For further details, please see Poldrack et al (2012). A meta-analysis is then performed to identify the neural correlates of each topic by searching for brain regions that are consistently more activated in articles that load highly on each topic than in articles that do not load highly on a topic. You can effectively think of a Neurosynth topic is a cluster of semantically-related words that tend to occur together in article abstracts. Instead of generating separate maps for, say, "working memory" and "cognitive control", a topic modeling approach might extract a single topic that assigns a large weight to each of these closely-related terms (as well as many others). Topics reflect an effort to move beyond individual term-based analyses by modeling the covariance between different terms in article abstracts. Topic-based meta-analyses: Frequently Asked Questions What is a "topic" in Neurosynth?
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