Abnormal developmental of structural covariance networks in young adults with heavy cannabis use: a 3-year follow-up study

• Published in: Translational psychiatry Vol. 14; no. 1; p. 45
• Main Authors: Xu, Hui, Li, Jiahao, Huang, Huan, Yin, Bo, Li, Dan-Dong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.01.2024; Nature Publishing Group
• Subjects: 59/57; 631/378; 692/53/2423; 692/699/476/5; Behavioral Sciences; Biological Psychology; Brain - diagnostic imaging; Brain - pathology; Cannabis; Follow-Up Studies; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Neurosciences; Pharmacotherapy; Psychiatry; Young Adult; Young adults
• ISSN: 2158-3188; 2158-3188
• DOI: 10.1038/s41398-024-02764-8

Heavy cannabis use (HCU) exerts adverse effects on the brain. Structural covariance networks (SCNs) that illustrate coordinated regional maturation patterns are extensively employed to examine abnormalities in brain structure. Nevertheless, the unexplored aspect remains the developmental alterations of SCNs in young adults with HCU for three years, from the baseline (BL) to the 3-year follow-up (FU). These changes demonstrate dynamic development and hold potential as biomarkers. A total of 20 young adults with HCU and 22 matched controls were recruited. All participants underwent magnetic resonance imaging (MRI) scans at both the BL and FU and were evaluated using clinical measures. Both groups used cortical thickness (CT) and cortical surface area (CSA) to construct structural covariance matrices. Subsequently, global and nodal network measures of SCNs were computed based on these matrices. Regarding global network measures, the BL assessment revealed significant deviations in small-worldness and local efficiency of CT and CSA in young adults with HCU compared to controls. However, no significant differences between the two groups were observed at the FU evaluation. Young adults with HCU displayed changes in nodal network measures across various brain regions during the transition from BL to FU. These alterations included abnormal nodal degree, nodal efficiency, and nodal betweenness in widespread areas such as the entorhinal cortex, superior frontal gyrus, and parahippocampal cortex. These findings suggest that the topography of CT and CSA plays a role in the typical structural covariance topology of the brain. Furthermore, these results indicate the effect of HCU on the developmental changes of SCNs in young adults.