AUTHOR=Xu Kun , Wang Jun , Liu Guangyao , Yan Jiahao , Chang Miao , Jiang Linzhen , Zhang Jing 

TITLE=Altered dynamic effective connectivity of the default mode network in type 2 diabetes

JOURNAL=Frontiers in Neurology

VOLUME=Volume 14 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1324988

DOI=10.3389/fneur.2023.1324988

ISSN=1664-2295

ABSTRACT=Altered functional connectivity of resting-state functional magnetic resonance imaging (rs-fMRI) within default mode network (DMN) regions has been verified to be closely associated with cognitive decline in patients with Type 2 diabetes mellitus (T2DM), but most studies neglected the fluctuations of brain activities—the dynamic effective connectivity (DEC) within DMN of T2DM is still unknown. Our study recruited 40 healthy controls (HC) and 36 T2DM patients to examine the variation of DEC of DMN in T2DM with the methodologies of independent components analysis (ICA) and multivariate granger causality analysis (mGCA). As results, altered DEC within DMN only showed decrease in state 1 and the causal information flow of diabetic patients major affected areas which are closely associated with food craving and metabolic regulation with the trend of neuronal activity weakening in state1 and strengthening in state2 in diabetes. In addition, T2DM patients stayed longer  in state 1 with lower brain activity and exhibited decreased transition rate between states. Moreover, these changes related negatively with the MoCA scores and education years and positively with HbA1C level and diastolic blood pressure. Notably, there were some crossovers of these results in bilateral middle cingulate cortex (MCC), middle frontal gyrus (MFG) and left inferior parietal lobule (LIPL) which need further focus in the future. In summary, our findings highlight the low brain activity status of T2DM patients, which are strongly correlated with clinical characteristics and MoCA scores and may offer a fresh perspective on brain dynamic activities to understand the mechanisms underlying T2DM-related cognitive deficits.