The brain as a crystal ball: the predictive potential of default mode network.

Abstract

The Danish Nobel Prize Niels Bohr once stated that “making a prediction is very difficult, especially about the future” (Ellis, 1970). The exact and original source of this quotation is still a matter of debate: someone argues that it is an old Danish proverb that Bohr was fond of quoting, others think that this is probably a sentence pronounced by a member of the Danish parliament or perhaps first said by the Danish cartoonist Robert Storm Petersen. Something is rotten in this Danish quote; however, it could be reasonable to agree with the content of the quote itself. Making predictions, one of the basic desires of mankind, is very difficult. Predictions are pillars of scientific method, and, from an epistemological point of view, the holy grail of every scientists should be designing a theory capable of generate predictions on all the possible consequences deriving from the theory itself, with each of them being testable and potentially falsifiable (Popper, 1959). More realistically, some researchers succeed in making, at least, some working hypotheses. However, something is changing. We currently know that when an individual is awake and alert and not actively engaged in attention-demanding tasks, organized neural activity is present in a set of brain regions called default mode network (DMN; Raichle et al., 2001). This network involves densely interconnected pivotal structures, such as the posterior cingulate cortex (PCC), the precuneus, and parts of the ventromedial prefrontal cortex (vmPFC), and its activity is suspended during specific goal-directed behaviors (Sandrone and Bacigaluppi, 2012). DMN activity have been investigated across the whole range of life cycle, from the emergence in 2-day-old newborn (Gao et al., 2009) to its disappearance in dead brain patients (Boly et al., 2009), through its evolution in both healthy and pathological conditions, where DMN show inter-individual differences, abnormalities, and disruptions (for a review see Power et al., 2010). Interestingly, recent evidence on these DMN differences, abnormalities, and disruptions in both connectivity and activity coming from different neuroscientific domains can account for an emerging concept that we named the “predictive value” or “predictive potential” of DMN (Sandrone and Bacigaluppi, 2012). In fact, it is seems that DMN can be used as predictive behavioral markers and as clinical diagnostic tools, thus making the brain a sort of crystal ball. These predictions are highly heterogeneous and the current evidence are scattered between several neuroscientific domains: they range from neurological to psychiatric conditions, and mix up behavioral phenotypes and neuropsychological evidence, in an intriguing parallelism between connectivity and activity.