Francesca Camera

Numerical characterization of intraoperative and chronic electrodes in deep brain stimulation

An intraoperative electrode (microelectrode) is used in the deep brain stimulation (DBS) technique to pinpoint the brain target and to choose the best parameters for the electrical stimulus. However, when the intraoperative electrode is replaced with the chronic one (macroelectrode), the observed effects do not always coincide with predictions. To investigate the causes of such discrepancies, a 3D model of the basal ganglia has been considered and realistic models of both intraoperative and chronic electrodes have been developed and numerically solved. Results of simulations of the electric potential (V) and the activating function (AF) along neuronal fibers show that the different geometries and sizes of the two electrodes do not change the distributions and polarities of these functions, but rather the amplitudes. This effect is similar to the one produced by the presence of different tissue layers (edema or glial tissue) in the peri-electrode space. Conversely, an inaccurate positioning of the chronic electrode with respect to the intraoperative one (electric centers not coincident) may induce a completely different electric stimulation in some groups of fibers.

A Microdosimetric Study of Electropulsation on Multiple Realistically Shaped Cells: Effect of Neighbours

Over the past decades, the effects of ultrashort-pulsed electric fields have been used to investigate their action in many medical applications (e.g. cancer, gene electrotransfer, drug delivery, electrofusion). Promising aspects of these pulses has led to several in vitro and in vivo experiments to clarify their action. Since the basic mechanisms of these pulses have not yet been fully clarified, scientific interest has focused on the development of numerical models at different levels of complexity: atomic (molecular dynamic simulations), microscopic (microdosimetry) and macroscopic (dosimetry). The aim of this work is to demonstrate that, in order to predict results at the cellular level, an accurate microdosimetry model is needed using a realistic cell shape, and with their position and packaging (cell density) characterised inside the medium.

A Computational Model for Real-Time Calculation of Electric Field due to Transcranial Magnetic Stimulation in Clinics

The aim of this paper is to propose an approach for an accurate and fast (real-time) computation of the electric field induced inside the whole brain volume during a transcranial magnetic stimulation (TMS) procedure. The numerical solution implements the admittance method for a discretized realistic brain model derived from Magnetic Resonance Imaging (MRI). Results are in a good agreement with those obtained using commercial codes and require much less computational time. An integration of the developed code with neuronavigation tools will permit real-time evaluation of the stimulated brain regions during the TMS delivery, thus improving the efficacy of clinical applications.

The CNP signal is able to silence a supra threshold neuronal model.

Several experimental results published in the literature showed that weak pulsed magnetic fields affected the response of the central nervous system. However, the specific biological mechanisms that regulate the observed behaviors are still unclear and further scientific investigation is required. In this work we performed simulations on a neuronal network model exposed to a specific pulsed magnetic field signal that seems to be very effective in modulating the brain activity: the Complex Neuroelectromagnetic Pulse (CNP). Results show that CNP can silence the neurons of a feed-forward network for signal intensities that depend on the strength of the bias current, the endogenous noise level and the specific waveforms of the pulses. Therefore, it is conceivable that a neuronal network model responds to the CNP signal with an inhibition of its activity. Further studies on more realistic neuronal networks are needed to clarify if such an inhibitory effect on neuronal tissue may be the basis of the induced analgesia seen in humans and the antinociceptive effects seen in animals when exposed to the CNP.

Restoring the encoding properties of a stochastic neuron model by an exogenous noise.

Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR) phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed.

Effects of pulsed magnetic field on neurons: Cnp signal silences a feed-forward network model

Several experimental results published in the literature showed that weak pulsed magnetic fields affected the response of the central nervous system. However, the specific biological mechanisms that regulate the observed behaviors are still unclear and further scientific investigation is required. In this work we performed simulations on neuronal models exposed to a specific pulsed magnetic field signal that seems to be very effective in modulating the brain activity: the Complex neuroelectromagnetic pulse (Cnp). Results show that Cnp can silence the neurons of a feed-forward network for signal intensities that depend on the strength of the bias current and the endogenous noise level.

