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年轻人概率奖励学习的皮质厚度相关性。

Cortical thickness correlates of probabilistic reward learning in young adults.

机构信息

Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1L8, Canada.

Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1L8, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada.

出版信息

Biol Psychol. 2020 Nov;157:107975. doi: 10.1016/j.biopsycho.2020.107975. Epub 2020 Oct 21.

DOI:10.1016/j.biopsycho.2020.107975
PMID:33096148
Abstract

While impairments in reward learning have been observed in depression, individual differences also exist among healthy normative populations and these differences have been parametrically mapped onto naturally occurring inter-individual variability in neuroanatomy. To date, alterations of cortical thickness as a function of reward learning have not been studied. Structural MRI and behaviourally assessedrobabilistic reward learning data from 103 participants (n = 35, n = 68, age = 19.0 ± 1.0 years) from the Duke Neurogenetics Study were used in our analysis. The relationship between reward learning and vertex-wise cortical thickness was tested with a multiple linear regression including gender, age, and reward task stimulus discriminability as nuisance variables. Reward learning was significantly, positively associated with cortical thickness in the right medial superior frontal cortex (t = 4.469, p = .048). This is the first study exploring the relationship between cortical thickness and probabilistic reward learning. It implicates a novel region in reward learning and can thereby inform future mechanistic studies.

摘要

虽然在抑郁症中观察到了奖励学习方面的障碍,但在健康的正态人群中也存在个体差异,这些差异已经被参数化映射到神经解剖学中自然发生的个体间变异性上。迄今为止,还没有研究过奖励学习作为大脑皮层厚度变化的功能。我们的分析使用了来自杜克神经遗传学研究的 103 名参与者(n = 35,n = 68,年龄 = 19.0 ± 1.0 岁)的结构 MRI 和行为评估的概率奖励学习数据。使用包括性别、年龄和奖励任务刺激可辨别性在内的多个线性回归来检验奖励学习与顶点大脑皮层厚度之间的关系,将这些变量作为混杂变量。奖励学习与右侧内侧额上回的大脑皮层厚度呈显著正相关(t = 4.469,p =.048)。这是第一项探索大脑皮层厚度与概率奖励学习之间关系的研究。它涉及到奖励学习的一个新区域,从而为未来的机制研究提供信息。

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