Neurotech roundup: Video games for wrist rehab, new algorithm helps tailor MS treatments, and more

Neuro Rehab Times explores the latest developments in the world of neurotech.

New MRI technique can help predict and guide stroke patients’ recovery

A new Georgetown University Medical Center study in collaboration with MedStar Health and the National Institutes of Health exploring a new brain imaging technique is bringing stroke experts a step closer to better tailoring rehabilitation.

Neurologists often use MRI images of the brain’s white matter to glean information about a person’s ability to recover, but a new imaging technique added to MRI allows clinicians to better see the condition of white matter tracts leading to the limbs, an observation usually only seen after death during an autopsy.

The imaging technique is called diffusion tensor-based morphometry (DTBM). It combines directional information about the structures in the brain with the shape and size of the structures being imaged.

Previously it was difficult to separate the white matter cables from the grey matter using morphometry approaches because they did not include the directional information. This two-part technique allows researchers to map and quantify changes over time in the white matter tracts.

Simple algorithm helps improve treatment and reduce disparities in Multiple Sclerosis

A simple treatment algorithm may help reduce treatment disparities for Hispanic and Black people with multiple sclerosis (MS), according to a preliminary study.

The programme uses an algorithm to determine the best disease-modifying treatments for people with MS by using readily available clinical factors such as weakness and bladder dysfunction. It also considers social factors such as out-of-pocket costs, transportation barriers, childcare and work schedules, but not race and ethnicity.

The researchers say that studies show Hispanic and Black people have higher levels of disability than white people but are not given prescriptions for the newer, more effective treatments as often as white people are, and this algorithm is a straightforward way to rapidly increase the use of these medications.

The intervention can match people to newer treatments that are highly effective at reducing MS relapses, including medications like natalizumab, rituximab and ofatumumab. Relapses are when MS symptoms like numbness, weakness, stiffness or vision problems appear for at least 24 hours. Because some of these drugs are expensive, not everyone with MS may be able to use them, which can widen health disparities.

Collaboration aims to discover CNS therapeutics using generative AI

AI-driven biotech company Insilico Medicine and Tenacia Biotechnology are entering into a research collaboration focused on the early discovery stage of novel Central Nervous System (CNS) disease therapies. The pair will be developing small molecule inhibitors from scratch to advance to preclinical candidate nomination.

The collaboration will combine Insilico’s Pharma.AI, a generative AI-based drug discovery platform, and its extensive research and development expertise with Tenacia’s specialised scientific and clinical knowledge.

The joint effort will focus on creating transformative treatments for Central Nervous System (CNS) disorders, with a particular emphasis on developing blood-brain barrier (BBB) penetrable small molecule inhibitors.

The partnership seeks to expand therapeutic options and enhance outcomes for patients worldwide.

Two video games created to improve hand and wrist rehabilitation

A system of exercise video games (or exergames) that promotes the rehabilitation of people with mobility problems in their hands and wrists has been developed.

The system provides data to therapists so that they can analyse their patients’ progress during the recovery stage.

The two video games, called “Peter Jumper” and “Andromeda”, have been developed on the free Unity platform and are arcade-type games (i.e., games similar to arcade machines). Their aim is to make the physical activity of the injured limb rewarding, generating motivation so that the patient can become more involved in the treatment and enhance the results of the rehabilitation.

In addition to the software, the system is composed of a specialised electromechanical controller, called “eJamar”, which is capable of measuring, through specialized sensors, the entire range of motion of the hand and wrist, as well as the patient’s grip strength.

The system, in turn, is able to store this information during each session, so that a specialist can consult the patient’s condition and check their progress over time, automatically recording metrics (strength profiles, fatigue, reaction times, etc.) that cannot be obtained using traditional methods.

An advantage of this device is that it can be used in a wide range of cases, from fractures or hand injuries to neurological pathologies such as stroke, multiple sclerosis or Parkinson’s disease.

Innovative voice-based approach enables early Alzheimer’ s detection

Researchers have developed a novel multi-task learning framework, called DEMENTIA, to improve the early detection and assessment of Alzheimer’s disease (AD).

Language decline is one of the first signs of cognitive deterioration. While automated speech analysis offers a non-invasive, cost-effective way to detect Alzheimer’ s, current methods often struggle with complexity, poor interpretability, and limited use of multiple data types, reducing accuracy and clinical application.

The DEMENTIA framework has been designed to address these challenges. It integrates speech, text, and expert knowledge with a hybrid attention mechanism, enhancing both accuracy and clinical interpretability in AD detection.

By leveraging large language model technologies, the framework captures complex intra- and inter-modal interactions, improving AD detection accuracy and enabling the prediction of cognitive function scores.

Additionally, comprehensive interpretability analyses showed the model’s strong clinical decision-support capabilities and robustness across different datasets.

These findings highlight the potential of speech-based tools for early AD screening and cognitive decline monitoring, offering significant scientific and societal value in tackling the challenges of an ageing population.

Consumer devices can be used to assess brain health

Apps designed for smartphones and wearable devices can provide unique insights into users’ brain health, a new study shows.

The study has found widely used consumer grade digital devices, such as the iPhone and Apple Watch, can be effective in assessing an individual’s cognitive health without requiring in-person visits or supervision. This is the largest cognition study of its kind to demonstrate that self-administered cognitive assessments can be leveraged to accurately assess cognitive health over time.

Of the participants enrolled, over 90 per cent were able to adhere to the study protocol for at least one year, which included using an iPhone and wearing an Apple Watch on a daily basis, as well as taking cognitive assessments on their own and completing questionnaires on a monthly and quarterly basis.

The researchers found that self-administered digital cognitive assessments were reliable and clinically valid across the broad populations enrolled. The ability to accurately measure cognitive health remotely could be the first step in providing individuals with the information they need to take action on their brain health.

Brain waves measured during sleep predict cognitive impairment years before symptoms appear

Researchers have developed an AI tool that analyses brain wave activity recorded during sleep using electroencephalography (EEG), a non-invasive technique that measures electrical activity in the brain through sensors placed on the scalp.

The AI tool was developed using sleep study data from a group of women over 65, who were tracked for five years.

The researchers identified subtle differences in brain wave patterns that predicted which participants would later be diagnosed with cognitive impairment, suggesting that wearable EEG devices could help identify individuals at risk for dementia, paving the way for earlier interventions.