Neural Microprobe Device Modelling for Implant Micromotions Failure Mitigation

Brain micromotion is a major contributor to the failure of implantable neural interfaces. Brain micromotions and tissue damage can be effectively reduced in two ways: (i) miniaturization of the implantable device footprint and (ii) choosing flexible materials for the device substrate. To meet these requirements, in this work we perform two sets of modelling using finite element method in COMSOL Multiphysics. First, we model the performance of different materials ranging from stiff (e.g. Silicon) to very soft (e.g. PDMS) with different sizes to find the optimal dimension and material for the microprobe. For the device size optimization, the main degree of freedom is thickness, while the minimum shank width and length depend on the recording sites and the target recording point, respectively. Modelling devices with different thicknesses (50 − 200 μm) and fixed shank width (100 μm) based on different substrates, we show that the Polyimide-based microprobe exhibits a safety factor of 5 to 15 and maximum von mises stress of 248–770 MPa. Further, simulations indicate that the Polyimide-based microprobe of 50 μm thickness, exhibiting safety factor of 5 and stress of 248 MPa, provides the optimal solution in terms of size and material. Second, to analyse the device shape factor, we model different layouts based on the obtained optimal design and find that the optimal layout features von mises stress of 134.123 MPa, which is versatile and suitable to be used as microprobe especially for the brain micromotion effects mitigation purpose

Causal coupling inference from multivariate time series based on ordinal partition transition networks

Identifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Intelligent biohybrid systems for functional brain repair

In the quest for novel neurotechnologies to defeat brain diseases, intelligent biohybrid systems have earned a privileged role among unconventional brain repair strategies. These systems are based on the functional interaction between the nervous tissue and engineered devices, the establishment of which is mediated by artificial intelligence. As novel, previously unimaginable neurotechnologies are emerging, what are the translational impact and the practical consequences carried by these tools for the clinical practice? In this review, we describe the progression of brain repair strategies, from the early pioneering demonstration of their feasibility to their recent implementation in the experimental and clinical settings. We will show how the convergence of different disciplines across the decades has led to the emergence of innovative concepts based on intelligent biohybrid designs. We discuss the advantages and limitations of the described approaches and we conclude by proposing possible solutions to the current shortcomings of available paradigms

Computational Modelling of Brain Implantable Microprobes for Diminishing Micromotions Failure

No abstract available

Computational Modelling of Brain Implantable Microprobes for Diminishing Micromotions Failure

No abstract available

GABA_AR modulates the bursting propensity of subicular IB cells.

<p><b>A:</b> Definition of burst ratio of IB cells responses to depolarising current injection. This IB neuron generated 4 bursts made of 2 spikes (black arrowheads), followed by the generation of a single action potential (grey arrowhead). Therefore, the number of spikes generated during burst-firing was 8 on a total of 9, thus yielding a burst ratio of 0.89. The burst indicated by the horizontal arrow is shown at expanded time scale in the inset on the left, where the black arrow points at the depolarizing membrane potential fluctuation giving rise to the second spike generated within the burst. Bath-application of picrotoxin (100 µM) decreased the burst ratio of this IB neuron to 0.33. <b>B:</b> Summary of the parameters used to quantify the bursting behavior of PCs and their changes by pharmacological blockade of GABA_AR.</p

TREATING EPILEPSY VIA ADAPTIVE NEUROSTIMULATION: A REINFORCEMENT LEARNING APPROACH

This paper presents a now methodology for automatically learning an optimal neurostimulation strategy for the treatment of epilepsy. The technical challenge is to automatically modulate neurostimulation parameters, as a function of the observed EEG signal, so as to minimize the frequency and duration of seizures. The methodology leverages recent techniques from the machine learning literature, in particular the reinforcement learning paradigm, to formalize this optimization problem. We present an algorithm which is able to automatically learn an adaptive neurostimulation strategy directly from labeled training data acquired from animal brain tissues. Our results suggest that this methodology can be used to automatically find a stimulation strategy which effectively reduces the incidence of seizures, while also minimizing the amount of stimulation applied. This work highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders such as epilepsy