Objectives To investigate the relationship between diffusion tensor imaging (DTI) indicators and cerebral small vessel disease (CSVD) with depressive states, and to explore the underlying mechanisms of white matter damage in CSVD with depression. Method A total of 115 elderly subjects were consecutively recruited from the neurology clinic, including 36 CSVD patients with depressive state (CSVD+D), 34 CSVD patients without depressive state (CSVD-D), and 45 controls. A detailed neuropsychological assessment and multimodal magnetic resonance imaging (MRI) were performed. Based on tract-based spatial statistics (TBSS) analysis and structural network analysis, differences between groups were compared, including white matter fiber indicators (fractional anisotropy and mean diffusivity) and structural brain network indicators (global efficiency, local efficiency and network strength), in order to explore the differences and correlations of DTI parameters among the three groups. Results There were no significant differences in terms of CSVD burden scores and conventional imaging findings between the CSVD-D and CSVD+D groups. Group differences were found in DTI indicators (p <  0.05), after adjusting for age, gender, education level, and vascular risk factors (VRF), there were significant correlations between TBSS analysis indicators and depression, including: fractional anisotropy (FA) (r = − 0.291, p <  0.05), mean diffusivity (MD) (r = 0.297, p < 0.05), at the same time, between structural network indicators and depression also show significant correlations, including: local efficiency (ELocal) (r = − 0.278, p < 0.01) and network strength (r = − 0.403, p < 0.001). Conclusions Changes in FA, MD values and structural network indicators in DTI parameters can predict the depressive state of CSVD to a certain extent, providing a more direct structural basis for the hypothesis of abnormal neural circuits in the pathogenesis of vascular-related depression. In addition, abnormal white matter alterations in subcortical neural circuits probably affect the microstructural function of brain connections, which may be a mechanism for the concomitant depressive symptoms in CSVD patients.

Background Impaired working memory (WM) is an important clinical symptom of cognitive dysfunction associated with cerebral small vessel disease (CSVD). Theta oscillations play an important role in the regulation of learning, WM and synaptic plasticity. Therefore, we speculate that theta oscillation may play an important role in the process of working memory impairment in CSVD. Methods Seventy-eight patients with CSVD (mean age 66.18 ± 1.42) and 49 healthy controls (HCs) (mean age 66.53 ± 1.3) were recruited to perform the WM task. Neural oscillations and functional connectivity during the encoding, maintenance, and retrieval phases of WM were evaluated during performance of WM test. Results Compared with the control group, the working memory behavior of the CSVD group showed a significantly longer reaction time and lower accuracy rate. The energy density and functional connection (FC) strength of the theta band in frontal region of the CSVD group were significantly lower than those of the control group, and the theta oscillation in the retrieval phase was significantly higher than that in the coding phase. However, there was no significant change in FC strengths among three phases. Both in the two groups, the FC was significantly positively correlated with accuracy and negatively correlated with reaction time (RT). Conclusion Our results indicated that CSVD patients have significant working memory impairment, and the lack of theta oscillation in the frontal region and the abnormal functional connection of the brain network may be one of its potential neurophysiological mechanisms.

Objectives: To investigate whether there is a difference in the incidence of depression between patients with small cerebral vascular disease (CSVD) and normal controls (NC), and the relationship between depression and white matter fiber damage and structural brain network in elderly patients with CSVD. Method: A total of 115 elderly subjects were consecutively recruited from the neurology clinic, including 36 patients with CSVD with depressive state, 34 patients with CSVD without depressive state, and 45 NCs. A detailed neuropsychological assessment and multimodal MRI were performed. Based on TBSS analysis and structural network analysis, differences between groups were compared, and logistic regression was used to determine the predictive value of TBSS indicators and structural brain network measures for depression in CSVD patients. Results: Group differences were found in global TBSS analysis and brain network measures. After adjusting for age, gender, education level, and vascular risk factors (VRF), there were significant correlations between TBSS indicators and structural network indicators and depression, including: FA (r= -0.291, p <0.05), ELocal (r = -0.278, p < 0.01) and in network strength (r = - 0.403, p < 0.001). At the same time, ELocal was an independent risk factor for depression in patients with CSVD.Conclusions: Changes in FA, MD values and structural network indicators in DTI parameters can predict the depressive state of CSVD to a certain extent, providing a more direct structural basis for the hypothesis of abnormal neural circuits in the pathogenesis of vascular depression. In addition, we believe that abnormal white matter alterations in subcortical neural circuits affect the microstructural function of brain connections, which may be a mechanism for the concomitant depressive symptoms in CSVD patients.