Researchers have developed a technique that accurately identifies genetic markers of autism in brain images, which could revolutionize early diagnosis and treatment.
A team of researchers co-led by University of Virginia engineering professor Gustavo K. Rohde has developed a system that can spot genetic markers of autism in brain images with 89 to 95% accuracy.
Their research, published in the journal Science Advances, indicates that doctors could use this method to see, classify, and treat autism and related neurological conditions without relying on or waiting for behavioral cues, potentially leading to earlier interventions.
“Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism,” the researchers explained.
Collaborative Research and Technique Development
Rohde, a professor of biomedical and electrical and computer engineering, collaborated with researchers from the University of California San Francisco and the Johns Hopkins University School of Medicine, including Shinjini Kundu, Rohde’s former Ph.D. student and first author of the paper.
While working in Rohde’s lab, Kundu — now a physician at the Johns Hopkins Hospital — helped develop a generative computer modeling technique called transport-based morphometry, or TBM, which is at the heart of the team’s approach.
Using a novel mathematical modeling technique, their system reveals brain structure patterns that predict variations in certain regions of the individual’s genetic code — a phenomenon called “copy number variations,” in which segments of the code are deleted or duplicated. These variations are linked to autism.
Understanding Autism’s Genetic and Morphological Links
TBM allows the researchers to distinguish normal biological variations in brain structure from those associated with the deletions or duplications.
“Some copy number variations 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.”
Advancements in Morphometric Analysis
Transport-based morphometry differs 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. Instead, they rely on recognizing patterns to identify anomalies. However, Rohde’s approach uses mathematical equations to extract the mass transport information from medical images, creating new images for visualization and further analysis.
Then, using a different set of mathematical methods, the system parses information associated with autism-linked CNV variations from other “normal” genetic variations that do not lead to disease or neurological disorders — what the researchers call “confounding sources of variability.”
Implications for Future Autism Research and Treatment
These sources previously prevented researchers from understanding the “gene-brain-behavior” relationship, effectively limiting care providers to behavior-based diagnoses and treatments.
According to Forbes magazine, 90% of medical data is in the form of imaging, which we don’t have the means to unlock. Rohde believes TBM is the skeleton key.
“As such, major discoveries from such vast amounts of data may lie ahead if we utilize more appropriate mathematical models to extract such information.”
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 localized changes in brain morphology linked to copy number variations, could point to brain regions and eventually mechanisms that can be leveraged for therapies,” Rohde said.
Reference: “Discovering the gene-brain-behavior link in autism via generative machine learning” by Shinjini Kundu, Haris Sair, Elliott H. Sherr, Pratik Mukherjee and Gustavo K. Rohde, 12 June 2024, Science Advances.
DOI: 10.1126/sciadv.adl5307
The research received funding from the National Science Foundation, the National Institutes of Health, the Radiological Society of North America, and the Simons Variation in Individuals Foundation.
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