Human brain is a dynamic complex system with large-scale ongoing fluctuations. Understanding those dynamic features is essential to our understanding of functional anatomy–and the pathologies associated with neuropsychiatric conditions. The key aspect of these dynamics is their complexity with higher complexity indicating a greater capacity for processing both internal or external inputs, which might be a requirement for the brain to adapt to the dynamically changing environment, characterized by a high occurrence of unpredictable events. Complexity has been addressed in many ways since the advent of the whole brain imaging1,2,3,4,5,6. The two main approaches are based upon information theory–using measures like entropy and mutual information1,4 – and dynamical system theory to address emergent phenomena – like self organized criticality and metastability5,6,7.

The first studies of brain complexity (based on fMRI) used information theory1,2: Because complex patterns of activity in the brain are intermediate between a state of incoherence, with regionally specific dynamics and a state of global coherence, greatest complexity is found somewhere between high-dimensional, chaotic behaviour and low-dimensional, orderly behaviour. This means that complexity is high when small regions have (on average) relatively high entropy with respect to the entropy of the whole system2. This measure is equivalent to the (average) mutual information between all small regions and the rest of the system in question and was shown to be higher than chance in fMRI signals acquired from the brain2. In effect, this means that long range spatial correlations are greater than would be predicted by short-range correlations.

The overall picture of a complex regime for neuronal dynamics–that lies somewhere between a low entropy coherent regime (such as coma or slow wave sleep) and a high entropy chaotic regime–also emerges from simulations of neuronal dynamics. For example, Deco and Jirsa6 used simulations to show that realistic dynamics–on realistic anatomical connections–show a characteristic metastability, leading to critical behaviours. This is consistent with the literature on self organized criticality in neuronal populations (e.g.5). A crucial aspect of complex dynamics–and in particular criticality–is the emergence of long range temporal correlations. This complements the information theory based characterizations of complexity above–that focused on the emergence of long range spatial correlations.

The potential importance of long range temporal correlations is highlighted by the recent focus on slow fluctuations in fMRI timeseries and the intrinsic spatial modes that they define–for example the default mode8. However, measuring the temporal complexity of fMRI timeseries in a robust and assumption free manner is a nontrivial issue. We have recently proposed a method9 to map the whole brain temporal complexity using a nonparametric entropy metric, the Sample Entropy10,11. This measure is based upon the entropy of measured haemodynamic states that considers dependency over time using temporal embedding. In other words, this use of entropy reflects the statistical dependencies or order implicit in itinerant dynamics, expressed over extended periods of time.

Using the brain entropy (BEN) mapping tool, we have demonstrated that regional BEN can be reliably mapped in the normal brain using resting state fMRI (rsfMRI)9. Normal brain presents structurally and functionally meaningful BEN distribution patterns at rest9. In subsequent studies, we showed BEN alterations in different brain disorders such as multiple sclerosis12, chronic cigarette smoking13, and cocaine addiction14, in complimentary to BEN alterations in aging15, Schizophrenia16, and attention deficit hyperactivity disorder (ADHD)17 reported by other groups. Altogether, these studies suggest BEN measured with rsfMRI as a reliable index of regional temporal brain dynamics, which is sensitive to regional functional modulations such as task activation and disease conditions. An important question to be answered next is whether BEN is sensitive to pharmaceutical modulations.

The purpose of this study was to examine sensitivity of BEN to caffeine, the most widely consumed psychostimulant18,19,20. Caffeine is an antagonist to adenosine20, a neuromodulator that reduces neural activity via binding to adenosine receptors, mainly the A1 and A2a receptors. Suppressing the potency of adenosine through the antagonistic binding leads to an increase of neural activity, which may eventually cause the various positive effects of caffeine on brain function, including alleviated alertness, arousal, and attention21,22,23,24,25. These changes suggest an increase of brain information processing capacity, which can be subsequently measured with BEN. Because the antagonistic binding of caffeine is not region specific, we hypothesized that caffeine will increase BEN across a large portion of the brain. To test this hypothesis, we collected rsfMRI from a large cohort of healthy subjects before and after taking a 200 mg caffeine pill.