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.
D. Papo, J.M. Buldú, S. Boccaletti and E.T. Bullmore
Philosophical Transactions of the Royal Society B, 369:20130520 (2014).
Complex network theory is a statistical physics understanding of graph theory, itself a much 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]
D. Papo, M. Zanin, J.A. Pineda-Pardo, S. Boccaletti, and J.M. Buldú
Philosophical Transactions of the Royal Society B 369:20130525 (2014).
Many physical 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]
Frontiers in Physiology, 5:24 (2014).
Temperature has profound effects on a wide range of parameters of neural activity at various scales . At the cell level, ionic currents, membrane potential, input resistance, action potential amplitude, duration and propagation, and synaptic transmission have all been shown to be affected by temperature variations [1-5]. At mesoscopic scales of neural activity, temperature changes can steer network activity toward different functional regimes , affecting the duration, frequency and firing rate of activated states during slow frequency oscillations, and the ability to end these states . Temperature also has a substantial effect on chemical reaction rates , and affects the blood oxygen saturation level by changing haemoglobin affinity for oxygen . Furthermore, cooling reduces metabolic processes , and has been used to silence cortical areas to study their function .
[Read more in Frontiers in Fractal Physiology]
Frontiers in Human Neuroscience, 7:45 (2013).
Cognitive neuroscience studies how cognitive function is produced by the brain. Seen from a reverse angle, cognitive neuroscience studies how brain activity is modulated by the execution of cognitive tasks. In the former case, cognitive function is characterized in terms of neural properties associated with the execution of given cognitive tasks, while in the latter it can be thought of as a probe exposing information on brain dynamics. Brain activity displays dynamics independently of whether a particular task is carried out or not. The question is then: should cognitive neuroscience get interested in the properties of resting brain activity? And, if so, how and to what extent can studying resting brain activity help characterizing the neural correlates of cognitive processes?
[Read more in Frontiers in Human Neuroscience]
The effects of temperature on various aspects of neural activity from single cell to neural circuit level have long been known. However, how temperature affects the system-level of activity typical of experiments using non-invasive imaging techniques, such as magnetic brain imaging of electroencephalography, where neither its direct measurement nor its manipulation are possible, is essentially unknown. Starting from its basic physical definition, we discuss
possible ways in which temperature may be used both as a parameter controlling the evolution of other variables through which brain activity is observed, and as a collective variable describing brain activity. On the one hand, temperature represents a key control parameter of brain phase space navigation. On the other hand, temperature is a quantitative measure of the relationship between spontaneous and evoked brain activity, which can be used to describe how brain activity deviates from thermodynamic equilibrium. These two aspects are further illustrated in the case of learning-related brain activity, which is shown to be reducible to a purely thermally guided phenomenon. The phenomenological similarity between brain activity and amorphous materials suggests a characterization of plasticity of the former in terms of the well-studied temperature and thermal history dependence of the latter, and of individual differences in learning capabilities as material-specific properties. Finally, methods to extract a temperature from experimental data are reviewed, from which the whole brain’s thermodynamics can then be reconstructed.
[Read more in ArXiv]