OBJECTIVES

Cerebral small vessel disease (CSVD) is a heterogeneous cerebrovascular syndrome with a variety of pathological mechanisms and clinical manifestations. A majority of research have shown that CSVD is associated with reduced expression of structural covariance networks (SCNs), but most of these SCN studies based on the group-level, which limits their ability to reflect individual variations in heterogeneous diseases. The purpose of this study is to analyze the structural covariance aberrations in patients with cerebral small vessels by utilizing individualized differential structural covariance network (IDSCN) analysis to explore the differences in SCNs and depressive states at the individual-level.

METHODS

A total of 22 CSVD patients with depression (CSVD+D) and 34 healthy controls (HCs) were included in this study. IDSCNs were constructed for each subject based on regional gray matter volumes derived from their T1-weighted MRI images. The unpaired-sample t-test was used to compare the IDSCNs between the two groups to obtain the differential structural covariance edge and its distribution. Finally, correlation analyses were performed between the differential edge, the total CSVD imaging burden and Hamilton Rating Scale for Depression (HAMD) score.

RESULTS

(1) Compared with HCs, the CSVD+D patients exhibited heterogeneous distributions of differential structural covariance edge, with the differential edge located between the caudate nucleus and the cerebellum. (2) There was a significant positive correlation between the total CSVD imaging burden and HAMD scores in CSVD patients with depression ( = 0.692,  < 0.001).

CONCLUSION

This study analyzed the IDSCNs between CSVD+D patients and HCs, which may indicate that the individual structural covariance aberrations between the caudate nucleus and cerebellum may contribute to depression in CSVD patients. Additionally, the higher total CSVD imaging burden of CSVD patients may indicate more severe depression. This finding suggests that early magnetic resonance imaging (MRI) assessment in CSVD patients may facilitate the early identification of depressive states and their severity in the near future.