The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder

D. Bernardi, D. Shannahoff-Khalsa, J. Sale, J.O. Wright, L. Fadiga, & D. Papo

Frontiers in Psychiatry14:1158404 (2023).

We study how obsessive-compulsive disorder (OCD) affects the complexity and time-reversal symmetry-breaking (irreversibility) of the brain resting-state activity as measured by magnetoencephalography (MEG). Comparing MEG recordings from OCD patients and age/sex matched control subjects, we find that irreversibility is more concentrated at faster time scales and more uniformly distributed across different channels of the same hemisphere in OCD patients than in control subjects. Furthermore, the interhemispheric asymmetry between homologous areas of OCD patients and controls is also markedly different. Some of these differences were reduced by one-year of Kundalini Yoga meditation treatment. Taken together, these results suggest that OCD alters the dynamic attractor of the brain’s resting state and hint at a possible novel neurophysiological characterization of this psychiatric disorder and how this therapy can possibly modulate brain function.

[Read more in Frontiers in Psychiatry]


Telling functional networks apart using ranked network features stability

M. Zanin, B. Güntekin, T. Aktürk, E. Yıldırım, , G. Yener, I. Kiyi, D., Hünerli‑Gündüz, H. Sequeira, & D. Papo

Scientific Reports12:2562 (2022).

Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecifc aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed. 

[Read more in Scientific Reports]

Attaining the recesses of the cognitive space

David Papo

Cognitive Neurodynamics, 16:767–778 (2022).

Existing neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury.

Keywords: Executive functions; closed-head injury; resource allocation; degeneracy; intermittency; scaling

[Read more in Cognitive Neurodynamics]

Algorithmic Approaches for Assessing Irreversibility in Time Series: Review and Comparison

M. Zanin and D. Papo

Entropy, 23:1474 (2021)

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The assessment of time irreversibility, i.e., of the lack of invariance of the statistical properties of a system under the operation of time reversal, is a topic steadily gaining attention within the research community. Irreversible dynamics have been found in many real-world systems, with alterations being connected to, for instance, pathologies in the human brain, heart, and gait, or to inefficiencies in financial markets. Assessing irreversibility in time series is not an easy task, due to its many aetiologies and to the different ways it manifests in data. It is thus not surprising that several numerical methods have been proposed in the last decades, based on different principles and with different applications in mind. In this contribution we review the most important algorithmic solutions that have been proposed to test the irreversibility of time series, their underlying hypotheses, computational and practical limitations, and their comparative performance. We further provide an open-source software library that includes all tests here considered. As a final point, we show that “one size does not fit all”, as tests yield complementary, and sometimes conflicting views to the problem; and discuss some future research avenues.

Keywords: Symmetry; irreversibility; time-reversal symmetry; thermodynamic equilibrium; detailed balance; non-equilibrium steady state; entropy production; fluctuation-dissipation relations; fluctuations relations; thermodynamics uncertainty relations; nonlinearity; non-Gaussianity; coarse-graining; permutation entropy scaling

[Read more in Entropy]

A fast transform for brain connectivity difference evaluation.

M. Zanin, I. Ivanoska, B. Güntekin, G. Yener, T. Loncar-Turukalo, N. Jakovljevic, O. Sveljo & Papo D.

Neuroinformatics (2021)

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Anatomical and dynamical connectivity are essential to healthy brain function. However, quantifying variations in connectivity across conditions or between patient populations and appraising their functional significance are highly non-trivial tasks. Here we show that link ranking differences induce specific geometries in a convenient auxiliary space that are often easily recognizable at mere eye inspection. Link ranking can also provide fast and reliable criteria for network reconstruction parameters for which no theoretical guideline has been proposed.

[Read more in Neuroinformatics]

Keywords: Functional brain connectivity; complex networks; link difference ranking; Alzheimer’s disease; Schizophrenia

Principles and open questions in functional brain network reconstruction

O. Korhonen, M. Zanin & D. Papo

Human Brain Mapping 42:3680–3711 (2021) .


Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.

[Read more in Human Brain Mapping]

Travel restrictions during pandemics: A useful strategy?

