心理学
前额叶腹内侧皮质
静息状态功能磁共振成像
背外侧前额叶皮质
额下回
特质
扣带回前部
构造(python库)
功能连接
默认模式网络
认知心理学
额内侧回
功能磁共振成像
意识的神经相关物
显著性(神经科学)
前额叶皮质
神经科学
认知
计算机科学
程序设计语言
作者
Zhiting Ren,Jiangzhou Sun,Cheng Liu,Xinyue Li,Xianrui Li,Xinyi Li,Zeqing Liu,Taiyong Bi,Jiang Qiu
摘要
Abstract Self‐control is a core psychological construct for human beings and it plays a crucial role in the adaptation to society and achievement of success and happiness for individuals. Although progress has been made in behavioral studies examining self‐control, its neural mechanisms remain unclear. In this study, we employed a machine‐learning approach—relevance vector regression (RVR) to explore the potential predictive power of intrinsic functional connections to trait self‐control in a large sample ( N = 390). We used resting‐state functional MRI (fMRI) to explore whole‐brain functional connectivity patterns characteristic of 390 healthy adults and to confirm the effectiveness of RVR in predicting individual trait self‐control scores. A set of connections across multiple neural networks that significantly predicted individual differences were identified, including the classic control network (e.g., fronto‐parietal network (FPN), salience network (SAL)), the sensorimotor network (Mot), and the medial frontal network (MF). Key nodes that contributed to the predictive model included the dorsolateral prefrontal cortex (dlPFC), middle frontal gyrus (MFG), anterior cingulate and paracingulate gyri, inferior temporal gyrus (ITG) that have been associated with trait self‐control. Our findings further assert that self‐control is a multidimensional construct rooted in the interactions between multiple neural networks.
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