Functional network dynamics between the anterior thalamus and the cortex in deep brain stimulation for epilepsy.

Owing to its unique connectivity profile with cortical brain regions, and its suggested role in the subcortical propagation of seizures, the anterior nucleus of the thalamus (ANT) has been proposed as a key deep brain stimulation (DBS) target in drug-resistant epilepsy. However, the spatio-temporal interaction dynamics of this brain structure, and the functional mechanisms underlying ANT DBS in epilepsy remain unknown. Here, we study how the ANT interacts with the neocortex in vivo in humans and provide a detailed neurofunctional characterization of mechanisms underlying the effectiveness of ANT DBS, aiming at defining intraoperative neural biomarkers of responsiveness to therapy, assessed at 6 months post-implantation as the reduction in seizure frequency. A cohort of 15 patients with drug-resistant epilepsy (n = 6 males, age = 41.6 ± 13.79 years) underwent bilateral ANT DBS implantation. Using intraoperative cortical and ANT simultaneous electrophysiological recordings, we found that the ANT is characterized by high amplitude θ (4-8 Hz) oscillations, mostly in its superior part. The strongest functional connectivity between the ANT and the scalp EEG was also found in the θ band in ipsilateral centro-frontal regions. Upon intraoperative stimulation in the ANT, we found a decrease in higher EEG frequencies (20-70 Hz) and a generalized increase in scalp-to-scalp connectivity. Crucially, we observed that responders to ANT DBS treatment were characterized by higher EEG θ oscillations, higher θ power in the ANT, and stronger ANT-to-scalp θ connectivity, highlighting the crucial role of θ oscillations in the dynamical network characterization of these structures. Our study provides a comprehensive characterization of the interaction dynamic between the ANT and the cortex, delivering crucial information to optimize and predict clinical DBS response in patients with drug-resistant epilepsy.

Multiparametric non-linear TENS modulation to integrate intuitive sensory feedback.

Objective. Transcutaneous electrical nerve stimulation (TENS) has been recently introduced in neurorehabilitation and neuroprosthetics as a promising, non-invasive sensory feedback restoration alternative to implantable neurostimulation. Yet, the adopted stimulation paradigms are typically based on single-parameter modulations (e.g. pulse amplitude (PA), pulse-width (PW) or pulse frequency (PF)). They elicit artificial sensations characterized by a low intensity resolution (e.g. few perceived levels), low naturalness and intuitiveness, hindering the acceptance of this technology. To address these issues, we designed novel multiparametric stimulation paradigms, featuring the simultaneous modulation of multiple parameters, and implemented them in real-time tests of performance when exploited as artificial sensory inputs.Approach. We initially investigated the contribution of PW and PF variations to the perceived sensation magnitude through discrimination tests. Then, we designed three multiparametric stimulation paradigms comparing them with a standard PW linear modulation in terms of evoked sensation naturalness and intensity. The most performant paradigms were then implemented in real-time in a Virtual Reality-TENS platform to assess their ability to provide intuitive somatosensory feedback in a functional task.Main results. Our study highlighted a strong negative correlation between perceived naturalness and intensity: less intense sensations are usually deemed as more similar to natural touch. In addition, we observed that PF and PW changes have a different weight on the perceived sensation intensity. As a result, we adapted the activation charge rate (ACR) equation, proposed for implantable neurostimulation to predict the perceived intensity while co-modulating the PF and charge per pulse, to TENS (ACRT). ACRTallowed to design different multiparametric TENS paradigms with the same absolute perceived intensity. Although not reported as more natural, the multiparametric paradigm, based on sinusoidal PF modulation, resulted being more intuitive and subconsciously integrated than the standard linear one. This allowed subjects to achieve a faster and more accurate functional performance.Significance. Our findings suggest that TENS-based, multiparametric neurostimulation, despite not consciously perceived naturally, can provide integrated and more intuitive somatosensory information, as functionally proved. This could be exploited to design novel encoding strategies able to improve the performance of non-invasive sensory feedback technologies.

Recalibration of neuromodulation parameters in neural implants with adaptive Bayesian optimization.

