Biology subjects

Kolbl, F.

Publications and source records attributed to Kolbl, F..

3 recordsLinked to original sources

bioRxiv2026

Selective Activation of Nerve Fiber Subpopulations with Intrafascicular Stimulation

BackgroundPeripheral nerve stimulation (PNS) is most effective when specific nerve fiber subpopulations are activated, while minimizing off-target activation, which may cause undesirable side effects. This selectivity depends primarily on electrode design and charge delivery. We hypothesized that selective PNS could be achieved through electrode placement and intrafascicular electric field steering using Longitudinal Intrafascicular Electrodes (LIFEs). MethodsLIFEs were implanted into the tibial fascicle of the sciatic nerve of 17 anesthetized adult rats. We tested whether electrodes positioned at different cross-sectional and longitudinal locations within the same fascicle, together with different electric field-steering approaches produced distinct activation patterns in the gastrocnemius lateralis muscle. Muscle responses were measured using high-density epimysial electromyography (HD-eEMG). ResultsElectrodes placed at different locations within the same fascicle activated distinct muscle regions, demonstrating intrafascicular selectivity. Bipolar stimulation recruited nerve fibers differently than monopolar stimulation, showing that electric field steering can further shape the selective recruitment. In both configurations, increasing the stimulation amplitude produced a graded increase in muscle activation. Furthermore, our findings demonstrated that HD-eEMG is an effective tool for evaluating intrafascicular selectivity. ConclusionThese findings suggest that improving on-target selectivity may support next-generation bioelectronic therapies with better outcomes and fewer side effects, potentially enabling more precise, organ-specific neuromodulation. Using multiple intrafascicular electrodes may provide two complementary strategies for enhancing selectivity: strategic intrafascicular placement to access different fiber subpopulations and bipolar configurations to steer recruitment beyond what a single electrode can achieve.

bioengineering↗

bioRxiv2025

Dynamical Diversity in Conductance-Based Neuron Response to kilohertz Electrical Stimulation

Neurons are notably rich in structure and functioning, so rather diverse in their response to stimuli. Consequently, the proper characterization of their dynamical response to external signals is a crucial step in understanding stimulation mechanisms. In particular, kilohertz (kHz) neuronal electrical stimulation tends to induce comportment and drives unseen in (more conventional) lower frequency ranges. Here, we investigate neuronal response of conductance-based models to kHz frequencies stimulation in a broad and often unexplored parameter space region. First, we show that the time evolution exhibited by the paradigmatic Hodgkin-Huxley model under kilohertz stimulation is highly diverse, ranging from regular spiking to chaotic dynamics, as well as displaying regions of complete activity suppression. However, to unveil all these features, a certain level of technical caution is required. For example, we demonstrate that common reductions of sodium dynamics become inaccurate under these frequency regimes. Also, based on suitable markers, we propose a method for mapping the mentioned behaviors on a stimulation parameter space. Second, by extending the study to models of mammalian central nervous system regions, a comprehensive dynamical atlas is obtained. It provides a rather systematic way to typify the response of rapidly forced conductance-based neurons. Thus, the present findings seems to point to an useful scheme for stimulation-based computational neuroscience research at kilohertz frequencies.

neuroscience↗

bioRxiv2024

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature. Author summaryElectrical stimulation of the peripheral nervous system is a powerful therapeutic approach for treating and alleviating patients suffering from a large variety of disorders, including loss of motor control or loss of sensation. Electrical stimulation works by connecting the neural target to a neurostimulator through an electrode that delivers a stimulus to modulate the electrical activity of the targeted nerve fiber population. Therapeutic efficacy is directly influenced by electrode design, placement, and stimulus parameters. Computational modeling approaches have proven to be an effective way to select the appropriate stimulation parameters. Such an approach is, however, poorly accessible to inexperienced users as it typically requires the use of multiple commercial software and/or development in different programming languages. Here, we describe a Python-based framework that aims to provide an open-source turnkey solution to any end user. The framework we developed is based on open-source packages that are fully encapsulated, thus transparent to the end-user. The framework is also being developed to enable simulation of granular complexity, from rapid first-order simulation to the evaluation of complex stimulation scenarios requiring a deeper understanding of the ins and outs of the framework.

neuroscience↗