1BrainPark, Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia.
2 Department of Psychiatry, University of Southampton, Southampton, United Kingdom.
J Behav Addict. 2024 Aug 14;13(3):823-840. doi: 10.1556/2006.2024.00041. Print 2024 Oct 4.
Cognitive control and reward-related abnormalities are centrally implicated in addiction. However, findings from longitudinal studies addressing neurocognitive predictors of addictive behaviors are mixed. Further, little work has been conducted predicting non-substance-related addictive behaviors. Our study aimed to assess predictors of substance and non-substance addictive behaviors in a community sample, systematically evaluating each neurocognitive function's independent influence on addictive behavior.
Australians (N = 294; 51.7% female; M[SD] age = 24.8[4.7] years) completed online neurocognitive tasks and surveys at baseline and 3-month follow-up. Self-report scales assessed problematic alcohol use, addictive eating (AE), problematic pornography use (PPU), and problematic internet use (PUI) at 3- and 6-month follow-ups. Linear regressions with bootstrapping assessed neurocognitive predictors for each addictive behavior across a 6-month period.
Neurocognition at baseline did not predict AE or PUI severity at 6-month follow-up. Less delay discounting at baseline predicted higher PPU at 6-month follow-up (β = -0.16, p = 0.005). Poorer performance monitoring at baseline predicted higher AE at 3-month follow-up (β = -0.16, p = 0.004), and more reward-related attentional capture at 3-months predicted higher AE at 6-month follow-up (β = 0.14, p = 0.033). Less reward-related attentional capture (β = -0.14, p = 0.003) and less risk-taking under ambiguity (β = -0.11, p = 0.029) at baseline predicted higher PUI at 3-month follow-up. All findings were of small effect size. None of the neurocognitive variables predicted problematic alcohol use.
We were unable to identify a core set of specific neurocognitive functions that reliably predict multiple addictive behavior types. However, our findings indicate both cognitive control and reward-related functions predict non-substance addictive behaviors in different ways. Findings suggest that there may be partially distinct neurocognitive mechanisms contributing to addiction depending on the specific addictive behavior.
认知控制和与奖励相关的异常在成瘾中起着核心作用。然而,关于神经认知预测成瘾行为的纵向研究结果存在差异。此外,很少有工作预测非物质相关的成瘾行为。我们的研究旨在评估社区样本中物质和非物质成瘾行为的预测因素,系统地评估每种神经认知功能对成瘾行为的独立影响。
澳大利亚人(N=294;51.7%为女性;M[SD]年龄=24.8[4.7]岁)在基线和 3 个月随访时完成了在线神经认知任务和调查。自我报告量表在 3 个月和 6 个月随访时评估了问题性饮酒、成瘾性饮食(AE)、问题性色情使用(PPU)和问题性互联网使用(PUI)。使用 bootstrap 进行线性回归,评估了 6 个月内每种成瘾行为的神经认知预测因素。
基线时的神经认知并不能预测 6 个月随访时的 AE 或 PUI 严重程度。基线时的延迟折扣越低,6 个月随访时的 PPU 越高(β=-0.16,p=0.005)。基线时的绩效监测较差,3 个月随访时的 AE 较高(β=-0.16,p=0.004),3 个月时的奖励相关注意力捕获较高,6 个月随访时的 AE 较高(β=0.14,p=0.033)。基线时的奖励相关注意力捕获减少(β=-0.14,p=0.003)和模糊条件下的风险承担减少(β=-0.11,p=0.029)预测 3 个月随访时的 PUI 较高。所有发现的效应量都较小。没有任何神经认知变量预测问题性饮酒。
我们无法确定一组特定的神经认知功能,这些功能能够可靠地预测多种成瘾行为类型。然而,我们的发现表明,认知控制和与奖励相关的功能以不同的方式预测非物质成瘾行为。研究结果表明,根据特定的成瘾行为,可能存在部分不同的神经认知机制导致成瘾。