Objective. Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nervous system and visceral organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop VN stimulation (VNS) would be crucial to increase effectiveness of this approach. Therefore, the extrapolation of useful physiological information from VN electrical activity would represent an invaluable source for single-target applications. Here, we present an advanced decoding algorithm novel to VN studies and properly detecting different functional changes from VN signals. Approach. VN signals were recorded using intraneural electrodes in anaesthetized pigs during cardiovascular and respiratory challenges mimicking increases in arterial blood pressure, tidal volume and respiratory rate. We developed a decoding algorithm that combines discrete wavelet transformation, principal component analysis, and ensemble learning made of classification trees. Main results. The new decoding algorithm robustly achieved high accuracy levels in identifying different functional changes and discriminating among them. Interestingly our findings suggest that electrodes positioning plays an important role on decoding performances. We also introduced a new index for the characterization of recording and decoding performance of neural interfaces. Finally, by combining an anatomically validated hybrid neural model and discrimination analysis, we provided new evidence suggesting a functional topographical organization of VN fascicles. Significance. This study represents an important step towards the comprehension of VN signaling, paving the way for the development of effective closed-loop VNS systems.

Objective: Ultrasound (US) stimulation carries the promise of a selective, reversible, and non-invasive modulation of neural activity without the need for genetic manipulation of neural structures. However, the mechanisms of US-induced generation of action potentials (APs) are still unclear. Methods: Here we address this issue by analyzing intracellularly recorded responses of leech nociceptive neurons to controlled delivery of US. Results: US induced a depolarization linearly accumulating in time and outlasting the duration of the stimulation. Spiking activity was reliably induced for an optimal US intensity range. Moreover, we found that APs induced by US differ in smaller amplitude and faster repolarization from those induced by electrical stimulation in the same cell but display the same repolarization rate. Conclusions: These results shed light on the mechanism by which spikes are induced by US and pave the way for designing more efficient US stimulation patterns.

21Bioelectronic medicine is opening new perspectives for the treatment of some major chronic 22 diseases through the physical modulation of autonomic nervous system activity. Being the 23 main peripheral route for electrical signals between central nervous system and visceral 24 organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop 25 neuromodulation would be crucial to increase effectiveness and reduce side effects, but it 26 depends on the possibility of extracting useful physiological information from VN electrical 27 activity, which is currently very limited. 28 Here, we present a new decoding algorithm properly detecting different functional changes 29 from VN signals. They were recorded using intraneural electrodes in anaesthetized pigs 30 during cardiovascular and respiratory challenges mimicking increases in arterial blood 31 pressure, tidal volume and respiratory rate. A novel decoding algorithm was developed 32 combining discrete wavelet transformation, principal component analysis, and ensemble 33 learning made of classification trees. It robustly achieved high accuracy levels in identifying 34 different functional changes and discriminating among them. We also introduced a new 35 index for the characterization of recording and decoding performance of neural interfaces. 36Finally, by combining an anatomically validated hybrid neural model and discrimination 37 analysis, we provided new evidence suggesting a functional topographical organization of 38 VN fascicles. This study represents an important step towards the comprehension of VN 39 signaling, paving the way to the development of effective closed-loop bioelectronic systems. 40 41 Algorithm, Hybrid Modeling Framework. 44 45 46 48 of body homeostasis. In ANS peripheral nerves, afferent and efferent fibres run together, 49 providing bidirectional communication between specific circuits of the central nervous 50 system and visceral organs. The artificial modulation of this complex circuitry is the 51 challenging goal of bioelectronic medicine (BM), a highly promising alternative to some 52 limited pharmacological tretments 1-3 . Among the main ANS nerves, the vagus nerve (VN) 53 represents a privileged target as it modulates vital functions like respiration, circulation and 54 the digestion 4 . VN stimulation (VNS) of cervical segments has shown a great potential for 55 the treatment of a wide range of pathological conditions such as epilepsy 5 , chronic heart 56 failure 6 , and inflammatory diseases 7,8 . However, the formidable amount of afferent and 57 efferent signals that simultaneously cross this VN segment, the numerous VNS side-effects 9 58 and the discovery of VN involvement in the regulation of complex functions like immunity 10 59 or central neuroplasticity 11,12 highlight the need for high precision and selectivity. In an ideal 60 scenario, the therapeutic stimulation or inhibition of VN or any other ANS nerve should be: 61 a) selectively directed to specific efferent or afferent fibres and b) regulated by a ...

Thoracic Endovascular Aortic Repair (TEVAR) is the preferred treatment option for thoracic aortic pathologies and consists of inserting a self-expandable stent-graft into the pathological region to restore the lumen. Computational models play a significant role in procedural planning and must be reliable. For this reason, in this work, high-fidelity Finite Element (FE) simulations are developed to model thoracic stent-grafts. Experimental crimp/release tests are performed to calibrate stent-grafts material parameters. Stent pre-stress is included in the stent-graft model. A new methodology for replicating device insertion and deployment with explicit FE simulations is proposed. To validate this simulation, the stent-graft is experimentally released into a 3D rigid aortic phantom with physiological anatomy and inspected in a computed tomography (CT) scan at different time points during deployment with an ad-hoc set-up. A verification analysis of the adopted modeling features compared to the literature is performed. With the proposed methodology the error with respect to the CT is on average 0.92 ± 0.64%, while it is higher when literature models are adopted (on average 4.77 ± 1.83%). The presented FE tool is versatile and customizable for different commercial devices and applicable to patient-specific analyses.

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