Herman Aleksandra M, Critchley Hugo D, Duka Theodora
Department of Psychology, Royal Holloway, University of London, Egham, United Kingdom.
Behavioural and Clinical Neuroscience, University of Sussex, Brighton, United Kingdom.
Front Behav Neurosci. 2020 Jun 24;14:111. doi: 10.3389/fnbeh.2020.00111. eCollection 2020.
Knowledge of brain mechanisms underlying self-regulation can provide valuable insights into how people regulate their thoughts, behaviors, and emotional states, and what happens when such regulation fails. Self-regulation is supported by coordinated interactions of brain systems. Hence, behavioral dysregulation, and its expression as impulsivity, can be usefully characterized using functional connectivity methodologies applied to resting brain networks. The current study tested whether individual differences in trait impulsivity are reflected in the functional architecture within and between resting-state brain networks. Thirty healthy individuals completed a self-report measure of trait impulsivity and underwent resting-state functional magnetic resonance imaging. Using Probabilistic Independent Components Analysis in FSL MELODIC, we identified across participants 10 networks of regions (resting-state networks) with temporally correlated time courses. We then explored how individual expression of these spatial networks covaried with trait impulsivity. Across participants, we observed that greater self-reported impulsivity was associated with decreased connectivity of the right lateral occipital cortex (peak mm 46/-70/16, FWE 1- = 0.981) with the somatomotor network. No supratheshold differences were observed in between-network connectivity. Our findings implicate the somatomotor network, and its interaction with sensory cortices, in the control of (self-reported) impulsivity. The observed "decoupling" may compromise effective integration of early perceptual information (from visual and somatosensory cortices) with behavioral control programs, potentially resulting in negative consequences.
对自我调节背后的大脑机制的了解,可以为人们如何调节自己的思想、行为和情绪状态,以及当这种调节失败时会发生什么提供有价值的见解。自我调节由大脑系统的协调相互作用支持。因此,行为失调及其表现为冲动性,可以通过应用于静息脑网络的功能连接方法进行有效表征。本研究测试了特质冲动性的个体差异是否反映在静息状态脑网络内部和之间的功能结构中。30名健康个体完成了一项特质冲动性的自我报告测量,并接受了静息状态功能磁共振成像。使用FSL MELODIC中的概率独立成分分析,我们在参与者中识别出10个具有时间相关时间进程的区域网络(静息状态网络)。然后,我们探讨了这些空间网络的个体表达如何与特质冲动性协变。在参与者中,我们观察到自我报告的冲动性越高,右侧枕叶外侧皮质(峰值毫米46/-70/16,FWE 1- = 0.981)与躯体运动网络的连接性越低。在网络间连接性方面未观察到超阈值差异。我们的研究结果表明,躯体运动网络及其与感觉皮层的相互作用在(自我报告的)冲动性控制中起作用。观察到的“去耦合”可能会损害早期感知信息(来自视觉和躯体感觉皮层)与行为控制程序的有效整合,可能导致负面后果。