Advances in neuroscience: How important is technology?

17 August 2023

Technology is evolving the field of neuroscience faster than ever, from AI capable of identifying brain injuries to brain implants helping people communicate. At Wave, we have been supporting clients in neuroscience for almost ten years and have a wealth of expertise as well as a passionate interest in developments in the field. In this article, Wave Medical Writer, Laura Smith, delves deeper into three of the latest technology-related trends in neuroscience.


    How is AI transforming neuroscience?

AI continues to innovate healthcare, but how can it be used to benefit neuroscience in particular? Researchers at the University of Cambridge and Imperial College London have developed a machine learning tool based on an artificial neural network to identify different types of brain injuries and abnormalities.1

By using large sets of imaging data, the tool has been trained to efficiently detect, quantify and differentiate different types of brain lesions.1

So, if we can program AI to automatically and consistently detect subtle changes in brain injuries over time, what does this mean for clinical practice? Equipped with this AI tool, neurologists may be able to automatically monitor brain injuries and understand why they progress. This opens up the possibility to improve patient stratification and management, investigate potential causes and risk factors, and develop personalised treatments.1,2

At the University of Pittsburgh, neurotrauma surgeons are using AI technology and automated brain scans to inform outcomes in patients with severe traumatic brain injury. This prognostic model can predict patients’ survival and recovery at six months post-injury to improve patient care.3

Another novel AI tool, called CRANK-MS, has been developed by researchers to identify people who will go on to develop Parkinson’s disease – with up to 96% accuracy. The algorithm analysed patients’ blood and identified several molecules that may serve as early biomarkers of Parkinson’s, including unique combinations of metabolites, to allow for earlier therapeutic intervention.4

With further validation, researchers hope to use AI tools in a hospital setting. Whether this be to help when resources are limited and fewer consultants are available, to increase turnaround time in emergency rooms or to develop personalised treatment algorithms, AI in neurology has an exciting future.2


   Brain implants and communication

Most of us are aware of prostheses to functionally replace a missing limb, but how many of us are aware of neuroprostheses?

Neuroprostheses act as a brain-machine interface, relying on a brain implant, to combine neural processing with prosthetics and is another exciting technological advancement transforming the field of neuroscience.5

By developing a neuroprosthetic brain implant, researchers at UC San Francisco have successfully enabled a man with severe paralysis to communicate in sentences, by translating signals from his brain and decoding this to appear as text on a screen.6

A multielectrode device was implanted into the brain of a man with anarthria (the inability to articulate speech) and spastic quadriparesis caused by a brain stem stroke. By using deep-learning algorithms, researchers created a computational model to decode words and sentences directly from patterns of activity recorded in the cerebral cortex.6

Over 81 weeks, the implant efficiently and accurately used the man’s neural activity to produce words from a 50-word set. The median number of words decoded per minute was 15.2 and the median decoding accuracy of sentences was 74.4%.6

Further research is needed to investigate whether this neuroprosthetic tool can be adopted for real-world use, but this represents an exciting advancement for assisted communication.6

Schematic illustration of a brain–machine interface. Adapted from: Salahuddin et al. 2021.7

It’s not only verbal communication that brain implants can be applied to. Researchers at the University of Utah and Miguel Hernandez University successfully created a form of artificial vision for a blind woman by implanting a microelectrode array into her brain.8

The implant was placed in the visual cortex to record, replay, and stimulate the electrical activity of neurons communicating in the brain. Eyeglasses, equipped with a miniature video camera, collected visual data to be encoded by specialised software and sent to the microelectrode array. This stimulated neurons to produce phosphenes, perceived by the patient as white points of light, to create an image.8

After being fully blind for 16 years, this implant partially restored the patient’s vision, and she could identify lines, shapes, and simple letters evoked by different stimulation patterns.8

This exciting advancement highlights the high potential for using brain implants to improve the lives of patients with long-term, life-debilitating conditions.


   Using neuroimaging to understand brain interactions

New approaches to traditional technology are also transforming neuroscience.

Neuroimaging has been used for decades to study brain function in health and disease and a new approach to magnetic resonance imaging (MRI) has highlighted the possibility of noninvasively tracking the propagation of brain signals on millisecond timescales.9,10

Researchers have developed a technique called ‘direct imaging of neuronal activity (DIANA)' that uses MRI technology to take a series of quickfire, partial images that are combined to create a high-resolution picture of active brain regions over time in mice.10,11

DIANA is capable of high-resolution, direct imaging of neuronal activity and has the possibility of being taken into patient studies and opening up a whole world of research by providing a deeper understanding of the brain in health and disease.10,11


   So, what does the future hold for neurotechnology?

Neurotechnology holds enormous possibilities and is already positively impacting the field of neuroscience. It is a fascinating field, and we are excited to see how technology can be used to catalyse neuroscience discoveries and solve important clinical questions to improve patient outcomes across several diseases.


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