Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function. This research has been pioneered by NeuroSynth, a database collecting both brain activation coordinates and associated text across a large cohort of neuroimaging research papers. One of the fundamental aspects of such meta-analysis is text-mining. To date, word counts and more sophisticated methods such as Latent Dirichlet Allocation have been proposed. In this work we present an unsupervised study of the NeuroSynth text corpus using Deep Boltzmann Machines (DBMs). The use of DBMs yields several advantages over the aforementioned methods, principal among which is the fact that it yields both word and document embeddings in a high-dimensional vector space. Such embeddings serve to facilitate the use of traditional machine learning techniques on the text corpus. The proposed DBM model is shown to learn embeddings with a clear semantic structure.
|Title of host publication
|PRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging
|Institute of Electrical and Electronics Engineers Inc.
|Published - 24 Aug 2016
|6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016 - Trento, Italy
Duration: 22 Jun 2016 → 24 Jun 2016
|6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016
|22/06/2016 → 24/06/2016
- Deep Boltzmann machines
- topic models