fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.

功能磁共振成像作为一种转化工具具有巨大的潜力，可用于了解风险、确定干预措施的优先顺序和改善脑部疾病的治疗。然而，最近的研究发现，许多最广泛使用的功能磁共振成像测量方法的可靠性较低，削弱了这种潜力。在这里，我们认为许多功能磁共振成像测量方法是不可靠的，因为它们旨在识别群体效应，而不是精确量化个体差异。然后，我们重点介绍四种新兴策略 [扩展聚合、可靠性建模、多回波 fMRI (ME-fMRI) 和刺激设计]，这些策略建立在已建立的心理测量特性的基础上，以生成更精确和可靠的 fMRI 测量。通过采用此类策略来提高可靠性，我们乐观地认为功能磁共振成像可以发挥其作为临床工具的潜力。