Effective queries for mega-analysis in cognitive neuroscience

Functional neuroimaging investigates the neural correlates of performing cognitive tasks. The empirical evidence in this field is constantly growing and gave rise to methods for assessment and integration of the results across different studies. A promising and suitable technique is the so-called mega-analysis. Performing mega-analysis is, however, challenging. It is a multi-step process which connects a researcher's implicit reasoning about information processing in the brain with complex analysis of heterogenous data. Although the process of mega-analysis is well understood, it comprises many concepts and queries that lack a formal definition. Therefore, it is difficult to choose a suitable data model, design a data schema, and implement the relevant queries. A prerequisite for a successful mega-analysis is a set of studies conforming to a carefully defined experimental setting. Finding such datasets is, however, a laborious and error-prone task of keyword-based literature search. To aid understanding of the underlying issues, we propose a conceptual model of mega-analysis. The model integrates a researcher's implicit knowledge with a systematic definition of relevant data. The nature of the data suggests a graph data model for effectively querying datasets. Consequently, we define a knowledge graph integrating the data associated with experimental setting, formally define the queries over the knowledge graph, and showcase their implementation in a graph database.