Simulation-enhanced High-density Magnetomyographic Quantum Sensor Systems for Decoding Neuromuscular Control During Motion

Introduction

Being able to decode neural signals that control skeletal muscles with high accuracy will enable scientific breakthroughs in diagnostics and treatment. This includes:

• Early detection of neurodegenerative diseases
• Optimising personalised treatment or gene therapy
• Assistive technologies like neuroprostheses

Technology Requirements

This breakthrough will require technology that is able to record signals from skeletal muscles in sufficient detail to allow the morpho-functional state of the neuromuscular system to be extracted. No existing technology can do this.

Magnetomyography (MMG)

Measuring the magnetic field induced by the flow of electrical charges in skeletal muscles, known as Magnetomyography (MMG), is expected to be the game-changing technology. This is because magnetic fields are not attenuated by biological tissue. However, the extremely small magnetic fields involved require extremely sensitive magnetometers.

Quantum Sensors

The only promising option is novel quantum sensors, such as optically pumped magnetometers (OPMs), because they are small, modular, and can operate outside of specialised rooms.

Project Vision

Our vision is to use this technology and our expertise in computational neuromechanics to decode, for the first time, neuromuscular control of skeletal muscles based on in vivo, high-density MMG data.

Prototype Development

For this purpose, we will:

1. Design the first high-density MMG prototypes with up to 96 OPMs
2. Develop custom calibration techniques

We will record magnetic fields induced by contracting skeletal muscles at the highest resolution ever measured.

Data Utilisation

Such data, combined with the advanced computational musculoskeletal system models, will allow us to derive robust and reliable source localisation and separation algorithms. This will provide us with unique input for subject-specific neuromuscular models.

Applications

We will demonstrate the superiority of the data over existing techniques with two applications:

1. Signs of ageing
2. Neuromuscular disorders

We will also show that it is possible to transfer these methodologies to clinical applications.