ABSTRACT
Previous approaches often use a considerable amount of curator work, with researchers reading all papers, and extracting by hand the relevant information (cf., Laird et al. (2009)). This severely limits the range of possible analyses. It is, therefore, of significant importancethat robust automated information retrieving approaches be added to the current attempts at building function neuroatlases. A recent, fully automated approach was proposed by Yarkoni et al. (2011). Their framework combines text-mining, meta-analysis and machine-learning techniques to generate probabilistic mappings between cognitive and neural states. One drawback of this method is that it addresses only text mining, and requires the presence of activation coordinates in the articles analysed. Those peak-coordinates and some text tags are the only representation of the activations, which results in the discarding of valuable information from the neural activity.
