Imagine your brain is like a vast, intricate network with different regions connected by highways. Well, scientists have discovered that changes in this network can signal the early stages of Alzheimer’s disease. In a recent study, researchers used advanced imaging techniques to analyze the brain networks of individuals with subjective cognitive decline (SCD), the earliest phase of Alzheimer’s. They found distinct altered patterns in certain brain regions, such as the frontal and parietal lobes, which are part of the default mode network and frontoparietal task control network. These changes were associated with enhanced local efficiency and modularity. To further validate their findings, the scientists developed a machine learning algorithm called multiple kernel support vector machine (MK-SVM) that combined multiple brain network attributes to accurately distinguish SCD individuals from normal controls. The MK-SVM achieved an impressive accuracy of 91.43%, showing great potential for early detection and intervention in Alzheimer’s disease.

To learn more about this fascinating study and dive into the intricate world of brain networks and Alzheimer’s disease, check out the full article!

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.

Read Full Article (External Site)