Cauda Franco, Manuello Jordi, Crocetta Annachiara, Duca Sergio, Costa Tommaso, Liloia Donato
GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.
FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.
Brain Struct Funct. 2024 Dec 24;230(1):17. doi: 10.1007/s00429-024-02867-4.
Co-activation of distinct brain areas provides a valuable measure of functional interaction, or connectivity, between them. One well-validated way to investigate the co-activation patterns of a precise area is meta-analytic connectivity modeling (MACM), which performs a seed-based meta-analysis on task-based functional magnetic resonance imaging (task-fMRI) data. While MACM stands as a powerful automated tool for constructing robust models of whole-brain human functional connectivity, its inherent limitation lies in its inability to capture the distinct interrelationships among multiple brain regions. Consequently, the connectivity patterns highlighted through MACM capture the direct relationship of the seed region with third brain regions, but also a (less informative) residual relationship between the third regions themselves. As a consequence of this, this technique does not allow to evaluate to what extent the observed connectivity pattern is really associated with the fact that the seed region is activated, or it just reflects spurious co-activations unrelated with it. In order to overcome this methodological gap, we introduce a meta-analytic Bayesian-based method, called meta-analytic connectivity perturbation analysis (MACPA), that allows to identify the unique contribution of a seed region in shaping whole-brain connectivity. We validate our method by analyzing one of the most complex and dynamic structures of the human brain, the amygdala, indicating that MACPA may be especially useful for delineating region-wise co-activation networks.
不同脑区的共同激活为它们之间的功能相互作用或连接性提供了一种有价值的衡量方法。研究精确脑区共同激活模式的一种经过充分验证的方法是元分析连接性建模（MACM），它对基于任务的功能磁共振成像（任务fMRI）数据进行基于种子的元分析。虽然MACM是构建全脑人类功能连接性稳健模型的强大自动化工具，但其固有局限性在于无法捕捉多个脑区之间独特的相互关系。因此，通过MACM突出显示的连接模式不仅捕捉了种子区域与第三脑区的直接关系，还捕捉了第三区域之间（信息量较少的）残余关系。因此，这种技术无法评估观察到的连接模式在多大程度上真的与种子区域被激活这一事实相关，或者它只是反映了与种子区域无关的虚假共同激活。为了克服这一方法上的差距，我们引入了一种基于元分析贝叶斯的方法，称为元分析连接性扰动分析（MACPA），它能够识别种子区域在塑造全脑连接性方面的独特贡献。我们通过分析人类大脑中最复杂、最具动态性的结构之一——杏仁核，验证了我们的方法，这表明MACPA对于描绘区域特异性共同激活网络可能特别有用。
