Task-specific networks of the brain revealed by a meta-analysis of more than 1600 neuroimaging studies
Neuroinformatics refers to free sharing of analysis tools and experimental data. When neuroinformatics was taking its very first steps, there were, among extensive support, also voices of critic, mostly doubting the usefulness of making published neuroimaging datasets available, whether anyone could in practice utilize data that is most often collected to answer some highly specific research question. With gigantic advances in computational power, it has become possible to put together thousands of such datasets and look for consistent patterns in the resulting big data with sophisticated data analysis algorithms that have been adapted from, e.g., statistical physics. Recently, there have been studies combining data over a large number of resting-state imaging studies to inspect the brain as a complex network. Consistent patterns of functional connectivity (or rather “co-activation”, where a number of brain areas tend to change their level of activity hand-in-hand) have indeed been observed, but it has been less well known how active engagement in various types of tasks changes the “resting state” networks of co-activity.
In their recent study, Crossley et al. (2013) included in a meta-analysis data from more than 1600 functional magnetic resonance imaging and positron emission tomography studies published between 1985–2010 to inspect the network activity patterns of the human brain when experimental subjects are engaging in different types of tasks, including perception, action, executive tasks, and during emotions. Based on this meta-analysis, the authors were able to observe that there are large similarities in functional networks of the brain between resting state (where the task of the subjects typically has been to lay in the scanner and either do nothing or focus on a fixation cross) and active tasks, however, differences also emerged. It was observed that so-called occipital module was mostly activated during perception, central module during action, the default-mode module by emotions, and fronto-parietal module by executive tasks. Further, the authors observed that there were important nodes in parietal and prefrontal cortex that often connected over long distances and were involved in diverse range of tasks. Deactivation of nodes was also noted to play an important role in flexible network reconfiguration with changing cognitive demands. Overall, this study is a prime example of the usefulness of big data in cognitive neuroscience by allowing sophisticated analysis of the brain’s central processing principles that will likely pave way for further research efforts in a highly significant manner.
Reference: Crossley NA, Mechelli A, Vértes PE, Winton-Brown TT, Patel AX, Ginestet CE, McGuire P, Bullmore ET. Cognitive relevance of the community structure of the human brain functional coactivation network. Proc. Natl. Acad. Sci. USA (2013) e-publication ahead of print. http://dx.doi.org/10.1073/pnas.1220826110