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

Abstract

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.

Fig. 1. Schematic workflow of structural covariance network analyses in this study.

CSA cortical surface area, CT cortical thickness.

Fig. 2. Group differences of integration and small-worldness of CSA.

Group differences in “integration” and “small-worldness” metrics of structural covariance networks based on the cortical surface area at baseline and 3-year follow-up at the range of 6–40% network sparsity, including A, D normalized path length, B, E global efficiency, and C, F “small-worldness”. The upper and lower blue lines represented a 95% confidence interval, whereas the black dot line in the middle denoted the mean difference after 1000 permutations. The red line represents the true group differences, which fall outside the confidence interval, indicating significant group differences (P < 0.05) under the current threshold. The positive values indicate young adults with HCU > HCs, and the negative values indicate young adults with HCU < HCs. The subpanels showed group differences in the area under the curve (AUC) value in each metric of SCNs. Compared with HCs, the young adults with HCU showed significantly higher AUC value of small worldness at baseline. *P < 0.05.

Fig. 3. Group differences of segregration of CSA.

Group differences in “segregation” metrics of structural covariance networks based on the cortical surface area at baseline and 3-year follow-up at the range of 6–40% network sparsity, including A, C normalized clustering coefficient, and B, D local efficiency. The upper and lower blue lines represented a 95% confidence interval, whereas the black dot line in the middle denoted the mean difference after 1000 permutations. The red line represents the true group differences, which fall outside the confidence interval and indicate significant group differences (P < 0.05) under the current threshold. The positive values indicate young adults with HCU > HCs, and the negative values indicate young adults with HCU < HCs. The subpanels showed group differences in the area under the curve (AUC) value in each metric of SCNs. Compared with HCs, the young adults with HCU showed significantly higher AUC value of local efficiency at baseline. **P < 0.01.

Fig. 4. Group differences of integration and small-worldness of CT.

Group differences in “integration” and “small-worldness” metrics of structural covariance networks based on cortical thickness at baseline and 3-year follow-up at the range of 6–40% network sparsity, including A, D normalized path length, B, E global efficiency, and C, F “small-worldness”. The upper and lower blue lines represented a 95% confidence interval, whereas the black dot line in the middle denoted the mean difference after 1000 permutations. The red line represents the true group differences, which fall outside the confidence interval and indicate significant group differences (P < 0.05) under the current threshold. The positive values indicate young adults with HCU > HCs, and the negative values indicate young adults with HCU < HCs. The subpanels showed group differences in the area under the curve (AUC) value in each metric of SCNs. Compared with HCs, the young adults with HCU showed significantly lower AUC value of small worldness at baseline. **P < 0.01.

Fig. 5. Group differences of segregration of CT.

Group differences in “segregation” metrics of structural covariance networks based on cortical thickness at baseline and 3-year follow-up at the range of 6%-40% network sparsity, including A, C normalized clustering coefficient, and B, D local efficiency. The upper and lower blue lines represented a 95% confidence interval, whereas the black dot line in the middle denoted the mean difference after 1000 permutations. The red line represents the true group differences, which fall outside the confidence interval and indicate significant group differences (P < 0.05) under the current threshold. The positive values indicate young adults with HCU > HCs, and the negative values indicate young adults with HCU < HCs. The subpanels showed group differences in the area under the curve (AUC) value in each metric of SCNs. Compared with HCs, the young adults with HCU showed significantly lower AUC value of local efficiency at baseline. **P < 0.01.

Fig. 6. Group differences in nodal network metrics (nodal degree, nodal efficiency, and nodal betweenness centrality) of structural covariance networks based on cortical surface area and cortical thickness at baseline and 3-year follow-up.

Regions that showed significant differences in AUC in the range from 6% to 40% network sparsity in nodal degree, nodal efficiency, and nodal betweenness centrality between groups were colored (P < 0.05, false discovery rate corrected). The green color represented regions that have altered nodal degrees in young adults with HCU. The blue color denoted regions that have altered nodal efficiency in young adults with HCU. The red color represented regions that have altered nodal betweenness centrality in young adults with HCU. This graph was plotted using the R and RStudio with both ggplot2 and ggseg packages.