Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea.
Partner Group of the Max Planck Institute for Human Cognitive and Brain Sciences at the Department of Brain and Cognitive Sciences, DGIST, Daegu 42988, Korea.
Cereb Cortex. 2021 Oct 22;31(12):5497-5510. doi: 10.1093/cercor/bhab174.
Recent advances in neuroimaging have augmented numerous findings in the human reasoning process but have yielded varying results. One possibility for this inconsistency is that reasoning is such an intricate cognitive process, involving attention, memory, executive functions, symbolic processing, and fluid intelligence, whereby various brain regions are inevitably implicated in orchestrating the process. Therefore, researchers have used meta-analyses for a better understanding of neural mechanisms of reasoning. However, previous meta-analysis techniques include weaknesses such as an inadequate representation of the cortical surface's highly folded geometry. Accordingly, we developed a new meta-analysis method called Bayesian meta-analysis of the cortical surface (BMACS). BMACS offers a fast, accurate, and accessible inference of the spatial patterns of cognitive processes from peak brain activations across studies by applying spatial point processes to the cortical surface. Using BMACS, we found that the common pattern of activations from inductive and deductive reasoning was colocalized with the multiple-demand system, indicating that reasoning is a high-level convergence of complex cognitive processes. We hope surface-based meta-analysis will be facilitated by BMACS, bringing more profound knowledge of various cognitive processes.
神经影像学的最新进展增加了人类推理过程中的许多发现,但结果却有所不同。造成这种不一致的一个可能原因是,推理是一种复杂的认知过程,涉及注意力、记忆、执行功能、符号处理和流体智力,因此,各种大脑区域不可避免地参与了这个过程的协调。因此,研究人员使用元分析来更好地理解推理的神经机制。然而,以前的元分析技术存在一些弱点,例如不能充分代表皮质表面高度折叠的几何形状。因此,我们开发了一种新的元分析方法,称为皮质表面的贝叶斯元分析(BMACS)。BMACS 通过将空间点过程应用于皮质表面,从研究中的大脑活动峰值快速、准确和可访问地推断认知过程的空间模式。使用 BMACS,我们发现归纳推理和演绎推理的共同激活模式与多需求系统重叠,表明推理是复杂认知过程的高级融合。我们希望 BMACS 能够促进基于表面的元分析,从而更深入地了解各种认知过程。