Altered pattern analysis and identification of subjective cognitive decline based on morphological brain network

Abstract

Subjective cognitive decline (SCD) is considered the first stage of Alzheimer's disease (AD). Accurate diagnosis and the exploration of the pathological mechanism of SCD are extremely valuable for targeted AD prevention. However, there is little knowledge of the specific altered morphological network patterns in SCD individuals. In this present study, 36 SCD cases and 34 paired-matched normal controls (NCs) were recruited. The Jensen-Shannon distance-based similarity (JSS) method was implemented to construct and derive the attributes of multiple brain connectomes (i.e., morphological brain connections and global and nodal graph metrics) of individual morphological brain networks. A t-test was used to discriminate between the selected nodal graph metrics, while the leave-one-out cross-validation (LOOCV) was used to obtain consensus connections. Comparisons were performed to explore the altered patterns of connectome features. Further, the multiple kernel support vector machine (MK-SVM) was used for combining brain connectomes and differentiating SCD from NCs. We showed that the consensus connections and nodal graph metrics with the most discriminative ability were mostly found in the frontal, limbic, and parietal lobes, corresponding to the default mode network (DMN) and frontoparietal task control (FTC) network. Altered pattern analysis demonstrated that SCD cases had a tendency for modularity and local efficiency enhancement. Additionally, using the MK-SVM to combine the features of multiple brain connectomes was associated with optimal classification performance [area under the curve (AUC): 0.9510, sensitivity: 97.22%, specificity: 85.29%, and accuracy: 91.43%]. Therefore, our study highlighted the combination of multiple connectome attributes based on morphological brain networks and offered a valuable method for distinguishing SCD individuals from NCs. Moreover, the altered patterns of multidimensional connectome attributes provided a promising insight into the neuroimaging mechanism and early intervention in SCD subjects.

Keywords: graph theory; morphological brain network; multiple kernel learning; structural magnetic resonance imaging; subjective cognitive decline.

Figure 1

The procedure of data processing and classification.

Figure 2

Comparison of normalized clustering coefficient (γ), normalized characteristic path length (λ), and “small world” (σ) between the subjective cognitive decline (SCD) and normal control (NC) groups.

Figure 3

Theconsensus connections of the morphological brain network. Left: theconsensus connections of the morphological brain network selected byleave-one-out cross-validation (LOOCV) in the subjective cognitivedecline (SCD) and normal control (NC) groups based on AAL90. Thethickness of an arc in the circle indicates the discriminative powerof an edge, which is inversely proportional to the estimatedP-values. The colors were randomly generated to differentiate regionsof interest (ROIs). Right: the consensus connections selected by LOOCV. The connections were mapped on the ICBM 152 template with the BrainNetViewer package (http://nitrc.org/projects/bnv/). Blue and red represent the decrease and increase of morphological connection weight of SCD groups, respectively.

Figure 4

Receiver operating characteristic (ROC) of classification based on different morphological connectome features. C, connection; G, global metrics; N, nodal metrics; FPR, false-positive rate; TPR, true-positive rate.