脂質異常症における安静時fMRIによる小脳-側頭葉ネットワーク再構築：グラフ理論的解析

Hyperlipidemia (HLP) is a major risk factor for cardiovascular diseases and has a significant impact on the nervous system, potentially leading to cognitive decline. This study uses resting-state functional magnetic resonance imaging (fMRI) to analyze the brain network characteristics of HLP patients and explore their relationship with cognitive performance.This study collected fMRI data from 50 patients with HLP and 54 healthy controls. Graph theory analysis was employed to examine differences in brain functional networks between the two groups. Compared with the healthy control group, the HLP group did not exhibit abnormal global properties at the whole-brain level (all P > 0.05). At the nodal level, patients showed abnormal local network topology, characterized by decreased local efficiency, degree centrality, and clustering coefficient in several brain regions, while increased local efficiency, nodal efficiency, degree centrality (P<0.05), and clustering coefficient were observed in multiple nodes located in the cerebellum (P<0.05). Patients with hyperlipidemia demonstrate cognitive decline and aberrant activity in several brain regions, most prominently in areas essential for cognition and memory, such as the cerebellum, temporal lobe, and basal ganglia. These findings enhance understanding of brain function in hyperlipidemia and offer a novel framework for exploring disease progression.