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New Zealand Develops New Diagnostic and Treatment for Anxiety Disorders

A researcher at the University of Otago has discovered a brain signal that will facilitate the diagnosis and treatment of anxiety disorders. The researcher and his team from the Department of Psychology have completed the final stage of testing their biomarker, a brain rhythm caused by emotional conflict, in anxiety patients. The study, which was funded by the New Zealand Health Research Council and is now published in Nature’s Scientific Reports, discovered that patients with anxiety disorders had high conflict rhythmicity, which varied in severity across diagnoses.

He noted that those with high scores have a specific type of anxiety disorder that is more likely to respond to specific anti-anxiety medications. Those with particularly high scores, on the other hand, will be resistant to conventional treatment. The new biomarker may aid in the development of new diagnostic tests and treatments for anxiety disorders, allowing patients to achieve remission faster with a less hit-and-miss choice of treatments.

The researcher added, “Our biomarker provides an immediate foundation for the development of new drugs and for diagnostic tests that could guide treatment choice and so improve response rates.

In the long run, the research’s underlying theory should allow for similar advances in a variety of threat and stress-related disorders. This could mark a watershed moment in the application of theoretical neuroscience to psychiatry, as well as the current trend of combining psychopathology and personality theory. According to the researcher, better treatment for anxiety disorders will help reduce the burden on patients, their families, workplaces, and governments by lowering healthcare costs.

In addition, with a $250,000 Emerging Researcher grant from the Health Research Council of New Zealand (HRC), psychology lecturers at the University of Otago also planned to develop a neuroimaging model that can predict ADHD-related cognitive deficits in children by analysing detailed MRI brain scans. His team hopes to capture the characteristics of the disorder from a biological standpoint over the next three years.

The team of researchers will use a large dataset of over 10,000 children from the United States (both with and without ADHD). This dataset contains various types of brain images that reflect children’s brain activity, connectivity, and anatomy, as well as cognitive abilities based on various tasks.

The team will first investigate the cognitive differences that occur in children who have been diagnosed with ADHD (predetermined through cognition tests with participants). They will then use specially designed machine-learning algorithms to match the brain images in the dataset with these cognitive differences. This will yield a brain-based ‘predictive score’ for each child in the dataset, indicating their risk of developing ADHD-related cognitive deficits.

This brain-based predictive approach will then be tested on New Zealand children who have been formally diagnosed with ADHD. Functional and structural MRI scans of participants in Dunedin will be taken, and the resulting scores matched against each child’s cognitive differences.

It is mentioned that the availability of such big data, combined with modern computation techniques such as machine learning, is one of the most exciting advances in psychiatry at this time, and will help generate a novel platform for neuroimaging and psychiatry.

One of the future benefits of a reliable biomarker would be the ability to test the efficacy of novel treatments that target cognitive functions. The biomarker, according to the researcher, will assist researchers in determining whether treatments affect cognitive areas of the brain. According to the Health Research Council’s chief executive, this project is a great example of how data-driven research can help address critical knowledge gaps in health.

OpenGov Asia in an article reported that some of the common goals and objectives of healthcare sector digitisation may include improved patient record-keeping, faster diagnosis, use of machine learning and AI capabilities, disease prevention, personalised medicine, and all at a reasonable cost that meets budget constraints. Overall, these initiatives benefit both the health organisation and their patients – patients feel more in control of their health, technology lowers operating costs and broadens accessibility, and the healthcare system runs smoothly and efficiently.


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