Bickel Warren K, Moody Lara N, Eddy Celia R, Franck Christopher T
Addiction Recovery Research Center, Virginia Tech Carilion Research Institute.
Department of Statistics, Virginia Tech.
Exp Clin Psychopharmacol. 2017 Aug;25(4):322-332. doi: 10.1037/pha0000115.
Neurocognitive dysfunctions are frequently identified in the addictions. These dysfunctions may indicate either diffuse dysfunction or may represent separate facets that have differential importance to the addiction phenotype. In a sample (n = 260) of alcohol and/or stimulant users and controls we measured responses across 7 diverse neurocognitive measures. These measures were Continuous Performance, Delay Discounting, Iowa Gambling, Stroop, Tower, Wisconsin Card Sorting, and Letter Number Sequencing. Comparisons were then made between the drug-dependent groups and controls using analysis of variance and also using a machine learning approach to classify participants based on task performance as substance-dependent or controls in 1 tree and as alcohol and/or stimulant users or controls in a second tree. The analysis of variance showed significant differences between groups on the Delay Discounting (p < .001), Iowa Gambling (p < .001), Letter Number Sequencing (p < .001), and Wisconsin Card Sorting (p < .05) tasks. The first classification tree correctly classified between substance-dependent or controls for 88.3% of participants and classified between alcohol and/or stimulant users or controls for 63.9% of participants. Delay discounting was the first split in both trees and in the substance-dependent and control tree. The analysis of variance results largely replicate previous findings. The machine learning classification tree analysis provides evidence to support the hypothesis that different measures of neurocognitive dysfunction represent different processes. Among them, delay discounting was the most robust in categorizing drug dependence. (PsycINFO Database Record
神经认知功能障碍在成瘾行为中经常被发现。这些功能障碍可能表明存在弥漫性功能障碍，或者可能代表对成瘾表型具有不同重要性的不同方面。在一个包含酒精和/或兴奋剂使用者及对照组的样本（n = 260）中，我们测量了参与者在7种不同神经认知测试中的反应。这些测试包括连续作业测试、延迟折扣、爱荷华赌博任务、斯特鲁普测试、河内塔测试、威斯康星卡片分类测试和字母数字序列测试。然后，使用方差分析以及机器学习方法，根据任务表现将参与者分类为药物依赖者或对照组（在一棵树中），以及酒精和/或兴奋剂使用者或对照组（在第二棵树中），对药物依赖组和对照组进行比较。方差分析显示，在延迟折扣任务（p < .001）、爱荷华赌博任务（p < .001）、字母数字序列测试（p < .001）和威斯康星卡片分类测试（p < .05）中，各组之间存在显著差异。第一棵分类树对88.3%的参与者正确分类为药物依赖者或对照组，对63.9%的参与者正确分类为酒精和/或兴奋剂使用者或对照组。延迟折扣是两棵树以及药物依赖者和对照组树中的第一个划分点。方差分析结果在很大程度上重复了先前的研究发现。机器学习分类树分析提供了证据，支持不同的神经认知功能障碍测量方法代表不同过程的假设。其中，延迟折扣在对药物依赖进行分类方面最为稳健。（《心理学文摘数据库记录》 ）
