Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.
about
The neurobiology of addiction: the perspective from magnetic resonance imaging present and future.Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder.Use of a machine learning framework to predict substance use disorder treatment success.Insular and cingulate attenuation during decision making is associated with future transition to stimulant use disorder.Is the Construct of Relapse Heuristic, and Does It Advance Alcohol Use Disorder Clinical Practice?Neural responses to negative outcomes predict success in community-based substance use treatment.Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root TraitsRat animal models for screening medications to treat alcohol use disorders.Selective activation of striatal NGF-TrkA/p75NTR/MAPK intracellular signaling in rats that show suppression of methamphetamine intake 30 days following drug abstinence.Reward-related neural dysfunction across depression and impulsivity: A dimensional approach.Commentary on Stewart et al. (2017): Stimulants and marijuana-the potential value in studying substance co-use.Working memory predicts methamphetamine hair concentration over the course of treatment: moderating effect of impulsivity and implications for dual-systems model.Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.Doubling down: increased risk-taking behavior following a loss by individuals with cocaine use disorder is associated with striatal and anterior cingulate dysfunction
P2860
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P2860
Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Individualized relapse predict ...... sing robustly predict relapse.
@ast
Individualized relapse predict ...... sing robustly predict relapse.
@en
type
label
Individualized relapse predict ...... sing robustly predict relapse.
@ast
Individualized relapse predict ...... sing robustly predict relapse.
@en
prefLabel
Individualized relapse predict ...... sing robustly predict relapse.
@ast
Individualized relapse predict ...... sing robustly predict relapse.
@en
P2860
P50
P1476
Individualized relapse predict ...... sing robustly predict relapse.
@en
P2093
Susan F Tapert
Tali M Ball
P2860
P304
P356
10.1016/J.DRUGALCDEP.2015.04.018
P577
2015-04-30T00:00:00Z