Posted in | Medical Sensor

Improved Wearable Brain Scanner Helps Understand and Diagnose Mental Illness

An advanced wearable brain scanner has exhibited a new potential to understand and diagnose mental illness following the expansion of the technology to scan the entire brain with millimeter precision.

A 49-channel whole head brain scanner. Image Credit: University of Nottingham.

In 2018, at the University of Nottingham, researchers designed an initial prototype of a new generation of brain scanner, a lightweight device that can be worn like a hat on the head and can scan the brain even when a patient moves.

The team's latest study has currently expanded this to a completely functional 49-channel device that can be employed to scan the entire brain and monitor the electrophysiological processes that are involved in several mental health problems. The study results have been reported in the Neuroimage journal.

Understanding mental illness remains one of the greatest challenges facing 21st century science. From childhood illnesses such as Autism, to neurodegenerative diseases such as Alzheimer’s, human brain health affects millions of people throughout the lifespan.

Matthew Brookes, Professor, University of Nottingham

Brookes continued, “In many cases, even highly detailed brain images showing what the brain looks like fail to tell us about underlying pathology, and consequently there is an urgent need for new technologies to measure what the brain actually does in health and disease.”

The brain cells function and communicate by synthesizing electrical currents. Such currents produce small magnetic fields that can be detected from outside the head. MEG is used to map the brain function by quantifying such magnetic fields. This enables acquiring a millisecond-by-millisecond image of which portions of the brain are engaged when several tasks, like moving or speaking, are performed.

In contrast to the huge unmanageable scanners where patients must stay very still, the wearable scanner enables patients to move freely. The first prototype of this system developed in 2018 included only 13 sensors and could only scan certain sections of the brain. With further advancements in 2019, first measurements could be performed in children.

The researchers collaborated with Added Scientific in Nottingham to design a new kind of 3D printed helmet, which is essential to the operation of the 49-channel device. The higher channel count implies that the system can be employed to scan the entire brain. It can reveal the brain regions that control the hand movement and vision located with millimeter precision.

Although there is exciting potential, OPM-MEG is a nascent technology with significant development still required. Whilst multi-channel systems are available, most demonstrations still employ small numbers of sensors sited over specific brain regions and the introduction of a whole-head array is an important step forward in moving this technology towards effective commercial application.

Ryan Hill, Study Lead Author, University of Nottingham

This latest whole head scanner opens the door for an array of applications, such as scanning children (who find it difficult to remain still) or scanning epileptic patients at the time of seizures to comprehend the abnormal activity of the brain that produces those seizures.

Our group in Nottingham, alongside partners at UCL, are now driving this research forward, not only to develop a new understanding of brain function, but also to commercialize the equipment that we have developed.

Matthew Brookes, Professor, University of Nottingham

Components of the scanner have already been sold, via industrial partners, to brain imaging laboratories across the world. It is thought that not only will the new scanner be significantly better than anything that currently exists, but also that it will be significantly cheaper,” concluded Brookes.

Journal Reference:

Hill, R. M., et al. (2020) Multi-Channel Whole-Head OPM-MEG: Helmet Design and a Comparison with a Conventional System. NeuroImage. doi.org/10.1016/j.neuroimage.2020.116995.

Source: https://www.nottingham.ac.uk/

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