School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332, United States.
Department of Psychology, Stanford University, Stanford, CA 94305, United States.
Cereb Cortex. 2023 Nov 4;33(22):11092-11101. doi: 10.1093/cercor/bhad348.
Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points toward sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (n = 50) and find that activations related to task, reaction time, and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.
神经科学研究通常假设存在普遍的神经机制，但越来越多的证据表明，大脑活动存在相当大的个体差异。目前仍不清楚这种特殊性的程度，以及不同类型的分析是否与不同程度的特殊性相关。在这里，我们开发了一种新的方法来解决这些问题。该方法包括计算大脑活动的个体内可靠性和个体间相似性，并将这些值提交给一个计算模型，该模型量化了组水平和个体水平因素的相对强度。我们将这种方法应用于一项感知决策任务（n=50），并发现与任务、反应时间和信心相关的激活受到组水平和个体水平因素的同等强烈影响。尽管更高水平的平滑增加了它们相对于噪声的贡献，但组水平和个体水平因素都被噪声因素所掩盖。总的来说，我们的方法允许对大脑活动的组水平和个体水平因素进行量化，从而更详细地了解大脑活动的特殊性程度。
