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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
注水萝卜完成签到 ,获得积分10
2秒前
Chem34完成签到,获得积分10
10秒前
12秒前
量子星尘发布了新的文献求助10
12秒前
hhh2018687完成签到,获得积分10
12秒前
12秒前
13秒前
13秒前
13秒前
13秒前
14秒前
14秒前
14秒前
14秒前
14秒前
14秒前
14秒前
嘒彼小星完成签到 ,获得积分10
14秒前
14秒前
15秒前
15秒前
15秒前
15秒前
16秒前
ri_290完成签到,获得积分10
16秒前
17秒前
nsc发布了新的文献求助30
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助10
19秒前
nsc发布了新的文献求助30
19秒前
nsc发布了新的文献求助10
19秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038039
求助须知:如何正确求助?哪些是违规求助? 3575756
关于积分的说明 11373782
捐赠科研通 3305574
什么是DOI,文献DOI怎么找? 1819239
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022