Prediction of disease progression in individuals with subjective cognitive decline using brain network analysis

• DOI: 10.1111/cns.14859
• Publication Date: 2024-07-16T02:54:11Z
• Authors: Simin Deng; Si Tan; Xiaojing Song; Xinyun Lin; Kaize Yang; Xiuhong Li
• Keywords: Male; Aged, 80 and over; 0301 basic medicine; Brain; Middle Aged; Magnetic Resonance Imaging; Diagnostic Self Evaluation; 0302 clinical medicine; Disease Progression; Humans; Original Article; Cognitive Dysfunction; Female; Nerve Net; Aged

AbstractObjectiveThe objective of this study is to explore potential differences in brain functional networks at baseline between individuals with progressive subjective cognitive decline (P‐SCD) and stable subjective cognitive decline (S‐SCD), as well as to identify potential indicators that can effectively distinguish between P‐SCD and S‐SCD.MethodsAlzheimer's Disease Neuroimaging Initiative (ADNI) database was utilized to enroll SCD individuals with a follow‐up period of over 3 years. This study included 39 individuals with S‐SCD, 15 individuals with P‐SCD, and 45 cognitively normal (CN) individuals. Brain functional networks were constructed based on the AAL template, and graph theory analysis was performed to determine the topological properties.ResultsFor global metric, the S‐SCD group exhibited stronger small‐worldness with reduced connectivity among nearby nodes and accelerated compensatory information transfer capacity. For nodal efficiency, the S‐SCD group showed increased connectivity in bilateral posterior cingulate gyri (PCG). However, for nodal local efficiency, the P‐SCD group exhibited significantly reduced connectivity in the right cerebellar Crus I compared with the S‐SCD group.ConclusionThere are differences in brain functional networks at baseline between P‐SCD and S‐SCD groups. Furthermore, the right cerebellar Crus I region may be a potentially useful brain area to distinguish between P‐SCD and S‐SCD.