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特质冲动的功能脑网络:全脑功能连接可预测自我报告的冲动性。

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

机构信息

Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Aachen, Germany.

JARA - Translational Brain Medicine, Aachen, Germany.

出版信息

Hum Brain Mapp. 2024 Oct 15;45(15):e70059. doi: 10.1002/hbm.70059.

Abstract

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.

摘要

鉴于冲动的多维性质及其对人类行为各个方面的影响,全面了解与这种特质相关的功能性大脑回路是必要的。在目前的研究中,我们利用了健康男性的全脑静息态功能连接数据(n=156),以识别一个能够预测个体冲动性的连接网络,该个体的冲动性是通过巴瑞特冲动量表(BIS-11)来评估的。我们的参与者是部分根据他们的 BIS-11 冲动性评分来选择的。具体来说,在筛选过程中报告高或低特质冲动性评分的个体首先被选择,然后是那些具有中间冲动性水平的个体。这使我们能够包括具有罕见、极端评分的参与者,并涵盖整个 BIS-11 冲动性谱。我们采用了重复的 K 折交叉验证进行特征选择,并使用分层的 10 折交叉验证来训练和测试我们的模型。我们的发现揭示了与特质冲动性相关的广泛的神经网络,以及预测和观察评分之间的显著相关性。特征重要性和节点度被评估,以突出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大脑区域,如小脑、脑干和颞叶,同时支持了以前关于基底神经节-丘脑-前额叶网络和前额叶-运动带网络与冲动性的研究结果。这种深入的理解为识别与功能失调的冲动性相关的功能性大脑网络的改变奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f8b/11519747/4dd0b62c033f/HBM-45-e70059-g003.jpg

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