the SCD group; g The scores on the CCI were positively associated with nodal global efficiency of the TPOsup.L (r = 0.297, P = 0.016) in the SCD group. Abbreviations: HC, healthy control; SCD, subjective cognitive decline; DCG.L, left median cingulate and paracingulate gyri; SFGdor.R, right dorsolateral superior frontal gyrus; SFGmed.L, left medial superior frontal gyrus; PHG.L, left parahippocampal gyrus; TPOsup.R, right temporal pole-superior temporal gyrus; IFGoperc.R, right inferior frontal gyrus-opercular part; CSF, cerebrospinal fluid; Aβ, amyloid-β; TPOsup.L, left temporal pole-superior temporal gyrus; LM, Logical Memory; CCI, cognitive change index
Fig. 5
Fig. 5
Result of discriminative analysis. For single-modality analyses, the functional connections exhibited the higher accuracy rate (76.15%) than the nodal properties which achieved the accuracy rate of 66.15%. Typically, classification accuracy improved after combining the network features of the two modalities, achieving the accuracy of up to 79.23%
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