A machine learning‐derived neuroanatomical pattern predicts delayed reward discounting in the Human Connectome Project Young Adult sample

人类连接体项目 连接体 心理学 显著性(神经科学) 神经科学 单变量 默认模式网络 人脑 冲动性 脑形态计量学 认知 发展心理学 多元统计 磁共振成像 机器学习 计算机科学 功能连接 医学 放射科
作者
Hui Xu,James MacKillop,Max M. Owens
出处
期刊:Journal of Neuroscience Research [Wiley]
卷期号:101 (7): 1125-1137 被引量:2
标识
DOI:10.1002/jnr.25185
摘要

Delayed reward discounting (DRD) is defined as the extent to which person favors smaller rewards that are immediately available over larger rewards available in the future. Higher levels of DRD have been identified in individuals with a wide range of clinical disorders. Although there have been studies adopting larger samples and using only gray matter volume to characterize the neuroanatomical correlates of DRD, it is still unclear whether previously identified relationships are generalizable (out-of-sample) and how cortical thickness and cortical surface area contribute to DRD. In this study, using the Human Connectome Project Young Adult dataset (N = 1038), a machine learning cross-validated elastic net regression approach was used to characterize the neuroanatomical pattern of structural magnetic resonance imaging variables associated with DRD. The results revealed a multi-region neuroanatomical pattern predicted DRD and this was robust in a held-out test set (morphometry-only R2 = 3.34%, morphometry + demographics R2 = 6.96%). The neuroanatomical pattern included regions implicated in the default mode network, executive control network, and salience network. The relationship of these regions with DRD was further supported by univariate linear mixed effects modeling results, in which many of the regions identified as part of this pattern showed significant univariate associations with DRD. Taken together, these findings provide evidence that a machine learning-derived neuroanatomical pattern encompassing various theoretically relevant brain networks produces robustly predicts DRD in a large sample of healthy young adults.
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