We will discuss this paper on Neuroquery, a relatively new web-based meta-analysis tool: https://elifesciences.org/articles/53385.pdf. This is different from Neurosynth in that it generates meta-analysis maps using predictive modeling from the string of text provided at the prompt, instead of performing inferential statistics to calculate the overlap of activation from different studies. This allows the user to generate predictive maps for more nuanced cognitive processes - especially for clinical populations which may be underrepresented in the literature compared to controls - and can be useful in generating predictions about where the activity will be for one's own study, and for creating ROIs.

Predictive modeling and dimensionality reduction of functional neuroimaging data have provided rich information about the representations and functional architectures of the human brain. While these approaches have been effective in many cases, we will discuss how neglecting the internal dynamics of the brain (e.g., spontaneous activity, global dynamics, effective connectivity) and its underlying computational principles may hinder our progress in understanding and modeling brain functions. By reexamining evidence from our previous and ongoing work, we will propose new hypotheses and directions for research that consider both internal dynamics and the computational principles that may govern brain processes.