Possible breakthrough in diagnosis and treatment ofautism. A multi-university research team co-led by Gustavo K. Rohdeprofessor of engineering at theUniversity of Virginiahas developed a system capable of identifying the genetic markers of autism in brain imaging with 89-95% accuracy.
The finding raises the possibility that doctors could identify, classify, and treat autism and related neurological conditions without having to wait for behavioral signals. This could lead to more rapid treatment.
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“Autism is traditionally diagnosed based on behavior, but it has a strong genetic basis. A genetics-based approach could transform understanding and treatment,” the researchers wrote in a paper published in the journal Science Advances.
Rohde, professor of biomedical, electrical and computer engineering, collaborated with researchers atUniversity of California San Francisco and of the Faculty of Medicine of the Johns Hopkins Universityincluding Shinjini Kundua former Rohde doctoral student and first author of the study.
While working in Rohde’s lab, Kundu, now a physician at Johns Hopkins Hospital, helped develop a generative computer modeling technique called Transport-based morphometry (TBM)which is at the heart of the team’s approach.
Using a new mathematical brain modeling technique, the system reveals patterns of brain structure that predict variations in certain regions of an individual’s genetic code: a phenomenon called “variations in the number of copies”in which segments of DNA are deleted or duplicated. These variations have been linked to autism. TBM allows researchers to distinguish normal biological variations in brain structure from those associated with deletions or duplications.
“Certain variations are known to be associated with autism,” Rohde said, “but their connection to brain morphology—in other words, how different types of brain tissue, such as gray or white matter, are arranged in our brains—is not well understood. Finding out how CNV relates to the morphology of brain tissue is an important first step in understanding the biological basis of autism.”
Transport-based morphometry is different from other machine learning image analysis models because it is based on mass transitor the movement of molecules such as proteins, nutrients, and gases into and out of cells and tissues.
“Most machine learning methods have little or no relationship to the biophysical processes that generate the data. Instead, they rely on pattern recognition to identify anomalies,” the researchers explained in their study.
But Rohde’s approach uses mathematical equations to extract mass transport information from medical images, creating new images for visualization and further analysis. Then, using a different set of mathematical methods, the system analyzes the information associated with the CNV variations linked to autism by other “normal” genetic variations that do not lead to neurological disease or disorder, what researchers call ‘confounding sources of variability.’
Previously, these sources prevented researchers from understanding the “gene-brain-behavior” relationshipeffectively limiting healthcare providers to behavior-based diagnoses and treatments. If more appropriate mathematical models were adopted to extract this information, important discoveries could be made from this enormous amount of data.
According to Forbes magazine, 90% of medical data is in the form of images, but there are no appropriate means to decipher them. Rohde believes that TBM is a master key: “From such huge amounts of data, important discoveries could be possible if we used more appropriate mathematical models.”
Nurse Times Editorial Team
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