Upon reaching the Results, there were some clearly interesting findings, but many of the interpretations were dependent upon a reverse inference from the brain regions activated (Poldrack RA, TiCS 2006), as opposed to an inference based on the task manipulation.
But when trying to deduce the presence of a mental process M from a specific pattern of brain activity A, frequently referred to as reverse inference, one has to consider several issues, regardless of the nature of the mental process.
Interpretation of a functional neuroimaging study deals with select topics such as reverse inference and the ethical implications of study interpretation.
Such a discussion necessarily entails "reverse inference" of a sort often considered problematic in the field of neuroimaging [51].
However, we would like to re-iterate that the current segregation between functional brain modules is hypothetical (reverse inference), as it relies on previous neuroimaging research for linking brain areas to function.
Although decoding gives a principled methodological framework for reverse inference, there are some tough statistical challenges.
Despite these limitations, reverse inference is nevertheless a powerful heuristic tool when it comes to generate testable hypotheses about the nature of mental processes and their relationships.
However, reverse inference usually takes place when A was unexpected (i.e. not supposed to be engaged by the experimental task).
Indeed, some researchers have argued that reverse inference based on brain imaging results should be used merely as a guide to direct further enquiry rather than a direct means to interpret results (Poldrack, 2008).
A complementary set of approaches are non-invasive, using reverse inference methods to extract mechanical properties from cell geometries [ 14– 16].
