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

Valle, Giacomo; Aiello, Giovanna; Ciotti, Federico; Čvančara, Paul; Martinović, Tamara; Kravic, Tamara; Navarro, Xavier; Bumbaširević, Marko; Raspopović, Staniša

doi:10.1016/j.biomaterials.2022.121874

Cited by 21 publications

(25 citation statements)

References 63 publications

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“… 47 A direct translation of FootSim to biomimetic policy creation needs to be investigated further in light of the limitations of available neurostimulating technologies. 48 Finally, developed stimulating paradigms could be transmitted to the neurostimulator through an appropriate transfer function. Potentially, such a biomimetic code encoded to nerves, through the neural implant, would be transmitted to the spinal cord and somatosensory cortex, enabling the transfer of the information about contact with the ground.…”

Section: Discussionmentioning

confidence: 99%

Modeling foot sole cutaneous afferents: FootSim

et al. 2023

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Section: Discussionmentioning

confidence: 99%

Modeling foot sole cutaneous afferents: FootSim

et al. 2023

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“…Multiple sensation characterizations have been performed throughout this period, with the purpose of collecting and analyzing the time evolution of the evoked sensations and their related stimulation parameters. Indeed, several changes in the optimal stimulation parameters were observed over time [63]. This was due to the implant's micromigrations, biological reactions (i.e.…”

Section: Somatosensory Neuroprosthesis Based On Neural Stimulationmentioning

confidence: 99%

“…A k-Means algorithm on the time series characterized by the evolution of the perceptual thresholds of all the AS taken into consideration has been performed as in [63]. The aim was to explore whether AS could be grouped based on their stability behavior with respect to their perceptual threshold change in time.…”

Section: Classification Of the Perceptual Thresholds Time Seriesmentioning

confidence: 99%

Recalibration of neuromodulation parameters in neural implants with adaptive Bayesian optimization

2023

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Objective. Neuromodulation technology hold promise to treat several conditions where physiological mechanisms of neural activity have been affected. Personalization of neurostimulation protocols is a mandatory step to make the treatment efficient and the devices highly effective. The interfaces between the targeted nervous tissue and the neurotechnology (i.e., human-machine link or neural interface) usually requires a constant re-calibration of the neuromodulation parameters, due to many different biological and microtechnological phenomena happening over-time. This adaptation of the optimal stimulation parameters generally involves an expert-mediated re-calibration, with correspondent 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 the 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 for two separate crucial scenarios where re-calibration is needed. In the first one, we built a model able to find the optimal active sites to use from 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). Main results. Our automatic algorithm can successfully adapt neurostimulation parameters to perceptual threshold changes and to evoked-sensation location changes over-time. These findings show an automatic and fast way of tackling the inevitable variability of neurostimulation parameters over time. It will potentially enable a widespread and a better usability of this technology upon validation in other frameworks, while decreasing the time and the costs of the treatment. This work suggests the exploitation of AI-based methods for developing the next generation of “smart” neuromodulation devices.

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“…In addition, hydrogels have been implanted directly into tissues for biomedical applications, for instance, to heal a damaged sciatic nerve for biomedical application. Due to their moisture-rich and conductive properties, hydrogels can be fixed in cells and tissues for recovery of the electrophysiological signal processing [ 15 , 16 ]. In addition to these applicable fields of the hydrogels, in recent years, research on biorobots has received a lot of attention because of their applicability in the biological and biomedical fields, including drug delivery, disease models, and sensing systems [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Nanomaterial-based biohybrid hydrogel in bioelectronics

Shin

Lim

et al. 2023

Nano Convergence

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Despite the broadly applicable potential in the bioelectronics, organic/inorganic material-based bioelectronics have some limitations such as hard stiffness and low biocompatibility. To overcome these limitations, hydrogels capable of bridging the interface and connecting biological materials and electronics have been investigated for development of hydrogel bioelectronics. Although hydrogel bioelectronics have shown unique properties including flexibility and biocompatibility, there are still limitations in developing novel hydrogel bioelectronics using only hydrogels such as their low electrical conductivity and structural stability. As an alternative solution to address these issues, studies on the development of biohybrid hydrogels that incorporating nanomaterials into the hydrogels have been conducted for bioelectronic applications. Nanomaterials complement the shortcomings of hydrogels for bioelectronic applications, and provide new functionality in biohybrid hydrogel bioelectronics. In this review, we provide the recent studies on biohybrid hydrogels and their bioelectronic applications. Firstly, representative nanomaterials and hydrogels constituting biohybrid hydrogels are provided, and next, applications of biohybrid hydrogels in bioelectronics categorized in flexible/wearable bioelectronic devices, tissue engineering, and biorobotics are discussed with recent studies. In conclusion, we strongly believe that this review provides the latest knowledge and strategies on hydrogel bioelectronics through the combination of nanomaterials and hydrogels, and direction of future hydrogel bioelectronics. Graphical Abstract

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

Cited by 21 publications

References 63 publications

Modeling foot sole cutaneous afferents: FootSim

Modeling foot sole cutaneous afferents: FootSim

Recalibration of neuromodulation parameters in neural implants with adaptive Bayesian optimization

Nanomaterial-based biohybrid hydrogel in bioelectronics

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