“Neurofeedback: principles, appraisal and outstanding issues“

D. Papo  


Neurofeedback is a form of brain training in which subjects are fed back information onAvatar Inv some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last twenty years, NF has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, despite its growing popularity, each of NF’s main steps comes with its own set of often covert assumptions. Here we critically examine some conceptual and methodological issues associated with the way NF’s general objectives and neural targets are defined, and review the neural mechanisms through which NF may act, and the way its efficacy is gauged. The NF process is characterised in terms of functional dynamics, and possible ways in which it may be controlled are discussed. Finally, it is proposed that improving NF will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain-behaviour relationships.

[Read more in arXiv]


Beyond the anatomy-based representation of brain function

D. PapoAvatar Inv

Physics of Life Reviews, 21:42-45 (2017).

Often, viz. in tumour removal procedures, neurosurgeons operate on a sedated but awake patient to precisely locate functional brain areas that must be avoided. To do so, brain regions are electrically stimulated while the patient performs tasks such as talking, counting or looking at pictures. The patient’s responses are then used to create a map of the functional areas of the brain and remove as much of the tumour as possible. In so doing, neurosurgeons parse the Euclidean space of brain anatomy to navigate into the space of cognitive function. However the map between these two spaces is not smooth, and the topology induced by local electrical stimulation non-trivial. So, how should stimulation be carried out, i.e. on what space should it act to render the application  smooth and the resulting topology “tractable”?

[Read more in Physics of Life Reviews]

Detecting switching and intermittent causalities in time series

M. Zanin and D. Papo

images (3)

Chaos, 27:047403 (2017).

In biological systems such as the brain, interactions can be fast and short-lived. However, robust causal relationships are usually quantified over time-windows much larger than functionally meaningful time scales. We propose a method to overcome this drawback and quantify causal dynamical connectivity in which every possible time window is considered and non-overlapping ones in which the causality is strongest eventually selected. Our results show that transient but not classical time-averaged causality estimations can discriminate between the electroencephalographic activity of a small sample of alcoholic subjects and a matched healthy control group. Differences between groups appear to be a local rather than a global network property. The implications of these results for the modelling of brain functioning and pathology are briefly discussed.

[Read more in Chaos]

The ACE brain

M. Zanin and D. Papo   eyes1

Frontiers in Computational Neuroscience, 10:122 (2016).

Neuroscientists’ models of brain functional organization, and in particular of how a given task recruits brain resources, bear important analogies with the way computer elements are arranged and activated to perform complex operations. In modern CPUs, data are distributed across different sub-units by a central controller, a structure inspired by the research performed in the 40s by von Neumann (1993). However, this is not the only possible configuration, and we compare it with the alternative proposed by Alan Turing in the same decade (Carpenter and Doran, 1986). How does the underlying model of computer functioning influence the way neuroscientists describe the brain? For instance, at a system-level of description, neuroscientists typically want to extract the minimum sub-system of the whole brain necessary to execute a given task. Suppose in particular that brain activity is endowed with a network representation (Bullmore and Sporns, 2009). What would the minimal subsystem look like? We propose that Turing’s approach is more representative of the human brain, and discuss when functional networks may yield misleading results when applied to such a system.

[Read more in Frontiers in Computational Neuroscience]

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.