Neurovascular uncoupling in type 2 diabetes mellitus: multimodal MRI reveals the association between hippocampal and subcortical structures dysfunction and cognitive decline
Abstract
To investigate the efficiency of neurovascular coupling (NVC) in hippocampal and subcortical structures in type 2 diabetes mellitus (T2DM) and its relations with cognition and glycemic control, and to characterize multimodal MRI features of T2DM-related cognitive decline. Forty-nine T2DM patients and 43 healthy controls underwent montreal cognitive assessment (MoCA), digit span (DST), clock drawing (CDT), and fasting blood glucose (FBG)/glycated hemoglobin (HbA1c) measurement. High-resolution T1-weighted structural images, three-dimensional arterial spin labeling (3D-ASL) perfusion images, and resting-state functional MRI (rs-fMRI) were acquired on a 3T scanner. Volumes, cerebral blood flow (CBF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), degree centrality (DC), and NVC were calculated for bilateral hippocampus, amygdala, anterior thalamus, caudate, nucleus accumbens, putamen, and globus pallidus. Partial correlations assessed associations between imaging metrics, glycemic parameters, and MoCA scores. T2DM had significantly lower MoCA ( P < 0.05) but no differences in DST or CDT. Right anterior thalamic volume was reduced. CBF decreased in bilateral hippocampus, bilateral anterior thalamus, and right amygdala. ReHo reduced in right anterior thalamus and left amygdala. NVC decreased in left hippocampus and left amygdala. No basal ganglia differences were found. MoCA negatively correlated with FBG ( r = −0.405) and positively with right thalamic volume ( r = 0.311). HbA1c and FBG negatively correlated with left anterior thalamic CBF ( r = −0.344 and −0.367, P < 0.05). T2DM shows decreased perfusion, reduced regional synchronization, and neurovascular uncoupling in limbic-subcortical structures including hippocampus, anterior thalamus, amygdala, while basal ganglia motor circuits are preserved. Multimodal MRI metrics may provide imaging targets for early identification and intervention.