Treves Isaac N, Kucyi Aaron, Park Madelynn, Kral Tammi R A, Goldberg Simon B, Davidson Richard J, Rosenkranz Melissa, Whitfield-Gabrieli Susan, Gabrieli John D E
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Hum Brain Mapp. 2025 Jan;46(1):e70123. doi: 10.1002/hbm.70123.
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. To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome-based predictive modeling analysis in 367 meditation-naïve adults across three samples collected at different sites. 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. 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.
特质正念是指个体以非评判和接纳的方式关注当下体验的倾向或习性。特质正念与积极的心理健康结果密切相关，但其神经基础却知之甚少。先前的静息态功能磁共振成像（fMRI）研究已将特质正念与默认模式网络（DMN）、额顶叶网络（FPN）和突显网络的网络内及网络间连接性联系起来。然而，这些研究结果的普遍性如何，它们与特质正念的不同组成部分有何关系，以及其他网络和脑区可能如何参与其中尚不清楚。为了填补这些空白，我们开展了迄今为止最大规模的特质正念静息态fMRI研究，该研究包括对来自不同地点收集的三个样本中的367名未经过冥想训练的成年人进行基于预注册连接组的预测建模分析。在模型训练数据集中，我们未发现能预测整体特质正念的连接，但我们识别出了两个正念子量表（“有意识行动”和“不评判”）的神经模型。模型既包括正性网络（随着连接性增加而正向预测正念的成对连接集）也包括负性网络，后者呈现相反的关系。“有意识行动”和“不评判”的正性网络模型分别显示出涉及FPN和DMN的不同网络表征。负性网络模型在各子量表间有显著重叠，涉及全脑连接，其中躯体运动、视觉和DMN网络参与明显。只有负性网络能够泛化以预测样本外的子量表分数，而不能在两个测试数据集上都实现。两个模型的预测结果也与一个成熟的思维游荡连接组模型的预测结果呈负相关。我们基于特定的情感和认知方面，为特质正念的可泛化连接模型提供了初步的神经证据。然而，模型在所有地点和扫描仪上的泛化不完全、模型稳定性有限以及模型之间存在大量重叠，凸显了寻找正念方面可靠脑标记物的困难。
