Commentary: The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs

D. PapoAvatar Inv

Frontiers in Human Neuroscience, 10:423 (2016).

The “entropic brain hypothesis” holds that the quality of conscious states depends on the system’s entropy [1]. Brain activity is said to become “more random and so harder to predict in primary states – of which the psychedelic state is an exemplar”. Psychedelic-induced brain activity would be associated with elevated entropy in some of its aspects with respect to normal wakeful consciousness. This would indicate that psychedelic-induced brain activity would exhibit criticality, while normal wakeful consciousness would be subcritical.

But can entropy be a unique indicator of the “quality of consciousness”? Are there reasons to believe that psychedelic-induced activity is not critical?

[1] Carhart-Harris, R.L., Leech, R., Hellyer, P.J., Shanahan, M., Feilding, A., Tagliazucchi, E., Chialvo, D.R., and Nutt, D. (2014). The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs.Front. Hum. Neurosci. 8:20.

[Read more in Frontiers in Human Neuroscience]


Beware of the Small-world, neuroscientist!

D. Papo, M. Zanin,  J.H. Martínezand J.M. Buldúconn

Frontiers in Human Neuroscience, 10:96 (2016).

Whether or not the brain is indeed a SW network is still very much an open question. The question that we address is of a pragmatical rather than an ontological nature: independently of whether the brain is a SW network or not, to what extent can neuroscientists using standard system-level neuroimaging techniques interpret the SW construct in the context of functional brain networks? In a typical experimental setting, neuroscientists record brain images, define nodes and links, construct a network, extract its topological properties, to finally assess their statistical significance and their possible functional meaning. We review evidence showing that behind each of these stages lurk fundamental technical, methodological or theoretical stumbling blocks that render the experimental quantification of the SW structure and its interpretation in terms of information processing problematic, questioning its usefulness as a descriptor of global brain organization. The emphasis is on functional brain activity reconstructed using standard system-level brain recording techniques, where the SW construct appears to be the most problematic.

[Read more in Frontiers in Human Neuroscience]     [arXiv]

How can we study reasoning in the brain?

D. PapoAvatar Inv

Frontiers in Human Neuroscience, 10:423 (2016).

The brain did not develop a dedicated device for reasoning. This fact bears dramatic consequences. While for perceptuo-motor functions neural activity is shaped by the input’s statistical properties, and processing is carried out at high speed in hardwired spatially segregated modules, in reasoning, neural activity is driven by internal dynamics and processing times, stages, and functional brain geometry are largely unconstrained a priori. Here, it is shown that the complex properties of spontaneous activity, which can be ignored in a short-lived event-related world, become prominent at the long time scales of certain forms of reasoning. It is argued that the neural correlates of reasoning should in fact be defined in terms of non-trivial generic properties of spontaneous brain activity, and that this implies resorting to concepts, analytical tools, and ways of designing experiments that are as yet non-standard in cognitive neuroscience. The implications in terms of models of brain activity, shape of the neural correlates, methods of data analysis, observability of the phenomenon and experimental designs are discussed.

Characterizing Motif Dynamics of Electric Brain Activity Using Symbolic Analysis

M. Zanin and D. Papoconn

Entropy, 16:5655-5667 (2014).

Motifs are small recurring circuits of interactions which constitute the backbone of networked systems. Characterizing motif dynamics is therefore key to understanding the functioning of such systems. Here we propose a method to define and quantify the temporal variability and time scales of electroencephalogram (EEG) motifs of resting brain activity. Given a triplet of EEG sensors, links between them are calculated by means of linear correlation; each pattern of links (i.e., each motif) is then associated to a symbol, and its appearance frequency is analyzed by means of Shannon entropy. Our results show that each motif becomes observable with different coupling thresholds and evolves at its own time scale, with fronto-temporal sensors emerging at high thresholds and changing at fast time scales, and parietal ones at low thresholds and changing at slower rates. Finally, while motif dynamics differed across individuals, for each subject, it showed robustness across experimental conditions, indicating that it could represent an individual dynamical signature.

[Read more in Entropy]

Complex network theory and the brain‎

D. Papo, J.M. Buldú, S. Boccaletti and E.T. Bullmoreconn

Philosophical Transactions of the Royal Society B369:20130520 (2014).

Complex network theory is a statistical physics understanding of graph ‎theory, itself a sample_cover PTRSBmuch older branch of pure mathematics. The statistical physics approach aims at explaining observable macroscopic behaviour of a given system as emerging in a non-trivial way from the interactions of a vast number of microscopic units or agents. Complex network theory can be thought of as a subfield of statistical physics for structurally disordered, dynamically heterogeneous systems with non-trivial topology; and as an extension of graph theory to systems with high structural heterogeneity and ‎inherently dynamical properties, two key properties of the vast ‎majority of real-life systems, including brains‎.

Can this approach be useful when studying brain anatomy and function?

Read more in Philosophical Transactions]   [Read interview in Phil. Trans Blog]    [Listen to podcast in Nature]

Functional brain networks: great expectations, hard times, and the big leap forward

D. Papo, M. Zanin, J.A. Pineda-Pardo, S. Boccaletti, and J.M. Buldúconn

Philosophical Transactions of the Royal Society B 369:20130525 (2014).

Many physicasample_cover PTRSBl and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode.

[Read more in Philosophical Transactions]    [Read more in ArXiv]       [Read interview in Phil. Trans Blog]    [Listen to podcast in Nature]

Parenclitic networks: uncovering new functions in biological data

images (3)M. Zanin, J. Medina Alcazar, J. Vicente Carbajosa, M. Gomez Paez, D. Papo, P. Sousa, E. Menasalvas, and S. Boccaletti

Scientific Reports, 4:5112 (2014).

We introduce a novel method to represent time independent, scalar data sets as complex networks. We apply our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana. In the proposed network representation, the most important genes for the plant response turn out to be the nodes with highest centrality in appropriately reconstructed networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure.

[Read more in Scientific Reports]        [Read more in ArXiv]