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.

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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]

Functional significance of complex fluctuations in brain activity: from resting state to cognitive neuroscience

D. PapoAvatar Inv

Frontiers in Systems Neuroscience, 8:112 (2014).

Behavioural studies have shown that human cognition is characterized by properties such as temporal scale invariance, heavy-tailed non-Gaussian distributions, and long-range correlations at long time scales, suggesting models of how (non observable) components of cognition interact. On the other hand, results from functional neuroimaging studies show that complex scaling and intermittency may be generic spatio-temporal properties of the brain at rest. Somehow surprisingly, though, hardly ever have the neural correlates of cognition been studied at time scales comparable to those at which cognition shows scaling properties. Here, we analyze the meanings of scaling properties and the significance of their task-related modulations for cognitive neuroscience. It is proposed that cognitive processes can be framed in terms of complex generic properties of brain activity at rest and, ultimately, of functional equations, limiting distributions, symmetries, and possibly universality classes characterizing them.

[Read more in Frontiers in Systems Neuroscience]

Measuring brain temperature without a thermometer

Avatar InvD. Papo

Frontiers in Physiology, 5:24 (2014).

Temperature has profound effects on a wide range of parameters of neural activity at various scales [1]. 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 [6], affecting the duration, frequency and firing rate of activated states during slow frequency oscillations, and the ability to end these states [7]. Temperature also has a substantial effect on chemical reaction rates [8], and affects the blood oxygen saturation level by changing haemoglobin affinity for oxygen [9]. Furthermore, cooling reduces metabolic processes [10], and has been used to silence cortical areas to study their function [11].

[Read more in Frontiers in Fractal Physiology]

Time scales in cognitive neuroscience

D. PapoAvatar Inv

Frontiers in Physiology, 4:86 (2013).

Cognitive neuroscience boils down to describing the ways in which cognitive function results from brain activity. In turn, brain activity shows complex fluctuations, with structure at many spatio-temporal scales. Exactly how cognitive function inherits the physical dimensions of neural activity, though, is highly non-trivial, and so are generally the corresponding dimensions of cognitive phenomena. As for any physical phenomenon, when studying cognitive function, the first conceptual step should be that of establishing its dimensions. Here, we provide a systematic presentation of the temporal aspects of task-related brain activity, from the smallest scale of the brain imaging technique’s resolution, to the observation time of a given experiment, through the characteristic time scales of the process under study. We first review some standard assumptions on the temporal scales of cognitive function. In spite of their general use, these assumptions hold true to a high degree of approximation for many cognitive (viz. fast perceptual) processes, but have their limitations for other ones (e.g., thinking or reasoning). We define in a rigorous way the temporal quantifiers of cognition at all scales, and illustrate how they qualitatively vary as a function of the properties of the cognitive process under study. We propose that each phenomenon should be approached with its own set of theoretical, methodological and analytical tools. In particular, we show that when treating cognitive processes such as thinking or reasoning, complex properties of ongoing brain activity, which can be drastically simplified when considering fast (e.g., perceptual) processes, start playing a major role, and not only characterize the temporal properties of task-related brain activity, but also determine the conditions for proper observation of the phenomena. Finally, some implications on the design of experiments, data analyses, and the choice of recording parameters are discussed.

[Read more in Frontiers in Fractal Physiology]

Why should cognitive neuroscientists study the brain’s resting state?

D. PapoAvatar Inv

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]

Brain temperature: what it means and what it can do for (cognitive) neuroscientists

David PapoAvatar Inv

arXiv:1310.2906v1 (2013).

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]