Computational Analysis of Research in Mammalian Neocortical Neurogenesis

Studies in neocortical neurogenesis have experienced an explosive growth since the early 2000s, measured by the increasing number of publications each year. I examine here the influence of Arnold Kriegstein in the field using Topic Modeling, a set of algorithms that can be applied to a collection of texts to elucidate the central themes of said collection. Using a Java-based software called MALLET, I obtained data for his corpus, and compared it to the texts of other researchers in the field. This latter collection, which I dub "General Corpus", was separated by year from 2000 to 2014. I found that Kriegstein's most frequently discussed topic concerned highly unique terms such as GABA, glutamate, and receptor, which did not appear in any of the primary topics of the General Corpus. This was in contrast to my initial hypothesis that Kriegstein's importance stemmed from his examination of different phenomena that constitute the broader aspect of neocortical neurogenesis. I predicted that the terms in Kriegstein's primary topic would appear many times throughout the topics of the General Corpus, but it was not so, aside from the common ones such as neurons, cortical, and development. Taken in tandem with NIH Reporter data, these results suggest that Kriegstein obtains a large amount of research funding because his studies concern unique topics when compared to others in the field. The implications of these findings are especially relevant in a world where funding is becoming increasingly difficult to come by.