New genetic markers system could transform autism diagnosis

While it is estimated that worldwide about one in 100 children has autism, a new system that detects genetic markers in brain images with 89-95% accuracy, potentially enabling earlier diagnosis and treatment, will be welcomed widely as it could spare families years of uncertainty. The system, which identifies brain structure patterns linked to autism-related genetic variations, offers a personalised approach to autism care. The technique, called transport-based morphometry, or TBM, could transform the understanding and treatment of autism by focusing on genetic markers rather than behavioural cues.The findings of a multi-university research team, co-led by University of Virginia engineering professor Gustavo K Rohde, suggest doctors may one day see, classify and treat autism and related neurological conditions with this method and result in earlier interventions."Autism is traditionally diagnosed behaviourally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism,” the researchers wrote in a paper published June 12 in the journal Science Advances.Rohde, a professor of biomedical and electrical and computer engineering, collaborated with researchers from the University of California San Franscisco and the Johns Hopkins University School of Medicine, including Shinjini Kundu, Rohde’s former PhD student and first author of the paper. It was while working in Rohde’s lab that Kundu — now a physician at the Johns Hopkins Hospital — helped develop a generative computer modelling technique, TBM, which is at the heart of the team’s approach.The new method starts out with standard brain-mapping via magnetic resonance imaging before re-analysing those scans via AI to detect the movements of proteins, nutrients and other processes within the brain that may indicate autism. TBM reveals brain structure patterns that predict variations in certain regions of the individual’s genetic code — a phenomenon called "copy number variations (CNV),” in which segments of the code are deleted or duplicated. These variations are linked to autism. "Some CNV are known to be associated with autism, but their link to brain morphology — in other words, how different types of brain tissues such as gray or white matter, are arranged in our brain — is not well known,” Rohde said. Finding out how CNV relates to brain tissue morphology is an important first step in understanding autism’s biological basis.TBM is different from other machine learning image analysis models because the mathematical models are based on mass transport — the movement of molecules such as proteins, nutrients and gases in and out of cells and tissues. ‘Morphometry’ refers to measuring and quantifying the biological forms created by these processes. Most machine learning methods, Rohde said, have little or no relation to the biophysical processes that generated the data. They rely instead on recognising patterns to identify anomalies. But Rohde’s approach uses mathematical equations to extract the mass transport information from medical images, creating new images for visualisation and further analysis.The researchers used data from participants in the Simons Variation in Individuals Project, a group of subjects with the autism-linked genetic variation. Control-set subjects were recruited from other clinical settings and matched for age, sex, handedness and non-verbal IQ while excluding those with related neurological disorders or family histories."We hope that the findings, the ability to identify localised changes in brain morphology linked to CNV, could point to brain regions and eventually mechanisms that can be leveraged for therapies,” Rohde said.Additional co-authors are Haris Sair of the Johns Hopkins School of Medicine and Elliott H Sherr and Pratik Mukherjee of the University of California San Francisco’s Department of Radiology. The research received funding from the National Science Foundation, National Institutes of Health, Radiological Society of North America and the Simons Variation in Individuals Foundation.