Characterizing functional fingerprint patterns of cortical areas based on thousands of datasets in BrainMap database
When browsing through cognitive neuroimaging literature, what often puzzles the beginner (and sometimes also the more advanced) cognitive neuroscientist is the relatively large number of different types of tasks and stimuli that are associated with hemodynamic/neural activity of a given cortical region. This apparent multidimensionality of functional anatomy raises the question of which cognitive functions a given brain area is most pivotal for. Databases storing neuroimaging data and results makes it increasingly possible to inspect the role(s) that a given cortical area plays in perceptual and cognitive functions over a vast number of studies, subjects, and stimulus/task conditions.
In their recent study, Anderson et al. (2013) combined the results of more than 2000 neuroimaging studies stored in the BrainMap database. They then constructed sensitivity profiles of brain regions based on 20 classes of task domains (e.g., execution-action, inhibition-action, happiness-emotion, vision-perception) associated with activity of each structure, as well as with activity of various brain networks (e.g., default mode network). The authors observed that degree of diversity varied considerably across the brain. Similarly, some brain networks consisted of relatively functionally similar areas, whereas other networks consisted of functionally more variable areas.
The study by Anderson et al. (2013) not only demonstrates the usefulness of databases, but also provides important insights into the fingerprint patterns / functional specialization exhibited by cortical areas. The authors quite correctly point out that inferring functional specialization based on frequency of occurrence in databases are potentially subject to so-called confirmation bias present in literature; results showing activation of amygdala in emotion studies are more probable to get published than for example visual cortex activation during music processing, as the latter finding is a less intuitively obvious. Keeping these limitations pointed out by the authors in mind, the possibilities offered by the vast amounts of data stored in databases such as the BrainMap are offering wonderful opportunities for cognitive neuroscientists, and the study by Anderson et al. (2013) very nicely shows how the heterogeneity of cortical areas and functional networks can be characterized based on analysis of huge amounts of data available in a database.