■特征正念是指一个人的性格或倾向于关注他们在当下的经历,以一种非评判和接受的方式。特质正念与积极的心理健康结果密切相关,但它的神经基础却知之甚少。先前的静息状态功能磁共振成像研究已将特质正念与默认模式(DMN)的网络内和网络间连接相关联,额顶叶(FPN),和显著性网络。然而,目前还不清楚这些发现有多普遍,它们如何与特质正念的不同组成部分相关联,以及其他网络和大脑区域如何参与。
■为了解决这些差距,我们进行了迄今为止最大的静息状态功能磁共振成像研究,包括在不同地点收集的三个样本中对367名成年人进行的预注册连接体预测模型分析。
■在模型训练数据集中,我们没有找到预测整体特征正念的联系,但是我们确定了两个正念子量表的神经模型,有意识地行事,不评判。模型包括正网络(成对连接的集合积极预测正念的连接)和负网络,这显示了相反的关系。感知行为和非判断积极网络模型显示出涉及FPN和DMN的不同网络表示,分别。负网络模型,在各个分量表上明显重叠,涉及整个大脑的连接,并明显参与了躯体运动,视觉和DMN网络。只有在样本外推广预测子量表得分的负网络,而不是跨两个测试数据集。来自两个模型的预测也与建立良好的思维游走的连接体模型的预测呈负相关。
■我们提供了基于特定情感和认知方面的特质正念的可概括连接模型的初步神经证据。然而,模型在所有站点和扫描仪中的不完全概括,模型的稳定性有限,以及模型之间的大量重叠,强调了寻找健壮的正念方面的大脑标记的困难。
UNASSIGNED: Trait mindfulness refers to one\'s disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved.
UNASSIGNED: To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites.
UNASSIGNED: In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model.
UNASSIGNED: We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.