[Network-wide effects of pallidal deep brain stimulation normalised abnormal cerebellar cortical activity in the dystonic animal model.

Background: Deep brain stimulation (DBS) targeting globus pallidus internus (GPi) is a recognised therapy for drug-refractory dystonia. However, the mechanisms underlying this effect are not fully understood. This study explores how pallidal DBS alters spatiotemporal pattern formation of neuronal dynamics within the cerebellar cortex in a dystonic animal model, the dt hamster.

Data-driven and equation-free methods for neurological disorders: analysis and control of the striatum network.

The striatum as part of the basal ganglia is central to both motor, and cognitive functions. Here, we propose a large-scale biophysical network for this part of the brain, using modified Hodgkin-Huxley dynamics to model neurons, and a connectivity informed by a detailed human atlas. The model shows different spatio-temporal activity patterns corresponding to lower (presumably normal) and increased cortico-striatal activation (as found in, e.

Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model.

. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target..

Optimal vaccine roll-out strategies including social distancing for pandemics.

Non-pharmacological interventions (NPIs), principally social distancing, in combination with effective vaccines, aspire to develop a protective immunity shield against pandemics and particularly against the COVID-19 pandemic. In this study, an agent-based network model with small-world topology is employed to find optimal policies against pandemics, including social distancing and vaccination strategies. The agents' states are characterized by a variation of the SEIR model (susceptible, exposed, infected, recovered).

Deep brain stimulation for movement disorder treatment: exploring frequency-dependent efficacy in a computational network model.

A large-scale computational model of the basal ganglia network and thalamus is proposed to describe movement disorders and treatment effects of deep brain stimulation (DBS). The model of this complex network considers three areas of the basal ganglia region: the subthalamic nucleus (STN) as target area of DBS, the globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus. Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi.

Bautin bifurcations in a forest-grassland ecosystem with human-environment interactions.

Ecosystems may be characterized by a complex dynamical behaviour where external disturbances and/or internal perturbations may trigger sudden/irreversible changes, called catastrophic shifts. Simple mathematical models in the form of ordinary and/or partial differential equations have been proposed to approximate in a qualitatively manner the observed complex phenomena, where catastrophic shifts are determined by bifurcation points. In this work, we show that in ecosystems, gradual/smooth changes may be transformed in sudden/catastrophic shifts as a consequence of codimension-2 bifurcations.

On the effect of the path length of small-world networks on epidemic dynamics.

We show how one can trace in a systematic way the coarse-grained solutions of individual-based stochastic epidemic models evolving on heterogeneous complex networks with respect to their topological characteristics. In particular, we illustrate the "distinct" impact of the average path length (with respect to the degree and clustering distributions) on the emergent behavior of detailed epidemic models; to achieve this we have developed an algorithm that allows its tuning at will. The framework could be used to shed more light on the influence of weak social links on epidemic spread within small-world network structures, and ultimately to provide novel systematic computational modeling and exploration of better contagion control strategies.

Dynamic analysis of EEG signals during spatial working memory used for either perception discrimination or planning of action.

We analysed multi-channel electroencephalographic (EEG) recordings during a spatial Working Memory (WM) task in order to test the hypothesis that segmentation of perception and action is present when the visual stimulus has been stored in spatial WM. To detect the interactions between different regions of the brain depending on the task we employed both Short Time Fourier Transformation (STFT) and the concept of Granger Causality (GC). Our computational analysis supports evidence that the Parietal Cortex (PC) is involved in WM processing.

Epidemionics: from the host-host interactions to the systematic analysis of the emergent macroscopic dynamics of epidemic networks.

One of the most critical issues in epidemiology revolves around the bridging of the diverse space and time scales stretching from the microscopic scale, where detailed knowledge on the immune mechanisms, host-microbe and host-host interactions is often available, to the macroscopic population-scale where the epidemic emerges, the questions arise and the answers are required. In this paper we show how the so called Equation-Free approach, a novel computational framework for multi-scale analysis, can be exploited to efficiently analyze the macroscopic emergent behavior of complex epidemic models on certain type of networks by acting directly on the multi-scale simulation. The methodology can be used to bypass the need of derivation of closures for the emergent population-level equations providing a systematic computational strict approach for macroscopic-level analysis.