Multivariate neural signatures for health neuroscience: Assessing spontaneous regulation during food choice

Establishing links between neural systems and health can be challenging since there isn’t a one-to-one mapping between brain regions and psychological states. Building sensitive and specific predictive models of health-relevant constructs using multivariate activation patterns of brain activation is a promising new direction. We illustrate the potential of this approach by building two “neural signatures” of food craving regulation using multivariate machine learning and, for comparison, a univariate contrast. We applied the signatures to two large validation samples of overweight adults who completed tasks measuring craving regulation ability and valuation during food choice. Across these samples, the machine learning signature was more reliable. This signature decoded craving regulation from food viewing and higher signature expression was associated with less craving. During food choice, expression of the regulation signature was stronger for unhealthy foods and inversely related to subjective value, indicating that participants engaged in craving regulation despite never being instructed to control their cravings. Neural signatures thus have the potential to measure spontaneous engagement of mental processes in the absence of explicit instruction, affording greater ecological validity. We close by discussing the opportunities and challenges of this approach, emphasizing what machine learning tools bring to the field of health neuroscience.

构建神经系统和健康之间的联系颇具挑战，因为大脑区域与心理状态之间并不存在一一对应的关系。利用脑激活的多变量激活模式构建对健康相关构念的敏感且特异的预测模型，开辟了崭新的研究方向。本文通过构建两种“神经特征”来展示该方法的潜力，一种采用多元机器学习，另一种则采用单变量对比。我们将这些特征应用于两个包含大量验证样本的肥胖成人群体，这些样本完成了衡量食物选择中渴望调节能力和评估的任务。在这些样本中，机器学习特征表现出更高的可靠性。该特征能够从食物观看中解码渴望调节，且特征表达水平越高，渴望程度越低。在食物选择过程中，调节特征的表达对于不健康食物更为强烈，且与主观价值呈负相关，表明尽管参与者未曾被要求控制其渴望，但他们仍然参与了渴望调节。因此，神经特征具有测量在没有明确指令的情况下自发参与心理过程的潜力，从而提供了更高的生态效度。最后，本文讨论了该方法的机遇与挑战，强调了机器学习工具为健康神经科学领域带来的贡献。