Physics Commons^™

Automated, Efficient, And Accelerated Knowledge Modeling Of The Cognitive Neuroimaging Literature Using The Athena Toolkit, Michael C. Riedel, Taylor P. Salo, Jason Hays, Matthew D. Turner, Matthew T. Sutherland, Jessica A. Turner, Angela Laird

Department of Physics

Neuroimaging research is growing rapidly, providing expansive resources for synthesizing data. However, navigating these dense resources is complicated by the volume of research articles and variety of experimental designs implemented across studies. The advent of machine learning algorithms and text-mining techniques has advanced automated labeling of published articles in biomedical research to alleviate such obstacles. As of yet, a comprehensive examination of document features and classifier techniques for annotating neuroimaging articles has yet to be undertaken. Here, we evaluated which combination of corpus (abstract-only or full-article text), features (bag-of-words or Cognitive Atlas terms), and classifier (Bernoulli naïve Bayes, k-nearest …