Inter-participant consistency of language-processing networks during abstract thoughts

Human brain imaging typically employs structured and controlled tasks to avoid variable and inconsistent activation patterns. Here we expand this assumption by showing that an extremely open-ended, high-level cognitive task of thinking about an abstract content, loosely defined as “abstract thinking” - leads to highly consistent activation maps. Specifically, we show that activation maps generated during such cognitive process were precisely located relative to borders of well-known networks such as internal speech, visual and motor imagery. The activation patterns allowed decoding the thought condition at >95%. Surprisingly, the activated networks remained the same regardless of changes in thought content. Finally, we found remarkably consistent activation maps across individuals engaged in abstract thinking. This activation bordered, but strictly avoided visual and motor networks. On the other hand, it overlapped with left lateralized language networks. Activation of the default mode network (DMN) during abstract thought was similar to DMN activation during rest. These observations were supported by a quantitative neuronal distance metric analysis. Our results reveal that despite its high level, and varied content nature - abstract thinking activates surprisingly precise and consistent networks in participants’ brains.