Objective. Neuromodulation technology holds promise for treating conditions where physiological mechanisms of neural activity have been affected. To make treatments efficient and devices highly effective, neurostimulation protocols must be personalized. The interface between the targeted nervous tissue and the neurotechnology (i.e. human-machine link or neural interface) usually requires constant re-calibration of neuromodulation parameters, due to many different biological and microscale phenomena happening over-time. This adaptation of the optimal stimulation parameters generally involves an expert-mediated re-calibration, with corresponding economic burden, compromised every-day usability and efficacy of the device, and consequent loss of time and increased discomfort of patients going back to clinics to get the device tuned. We aim to construct an adaptable AI-based system, able to compensate for these changes autonomously.Approach. We exploited Gaussian process-based Bayesian optimization (GPBO) methods to re-adjust the neurostimulation parameters in realistic neuroprosthetic data by integrating temporal information into the process to tackle the issue of time variability. To this aim, we built a predictive model able to tune the neuromodulation parameters in two separate crucial scenarios where re-calibration is needed. In the first one, we built a model able to find the optimal active sites in a multichannel electrode, i.e. able to cover a certain function for a neuroprosthesis, which in this specific case was the evoked-sensation location variability. In the second one, we propose an algorithm able to adapt the injected charge required to obtain a functional neural activation (e.g. perceptual threshold variability). By retrospectively collecting the outcomes from the calibration experiments in a human clinical trial utilizing implantable neuromodulation devices, we were able to quantitatively assess our GPBO-based approach in an offline setting.Main results.Our automatic algorithm can successfully adapt neurostimulation parameters to evoked-sensation location changes and to perceptual threshold changes over-time. These findings propose a quick, automatic way to tackle the inevitable variability of neurostimulation parameters over time. Upon validation in other frameworks it increases the usability of this technology through decreasing the time and the cost of the treatment supporting the potential for future widespread use. This work suggests the exploitation of AI-based methods for developing the next generation of 'smart' neuromodulation devices.

Multifaceted understanding of human nerve implants to design optimized electrodes for bioelectronics.

Bioelectronic medicine is a promising venue for treatment of disabilities using implantable neural interfaces. Peripheral neurostimulation of residual nerves recently enabled multiple functional benefits in amputees. Despite the preliminary promising impact on patients' life, the over-time stability of implants and the related nerve reactions are unclear. To unveil the mechanisms and inform the design of better nerve-electrode interfaces, we engaged a multifaceted approach, merging functional responses from patients, their histological data, and corresponding computational modelling. Neurostimulation evoked different selective sensation locations and qualities over-time, with respective perceptual thresholds, that showed different degree of time stabilities dependent from the stimulating active sites. The histological analysis after explant showed mild tissue reactions, while electromechanically active sites and substrates remained conserved. Computational models, based on patients' histology, revealed the direct influence of the simulated tissue reaction to change of thresholds and type of perceived sensations. Novel insights of electrode biocompatibility was observed compared to animals and the increase of thresholds could be predicted computationally. This multifaced framework suggest that future intraneural implants should have easier implantation and higher biocompatibility counteracting the sensations changes through AI-based stimulations and electrode coatings.

Moderate antioxidant supplementation has no effect on biomarkers of oxidant damage in healthy men with low fruit and vegetable intakes.

The link between high fruit/vegetable intake and reduced chronic disease may be partly explained by antioxidant protection. To determine the effect of moderate antioxidant intake on biomarkers of oxidant damage, we assessed in vivo lipid and protein oxidation in 77 healthy men whose typical diet contained few fruits and vegetables (mean of 2.6 servings/d). The 39 nonsmokers and 38 smokers, age 20- 51 y, were given a daily supplement (272 mg vitamin C, 31 mg all-rac-alpha-tocopherol, and 400 micro g folic acid), or placebo, for 90 d with their usual diet. Blood and urine were taken at baseline and the end of the study for determination of lipid peroxidation products, including F(2)-total and 8-isoprostanes, and protein carbonyls. Urine thiobarbituric acid reactive substances (TBARS) was the only oxidant damage marker that was significantly higher in smokers compared to nonsmokers (P < 0.05). Supplementation increased plasma ascorbate and tocopherol, but had no effect on the oxidant biomarkers. In healthy young men, the endogenous antioxidant defense system and a modest intake of dietary antioxidants are adequate to minimize levels of in vivo oxidant damage such that they cannot be differentiated by current methods.