Progressive Brain Degeneration From Subjective Cognitive Decline to Amnestic Mild Cognitive Impairment: Evidence From Large-Scale Anatomical Connection Classification Analysis

Abstract

People with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are both at high risk for Alzheimer's disease (AD). Behaviorally, both SCD and aMCI have subjective reports of cognitive decline, but the latter suffers a more severe objective cognitive impairment than the former. However, it remains unclear how the brain develops from SCD to aMCI. In the current study, we aimed to investigate the topological characteristics of the white matter (WM) network that can successfully identify individuals with SCD or aMCI from healthy control (HC) and to describe the relationship of pathological changes between these two stages. To this end, three groups were recruited, including 22 SCD, 22 aMCI, and 22 healthy control (HC) subjects. We constructed WM network for each subject and compared large-scale topological organization between groups at both network and nodal levels. At the network level, the combined network indexes had the best performance in discriminating aMCI from HC. However, no indexes at the network level can significantly identify SCD from HC. These results suggested that aMCI but not SCD was associated with anatomical impairments at the network level. At the nodal level, we found that the short-path length can best differentiate between aMCI and HC subjects, whereas the global efficiency has the best performance in differentiating between SCD and HC subjects, suggesting that both SCD and aMCI had significant functional integration alteration compared to HC subjects. These results converged on the idea that the neural degeneration from SCD to aMCI follows a gradual process, from abnormalities at the nodal level to those at both nodal and network levels.

Keywords: Alzheimer’s disease; amnestic mild cognitive impairment; network; subjective cognitive decline; white matter.

FIGURE 1

Workflow of data analysis. White matter matrices were constructed based on the AAL template, an automated anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume, during which indexes (GE, SP, LE, and CE) at the network and nodal level were extracted. At the network level, four indexes, including GE, SP, LE, and CE were used separately or combined to build a classifier. GE, global efficiency; SP, short path length; LE, local efficiency; CE, clustering coefficient. At the nodal level, each index includes 90 features. To select the most discriminate features, LSVM (an outer LOOCV) was nested with a feature selection produce (an inner LOOCV). LSVM, linear support vector model; LOOCV, leave-one-out cross-validation. Similar to nodal level, four indexes were used separately or combined to build a classifier.

FIGURE 2

The mean structural network matrix of each group built on the white matter. The horizontal and vertical coordinates represent 90 brain regions in the AAL template. Values in each cell represent the mean FN between two brain areas.

FIGURE 3

Receiver operating characteristic (ROC) and area under the curve (AUC) or accuracy distribution when all indexes (i.e., global efficiency, local efficiency, cluster efficiency, and short path) at the network level were combined. ROC maps shows the classification performance (A,B), where a greater AUC corresponds to better performance. AUC and accuracy distribution maps (C–F) were built by permutation tests, during which group labels were randomly arranged 1,000 times. Arrows in the distribution maps marker AUC or accuracy based on real group labels.

FIGURE 4

Receiver operating characteristic (ROC) maps and area under the curve (AUC) or accuracy distribution of a classifier built on indexes at the nodal level. ROC maps show the classification performance (A,B), where greater AUC corresponds to better performance. AUC and accuracy distribution maps (C–F) were built by permutation tests, during which group labels were randomly arranged 1,000 times. Arrows in the distribution maps marker AUC or accuracy based on real group labels.

FIGURE 5

Correlation between network metrics and memory in different groups.