Research that was published in Frontiers in Physiology has found that communication among different regions in the brain, which is referred to as brain connectivity, could possibly serve as a biomarker for attention-deficit hyperactivity disorder (“ADHD”). For the study, researchers from the University of Buffalo in collaboration with the University of Cincinnati used machine-learning classifiers to identify grown-ups who had been diagnosed with ADHD during their childhood with 99% accuracy.
The researchers note that these findings may be useful in helping clinicians find proper treatments that address the specific needs for each patient and may also increase the ease with which this disorder is diagnosed. In a press release, Chris McNorgan, who was part of the study, stated that understanding the different ADHD types could assist clinicians in making decisions concerning the medications used for treatment, as some pharmaceuticals only react with specific pathways.
Despite ADHD being the most commonly diagnosed psychological disorder among young children, its identification and diagnosis is often challenging.
Diagnostics are made difficult by the disorder’s multiple subtypes, with researchers finding that the disorder’s clinical diagnosis may change when the same patient visits the clinician for a follow-up evaluation. In the release, McNorgan explained that patients could exhibit behavioral symptoms that were consistent with the disorder one day but not present those symptoms or present them to a different degree at a later date.
The researchers used fMRI data obtained from 80 adult participants who had received a childhood diagnosis of ADHD. They applied machine-learning classifiers to four activity snapshots that were taken while participants were carrying out a task designed to test their ability to impede an automatic response.
In their report, the researchers stated that focused analysis of individual runs reached 91% with collective analysis attaining 99% diagnostic accuracy. In his statement, McNorgan added that this was the highest accuracy reported in any study that focused on this disorder and exceeded anything achieved with behavioral assessments. He asserted that there were many factors that contributed to the research’s superior classification performance.
According to the researchers, this accuracy may be ascribable to the application of deep learning networks, which can better detect conditional relationships in comparison with direct linear classification. The authors note that since their study was designed to predict attention-deficit hyperactivity disorder based on the communication patterns between groups of brain areas, using deep-learning classifiers may have contributed to their diagnostic model’s success, given that the connections often require multiple factors to be considered in contrast with linear correlation.
The field of neurology diagnostics is advancing at a high rate. Many companies, including Brain Scientific Inc. (OTCQB: BRSF), are hard at work developing and commercializing technologies aimed at making diagnosing neurological conditions easier and more accurate as well as more affordable.
NOTE TO INVESTORS: The latest news and updates relating to Brain Scientific Inc. (OTCQB: BRSF) are available in the company’s newsroom at https://ibn.fm/BRSF
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