M. Zanin and D. Papo

Chaos, 30:111103 (2020)

Though carrying considerable economic and societal costs, restricting individuals’ traveling freedom appears as a logical way to curb the spreading of an epidemic. However, whether, under what conditions, and to what extent travel restrictions actually exert a mitigating effect on epidemic spreading are poorly understood issues. Recent studies have actually suggested the opposite, i.e., that allowing some movements can hinder the propagation of a disease. Here, we explore this topic by modeling the spreading of a generic contagious disease where susceptible, infected, or recovered point-wise individuals are uncorrelated random-walkers evolving within a space comprising two equally sized separated compartments. We evaluate the spreading process under different separation conditions between the two spatial compartments and a forced relocation schedule. Our results confirm that, under certain conditions, allowing individuals to move from regions of high to low infection rates may turn out to have a positive effect on aggregate; such positive effect is nevertheless reduced if a directional flow is allowed. This highlights the importance of considering travel restriction policies alternative to classical ones.

[Read more in Chaos]

Projective mechanisms subtending real world phenomena wipe away cause effect relationships

A. Tozzi and D. Papo

Progress in Biophysics and Molecular Biology, 151:1-13 (2020)


Causal relationships lie at the very core of scientific description of biophysical phenomena. Nevertheless, observable facts involving changes in system shape, dimension and symmetry may elude simple cause and effect inductive explanations. Here we argue that numerous physical and biological phenomena such as chaotic dynamics, symmetry breaking, long-range collisionless neural interactions, zero-value energy singularities, particle/wave duality can be accounted for in terms of purely topological mechanisms devoid of causality. We illustrate how simple topological claims, seemingly far away from scientific inquiry (e.g., “given at least some wind on Earth, there must at all times be a cyclone or anticyclone somewhere”; “if one stirs to dissolve a lump of sugar in a cup of coffee, it appears there is always a point without motion”; “at any moment, there is always a pair of antipodal points on the Earth’s surface with equal temperatures and barometric pressures” ) reflect the action of non-causal topological rules. To do so, we introduce some fundamental topological tools and illustrate how phenomena such as double slit experiments, cellular mechanisms and some aspects of brain function can be explained in terms of geometric projections and mappings, rather than local physical effects. We conclude that unavoidable, passive, spontaneous topological modifications may lead to novel functional biophysical features, independent of exerted physical forces, thermodynamic constraints, temporal correlations and probabilistic a priori knowledge of previous cases.

[Read more in Progress in Biophysics and Molecular Biology]

Keywords: topology; topological explanation; causality; Poincaré-Hopf theorem; hairy ball theorem; Borsuk-Ulam theorem; Kneser graph; non-Hermitian systems

Time irreversibility of resting-state activity in the healthy brain and pathology

M. Zanin, B. Güntekin, T. Aktürk, L. Hanoğlu, & D. Papo

Frontiers in Physiology, 10:1619 (2020)

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Characterizing brain activity at rest is of paramount importance to our understanding both of general principles of brain functioning and of the way brain dynamics is affected in the presence of neurological of psychiatric pathologies. We measured the time-reversal symmetry of spontaneous electroencephalographic brain activity recorded from three groups of patients and their respective control group under two experimental conditions (eyes open and closed). We evaluated differences in time irreversibility in terms of possible underlying physical generating mechanisms. The results showed that resting brain activity is generically time-irreversible at sufficiently long time scales, and that brain pathology is generally associated with a reduction in time asymmetry, albeit with pathology-specific patterns. The significance of these results and their possible dynamical aetiology are discussed. Some implications of the differential modulation of time asymmetry by pathology and experimental condition are examined.

[Read more in Frontiers in Physiology] [Read more in arXiv]

Keywords: resting state; functional; entropy production; time reversal symmetry; Parkinson’s disease; Schizophrenia; EEG

Gauging functional brain activity: from distinguishability to accessibility

D. Papo

Frontiers in Physiology, 10:509 (2019)

Standard neuroimaging techniques provide non-invasive access not only to human brain Avatar Invanatomy but also to its physiology. The activity recorded with these techniques is generally called functional imaging, but what is observed per se is an instance of dynamics, from which functional brain activity should be extracted. Distinguishing between bare dynamics and genuine function is a highly non-trivial task, but a crucially important one when comparing experimental observations and interpreting their significance. Here we illustrate how the ability of neuroimaging to extract genuine functional brain activity is bounded by the structure of functional representations. To do so, we first provide a simple definition of functional brain activity from a system-level brain imaging perspective. We then review how the properties of the space on which brain activity is represented allow defining relations ranging from distinguishability to accessibility of observed imaging data. We show how these properties result from the structure defined on dynamical data and dynamics-to-function projections, and consider some implications that the way and extent to which these are defined have for the interpretation of experimental data from standard system-level brain recording techniques.

Keywords: functional brain activity; functional networks; spatial networks; structure; dynamics; geometry; topology; topological signal processing


[Read more in ArXiv]