Streamlining neural signal detection for improved bladder control prostheses

Lower urinary tract dysfunctions can be caused by damage to the nervous system, such as spinal cord injuries or neurodegenerative disorders like Parkinson’s or Alzheimer’s. These can result in loss of bladder control, adding further challenges to an already complicated condition.

Neuroprosthetic devices can act as an effective treatment method for this condition. These devices are implanted and use electrical currents to stimulate a nearby nerve. Current neuroprostheses employ signals sent at regular intervals, known as open-loop stimulation. Giannotti et al. developed a method to receive nerve signals that can be incorporated into a closed-loop paradigm where the nerve stimulation is dependent on the state of the bladder.

“If you continuously stimulate a nerve, the nerves begin to adapt,” said author Alice Giannotti. “In the long term, the efficacy of this solution is no longer the same as in the beginning.”

In contrast, closed-loop paradigms only apply stimulation when needed, aiming to restore the physiological urination cycle. Unlike other closed-loop designs that measure bladder filling state with pressure sensors, the team used the intraneural electrode as a sensor to measure nerve activity. They employed machine learning to decipher the signals and accurately deduce bladder state.

In their in vivo tests, the authors demonstrated the ability to distinguish bladder filling states with over 85% accuracy. They plan to combine this technique with more targeted nerve stimulation to create a highly effective neuroprosthesis.

“The ultimate goal would be to have a fully implantable system to live within the patient,” said Giannotti. “To do that, we will move towards long-term experiments first in animals, and then hopefully implant the first devices in patients.”

Source: “Decoding bladder state from pudendal intraneural signals in pigs,” by Alice Giannotti, Sara Lo Vecchio, Stefania Musco, Leonardo Pollina, Fabio Vallone, Ivo Strauss, Valentina Paggi, Fabio Bernini, Khatia Gabisonia, Lucia Carlucci, Carla Lenzi, Andrea Pirone, Elisabetta Giannessi, Vincenzo Miragliotta, Stephanie P. Lacour, Giulio Del Popolo, Sara Moccia, and Silvestro Micera, APL Bioengineering (2023). The article can be accessed at https://doi.org/10.1063/5.0156484 .

This paper is part of the Implantable Bioelectronics Collection, learn more here .