Time-evolving dynamics in brain networks forecast responses to health messaging

Time-evolving dynamics in brain networks forecast responses to health messaging
复制标题

DOI:
10.1162/netn_a_00058
复制
发表时间:
2018-01-01
影响因子:
4.7
通讯作者:
Vettel, Jean M.
Vettel, Jean M.
中科院分区:
医学3区
文献类型:
--
作者:
Cooper, Nicole;Garcia, Javier O.;Vettel, Jean M.

文献摘要

被引文献

相似文献

神经成像方法已被用来预测复杂的行为,包括个体如何在有说服力的交流中改变对自己健康的决定,但很少纳入大脑网络动力学的指标。大脑网络内部和之间的功能动力学如何与说服和行为改变的过程相关?为了解决这个问题,我们使用功能磁共振成像对45名成年吸烟者进行了扫描,同时他们观看了反吸烟图像。参与者在扫描前和1个月后报告他们的吸烟行为和戒烟意图。我们重点关注了四个atlas定义的网络中的地区,并检查了它们在这项任务中是否形成了一致的网络社区(以忠诚度衡量)。那些在默认模式和额顶网络中表现出对区域忠诚度降低的吸烟者,在一个月后戒烟意图也表现出更大的增长。我们进一步研究了腹内侧额前皮质(VmPFC)的动力学,因为该区域的激活经常与行为变化有关。VmPFC随着时间的推移改变其社区分配的程度(以灵活性衡量)与吸烟减少呈正相关。这些数据突显了考虑大脑网络动力学对更广泛地理解消息有效性和社交过程的价值。
Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly.