特质冲动的大脑功能网络:全脑功能连接性预测自述冲动性

IF 3.5 2区 医学 Q1 NEUROIMAGING
Philippa Hüpen, Himanshu Kumar, Dario Müller, Ramakrishnan Swaminathan, Ute Habel, Carmen Weidler
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引用次数: 0

摘要

鉴于冲动的多维性及其对人类行为各个方面的影响,我们有必要全面了解与这一特征相关的大脑功能回路。在目前的研究中,我们利用健康男性(n = 156)的全脑静止态功能连接数据来识别可预测个体冲动性的连接网络,该网络由巴拉特冲动量表(BIS)-11 评估。我们选择参与者的部分依据是他们自我报告的 BIS-11 冲动性得分。具体来说,首先选择在筛查中报告特质冲动性得分较高或较低的人,然后再选择冲动性处于中等水平的人。这样,我们就能将罕见的极端分数参与者包括进来,并涵盖整个 BIS-11 冲动性谱。我们采用重复 K 倍交叉验证来选择特征,并使用分层 10 倍交叉验证来训练和测试我们的模型。我们的研究结果表明,一个广泛的神经网络与特质冲动有关,并且预测分数和观察分数之间存在显著的相关性。对特征重要性和节点度的评估突出了冲动性网络中的特定节点和边缘,揭示了以前被忽视的关键脑区,如小脑、脑干和颞叶,同时支持了以前关于基底节-丘脑-前额叶网络和前额叶-运动带网络与冲动性关系的研究结果。这一认识的深化为确定与功能性冲动有关的大脑功能网络的改变奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Functional Brain Network of Trait Impulsivity: Whole-Brain Functional Connectivity Predicts Self-Reported Impulsivity

Functional Brain Network of Trait Impulsivity: Whole-Brain Functional Connectivity Predicts Self-Reported Impulsivity

Given impulsivity's multidimensional nature and its implications across various aspects of human behavior, a comprehensive understanding of functional brain circuits associated with this trait is warranted. In the current study, we utilized whole-brain resting-state functional connectivity data of healthy males (n = 156) to identify a network of connections predictive of an individual's impulsivity, as assessed by the Barratt Impulsiveness Scale (BIS)-11. Our participants were selected, in part, based on their self-reported BIS-11 impulsivity scores. Specifically, individuals who reported high or low trait impulsivity scores during screening were selected first, followed by those with intermediate impulsivity levels. This enabled us to include participants with rare, extreme scores and to cover the entire BIS-11 impulsivity spectrum. We employed repeated K-fold cross-validation for feature-selection and used stratified 10-fold cross-validation to train and test our models. Our findings revealed a widespread neural network associated with trait impulsivity and a notable correlation between predicted and observed scores. Feature importance and node degree were assessed to highlight specific nodes and edges within the impulsivity network, revealing previously overlooked key brain regions, such as the cerebellum, brainstem, and temporal lobe, while supporting previous findings on the basal ganglia-thalamo-prefrontal network and the prefrontal-motor strip network in relation to impulsiveness. This deepened understanding establishes a foundation for identifying alterations in functional brain networks associated with dysfunctional impulsivity.

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来源期刊
Human Brain Mapping
Human Brain Mapping 医学-核医学
CiteScore
8.30
自引率
6.20%
发文量
401
审稿时长
3-6 weeks
期刊介绍: Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged. Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.
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