Effects of nanosecond pulsed electric fields on the activity of a Hodgkin and Huxley neuron model

The cell membrane poration is one of the main assessed biological effects of nanosecond pulsed electric fields (nsPEF). This structural change of the cell membrane appears soon after the pulse delivery and lasts for a time period long enough to modify the electrical activity of excitable membranes in neurons. Inserting such a phenomenon in a Hodgkin and Huxley neuron model by means of an enhanced time varying conductance resulted in the temporary inhibition of the action potential generation. The inhibition time is a function of the level of poration, the pore resealing time and the background stimulation level of the neuron. Such results suggest that the neuronal activity may be efficiently modulated by the delivery of repeated pulses. This opens the way to the use of nsPEFs as a stimulation technique alternative to the conventional direct electric stimulation for medical applications such as chronic pain treatment.

Automatic decoding of input sinusoidal signal in a neuron model: Improved SNR spectrum by low-pass homomorphic filtering

Abstract The principles on how neurons encode and process information from low-level stimuli are still open questions in neuroscience. Neuron models represent useful tools to answer this question but a sensitive method is needed to decode the input information embedded in the neuron spike sequence. In this work, we developed an automatic decoding procedure based on the SNR spectrum improved by low-pass homomorphic filtering. The procedure was applied to a stochastic Hodgkin Huxley neuron model forced by a low-level sinusoidal signal in the range 50 Hz–300 Hz. It exhibited very high performance, in terms of sensitivity and precision, in automatically decoding the input information even when using a relatively small number of model runs (≈ 200). This could provide a fast and valid procedure to understand the encoding mechanisms of low-level sinusoidal stimuli used by different types of neurons.

An open-label, one-arm, dose-escalation study to evaluate safety and tolerability of extremely low frequency magnetic fields in acute ischemic stroke

Extremely low frequency magnetic fields (ELF-MF) could be an alternative neuroprotective approach for ischemic stroke because preclinical studies have demonstrated their effects on the mechanisms underlying ischemic damage. The purpose of this open-label, one arm, dose-escalation, exploratory study is to evaluate the safety and tolerability of ELF-MF in patients with acute ischemic stroke. Within 48 hours from the stroke onset, patients started ELF-MF treatment, daily for 5 consecutive days. Clinical follow-up lasted 12 months. Brain MRI was performed before and 1 month after the treatment. The distribution of ELF-MF in the ischemic lesion was estimated by dosimetry. Six patients were stimulated, three for 45 min/day and three for 120 min/day. None of them reported adverse events. Clinical conditions improved in all the patients. Lesion size was reduced in one patient stimulated for 45 minutes and in all the patients stimulated for 120 minutes. Magnetic field intensity within the ischemic lesion was above 1 mT, the minimum value able to trigger a biological effect in preclinical studies. Our pilot study demonstrates that ELF-MF are safe and tolerable in acute stroke patients. A prospective, randomized, placebo-controlled, double-blind study will clarify whether ELF-MFs could represent a potential therapeutic approach.

Automatic decoding of input sinusoidal signal in a neuron model: High pass homomorphic filtering

Abstract A processing technique for decoding the information transferred from a sinusoidal input to the output spike sequence of a neuron model is a desirable tool for understanding the encoding principles of neuronal systems. An automatic decoding procedure, already proposed by the authors, is based on an improved version of the Signal to Noise Ratio (SNR) calculation and requires a knowledge of both spontaneous (in absence of input signal) and stimulated (in presence of input signal) neuronal activities. In this work, an automatic decoding procedure based on high-pass homomorphic filtering is developed that provides performances comparable or better than that obtained with the improved SNR. The advantages of not requiring the neuronal spontaneous activities, as most SNR methods do, are a procedure simplification, a reduction of the amount of data needed to decode the information, and the possibility of application to contexts where the neuronal spontaneous activity is not available.