An information-theoretic approach to self-organisation: Emergence of complex interdependencies in coupled dynamical systems

Self-organisation lies at the core of fundamental but still unresolved scientific questions, and holds the promise of de-centralised paradigms crucial for future technological developments. While self-organising processes have been traditionally explained by the tendency of dynamical systems to evolve towards specific configurations, or attractors, we see self-organisation as a consequence of the interdependencies that those attractors induce. Building on this intuition, in this work we develop a theoretical framework for understanding and quantifying self-organisation based on coupled dynamical systems and multivariate information theory. We propose a metric of global structural strength that identifies when self-organisation appears, and a multi-layered decomposition that explains the emergent structure in terms of redundant and synergistic interdependencies. We illustrate our framework on elementary cellular automata, showing how it can detect and characterise the emergence of complex structures

Spectrally and temporally resolved estimation of neural signal diversity

Quantifying the complexity of neural activity has provided fundamental insights into cognition, consciousness, and clinical conditions. However, the most widely used approach to estimate the complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that substantially restrict its domain of applicability. In this article we leverage the information-theoretic foundations of LZ to overcome these limitations by introducing a complexity estimator based on state-space models — which we dub Complexity via State-space Entropy Rate (CSER). While having a performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial advantages: 1) CSER offers a principled decomposition into spectral components, which allows us to rigorously investigate the relationship between complexity and spectral power; and 2) CSER provides a temporal resolution two orders of magnitude better than LZ, which allows complexity analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver of complexity changes across states of consciousness; and reveals early entropy increases that precede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms. Overall, by overcoming the main limitations of LZ and substantially extending its range of applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal structure of the signal complexity associated with cognitive processes and conscious states

New development: strategic user orientation in public services delivery—the missing link in the strategic trinity?

This paper explores the application of strategic planning and management to Public Service Organisations (PSOs). It argues that the impact of these approaches has been limited by the absence of an underlying strategic orientation that would provide a value-base upon which to embed these approaches within PSOs. It argues further for such an orientation to privelege the need for public services to add value to the lives of citizens and service users and not to focus solely upon internal measures of efficiency and performance

Causal blankets : Theory and algorithmic framework

Funding Information: F.R. was supported by the Ad Astra Chandaria foundation. P.M. was funded by the Wellcome Trust (grant no. 210920/Z/18/Z). M.B. was supported by a grant from Tem-pleton World Charity Foundation, Inc. (TWCF). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of TWCF. Publisher Copyright: © 2020, Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of Rosas, F. E., Mediano, P. A. M., Biehl, M., Chandaria, S., & Polani, D. (2020). Causal blankets: Theory and algorithmic framework. In T. Verbelen, P. Lanillos, C. L. Buckley, & C. De Boom (Eds.), Active Inference - First International Workshop, IWAI 2020, Co-located with ECML/PKDD 2020, Proceedings (pp. 187-198). (Communications in Computer and Information Science; Vol. 1326). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-64919-7_19We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics. Our approach is based on the notion of causal blanket, which captures sensory and active variables as dynamical sufficient statistics—i.e. as the “differences that make a difference.” Furthermore, our theory provides a broadly applicable procedure to construct PALOs that requires neither a steady-state nor Markovian dynamics. Using our theory, we show that every bipartite stochastic process has a causal blanket, but the extent to which this leads to an effective PALO formulation varies depending on the integrated information of the bipartition

Prestroke Physical Activity and Adverse Health Outcomes after Stroke in the Atherosclerosis Risk in Communities Study

