Bottenhorn Katherine L, Flannery Jessica S, Boeving Emily R, Riedel Michael C, Eickhoff Simon B, Sutherland Matthew T, Laird Angela R
Department of Psychology, Florida International University, Miami, FL, USA.
Department of Physics, Florida International University, Miami, FL, USA.
Netw Neurosci. 2018 Oct 1;3(1):27-48. doi: 10.1162/netn_a_00050. eCollection 2019.
Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses -means clustering to identify core brain networks engaged across the naturalistic functional neuroimaging literature. Functional decoding allowed us to, then, delineate how information is distributed between these networks throughout the execution of dynamical cognition in realistic settings. This analysis revealed six recurrent patterns of brain activation, representing sensory, domain-specific, and attentional neural networks that support the cognitive demands of naturalistic paradigms. Although gaps in the literature remain, these results suggest that naturalistic fMRI paradigms recruit a common set of networks that allow both separate processing of different streams of information and integration of relevant information to enable flexible cognition and complex behavior.
认知过程并非通过单纯的嵌入而发生,相反,它依赖于同时出现的全部心理过程,包括感知和运动功能。因此,对于使用高度受控任务来分离感兴趣的心理过程的人类神经成像实验而言,其生态效度是有限的。然而,越来越多的文献表明,动态的交互式任务如何使研究人员能够研究更自然发生的认知。对此类神经成像实验进行的综合分析,可能会回答有关自然主义认知如何在大脑中进行生物学分布的更广泛问题。我们应用了一种无偏倚的、数据驱动的元分析方法,该方法使用均值聚类来识别自然主义功能神经成像文献中涉及的核心脑网络。功能解码使我们能够进而描绘出在现实环境中动态认知执行过程中信息如何在这些网络之间分布。该分析揭示了六种反复出现的大脑激活模式,代表了支持自然主义范式认知需求的感觉、特定领域和注意力神经网络。尽管文献中仍存在空白,但这些结果表明,自然主义功能磁共振成像范式会调用一组共同的网络,这些网络既允许对不同信息流进行单独处理,又能整合相关信息以实现灵活的认知和复杂行为。