A Topic Learning Pipeline for Curating Brain Cognitive Researches

Abstract

Cognition is the most basic but complex process of human beings. Benefit from noninvasive neuroimaging technologies, a series of important brain projects have been carried out to model cognition from different aspects and levels. Because modeling such a complex phenomenon requires characterizations of numerous entities and cannot only depend on the efforts of one or more laboratories within a project cycle, a lot of neuroimaging text mining researches have focused on curating neuroimaging-based brain cognitive raw data, derived data and result data, to collect multi-aspect information about brain cognitive researches for comprehensively and objectively characterizing key entities of brain cognition. However, the data-centric perspective leads to the shortcomings of poor topic semantics and topic independent results. This paper proposes a brand-new perspective of big data sharing in neuroimaging, that is, curating brain cognitive researches. A new task definition of neuroimaging text mining and a topic learning pipeline integrating the heterogeneous deep neural networks and density clustering of topic relations are designed to realize this new perspective. The experimental results on actual data sets show that the proposed method can obtain more accurate and complete research topics for effectively characterizing brain cognition and its researches.