Effect of baseline cannabis use and working-memory network function on changes in cannabis use in heavy cannabis users: a prospective fMRI study

Abstract

Theoretical models of addiction suggest that a substance use disorder represents an imbalance between hypersensitive motivational processes and deficient regulatory executive functions. Working-memory (a central executive function) may be a powerful predictor of the course of drug use and drug-related problems. Goal of the current functional magnetic resonance imaging study was to assess the predictive power of working-memory network function for future cannabis use and cannabis-related problem severity in heavy cannabis users. Tensor independent component analysis was used to investigate differences in working-memory network function between 32 heavy cannabis users and 41 nonusing controls during an N-back working-memory task. In addition, associations were examined between working-memory network function and cannabis use and problem severity at baseline and at 6-month follow-up. Behavioral performance and working-memory network function did not significantly differ between heavy cannabis users and controls. However, among heavy cannabis users, individual differences in working-memory network response had an independent effect on change in weekly cannabis use 6 months later (ΔR(2) = 0.11, P = 0.006, f(2) = 0.37) beyond baseline cannabis use (ΔR(2) = 0.41) and a behavioral measure of approach bias (ΔR(2) = 0.18): a stronger network response during the N-back task was related to an increase in weekly cannabis use. These findings imply that heavy cannabis users requiring greater effort to accurately complete an N-back working-memory task have a higher probability of escalating cannabis use. Working-memory network function may be a biomarker for the prediction of course and treatment outcome in cannabis users.

Keywords: N-back; cannabis; cannabis use disorder; fMRI; working-memory.

Figure 1

N‐back behavioral performance and network activation per memory load level in heavy cannabis users and controls. (A) 0‐back, 1‐back, and 2‐back average group accuracy expressed as proportion correct responses minus errors with standard deviation error bars. (B) 0‐back, 1‐back, and 2‐back median group RT of correct responses with standard deviation error bars. (C) Working‐memory network activity during 1‐back and 2‐back expressed as percent signal change of 0‐back with standard deviation error bars. *P < 0.05, **P < 0.001.

Figure 2

Spatial and temporal characteristics of the working‐memory network extracted by tensor‐ICA across groups. (A) Spatial characteristics. Significant clusters are overlaid on a standard MNI brain. Right side of the brain is depicted at right side. (B) Temporal characteristics. y‐Axis: normalized response, x‐axis: time (s), red line: network time‐course, black dotted line: task‐model time‐course. (C) Task‐model: order of 0‐back, 1‐back, and 2‐back blocks during N‐back task, x‐axis: time (s). [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 3

Direct relationships between weekly cannabis use at baseline, weekly cannabis use at 6‐month follow‐up, and working‐memory network function in heavy cannabis users (n = 30). (A) Association weekly cannabis at baseline and 6‐month follow‐up in gram per week, R ² = 0.42, p < 0.001. (B) Association working‐memory network response strength and weekly cannabis use at baseline, R ² = 0.02, P = 0.42. (C) Association working‐memory network response strength and weekly cannabis use at 6‐month follow‐up, R ² = 0.25, P = 0.005. (D) Association working‐memory network response strength and change in weekly cannabis use at 6‐month follow‐up (cannabis use follow‐up – cannabis use baseline), R ² = 0.29, P = 0.002. Response strength of the working‐memory network is expressed as the participant effect magnitude (arbitrary unit) derived from the Tensor‐ICA analysis.