Background and Purpose: The association of physical activity (PA) before stroke (prestroke PA) with long-term prognosis after stroke is still unclear. We examined the association of prestroke PA with adverse health outcomes in the ARIC study (Atherosclerosis Risk in Communities). Methods: We included 881 participants with incident stroke occurring between 1993 and 1995 (visit 3) and December 31, 2016. Follow-up continued until December 31, 2017 to allow for at least 1-year after incident stroke. Prestroke PA was assessed using a modified version of the Baecke questionnaire in 1987 to 1989 (visit 1) and 1993 to 1995 (visit 3), evaluating PA domains (work, leisure, and sports) and total PA. We used Cox proportional hazards models to quantify the association between tertiles of accumulated prestroke PA levels over the 6-year period between visits 1 and 3 and mortality, risk of cardiovascular disease, and recurrent stroke after incident stroke. Results: During a median follow-up of 3.1 years after incident stroke, 676 (77%) participants had adverse outcomes. Highest prestroke total PA was associated with decreased risks of all-cause mortality (hazard ratio, 0.78 [95% CI, 0.63-0.97]) compared with lowest tertile. In the analysis by domain-specific PA, highest levels of work PA were associated with lower risk for all-cause (hazard ratio, 0.77 [95% CI, 0.62-0.96]) and cardiovascular mortality (hazard ratio, 0.45 [95% CI, 0.29-0.70]), and highest levels of leisure PA were associated with lower all-cause mortality (hazard ratio, 0.72 [95% CI, 0.58-0.89]) compared with lowest tertile of PA. No significant associations for sports PA were observed. Conclusions: Higher levels of total prestroke PA as well as work and leisure PA were associated with lower risk of mortality after incident stroke. Public health strategies to increase lifetime PA should be encouraged to decrease long-term mortality after stroke

Reduced emergent character of neural dynamics in patients with a disrupted connectome

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as “Integrated Information Decomposition,” which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems — including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients’ structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence

Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness

The human brain entertains rich spatiotemporal dynamics, which are drastically reconfigured when consciousness is lost due to anaesthesia or disorders of consciousness (DOC). Here, we sought to identify the neurobiological mechanisms that explain how transient pharmacological intervention and chronic neuroanatomical injury can lead to common reconfigurations of neural activity. We developed and systematically perturbed a neurobiologically realistic model of whole-brain haemodynamic signals. By incorporating PET data about the cortical distribution of GABA receptors, our computational model reveals a key role of spatially-specific local inhibition for reproducing the functional MRI activity observed during anaesthesia with the GABA-ergic agent propofol. Additionally, incorporating diffusion MRI data obtained from DOC patients reveals that the dynamics that characterise loss of consciousness can also emerge from randomised neuroanatomical connectivity. Our results generalise between anaesthesia and DOC datasets, demonstrating how increased inhibition and connectome perturbation represent distinct neurobiological paths towards the characteristic activity of the unconscious brain

Comparison of hemodynamic and nutritional parameters between older persons practicing regular physical activity, nonsmokers and ex-smokers

<p>Abstract</p> <p>Background</p> <p>Sedentary lifestyle combined with smoking, contributes to the development of a set of chronic diseases and to accelerating the course of aging. The aim of the study was to compare the hemodynamic and nutritional parameters between elderly persons practicing regular physical activity, nonsmokers and ex-smokers.</p> <p>Methods</p> <p>The sample was comprised of 40 elderly people practicing regular physical activity for 12 months, divided into a Nonsmoker Group and an Ex-smoker Group. During a year four trimestrial evaluations were performed, in which the hemodynamic (blood pressure, heart rate- HR and VO₂) and nutritional status (measured by body mass index) data were collected. The paired t-test and t-test for independent samples were applied in the intragroup and intergroup analysis, respectively.</p> <p>Results</p> <p>The mean age of the groups was 68.35 years, with the majority of individuals in the Nonsmoker Group being women (n = 15) and the Ex-smoker Group composed of men (n = 11). In both groups the variables studied were within the limits of normality for the age. HR was diminished in the Nonsmoker Group in comparison with the Ex-smoker Group (p = 0.045) between the first and last evaluation. In the intragroup analysis it was verified that after one year of exercise, there was significant reduction in the HR in the Nonsmoker Group (p = 0.002) and a significant increase in VO₂for the Ex-smoker Group (p = 0.010). There are no significant differences between the hemodynamic and nutritional conditions in both groups.</p> <p>Conclusion</p> <p>In elderly persons practicing regular physical activity, it was observed that the studied variables were maintained over the course of a year, and there was no association with the history of smoking, except for HR and VO₂.</p

Computational modelling in disorders of consciousness: closing the gap towards personalised models for restoring consciousness

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges