An Image-Enhanced Topic Modeling Method for Neuroimaging Literature.

Topic modeling based on neuroimaging literature is an important approach to aggregate world-wide research findings for decoding brain cognitive mechanism, as well as diagnosis and treatment of brain and mental diseases, artificial intelligence researches, etc. However, existing neuroimaging literature mining only focused on texts and neglects brain images which contain a large amount of topic information. Following the writing and reading habits combining images with texts, we present in this paper an image-enhanced LDA (Latent Dirichlet Allocation), which extracts literature topics from both neuroimaging images and full texts. Combining topics from fMRI brain regions activation images with topics from full texts to model neuroimaging literatures more accurately. On the one hand, topics related brain cognitive mechanism can be pertinently extracted from activated brain images and their descriptions. On the other hand, topics from activated brain images can be integrated with topics from full text to model neuroimaging literature more accurately. The experiments based on actual data has preliminarily proved effectiveness of proposed method.