Diffusion Tensor Imaging with MRI May Help Analyze Alzheimer's Risk
A new study presented at RSNA 2018 finds potential in the neuroimaging technique as a test to predict the development of Alzheimer's disease.
No single diagnostic test currently exists that proves a person has Alzheimer’s disease, the most common form of dementia. Physicians look at an individual's medical history, perform neurological exams and mental status tests, and use MRI or CT to rule out other conditions. But a more definitive test that predicts the risk of cognitive decline due to Alzheimer's could help patients get access to treatment options and plan for the future.
In a new study, a team of researchers from Washington University in St. Louis and the University of California, San Francisco explores the use of diffusion tensor imaging for analysis of dementia risk. DTI is an MRI-based imaging technique that tracks the diffusion of water molecules along white matter tracts in the brain, as well as other fibrous tissues such as cardiac muscle and the prostate. The study was presented Nov. 25 at the annual meeting of the Radiological Society of North America in Chicago.
“The abnormalities in the brain that lead to Alzheimer's disease happen over the course of years before the first symptoms of memory loss appear,” said first author Cyrus Raji, assistant professor of radiology at the Mallinckrodt Institute of Radiology at Washington University in St. Louis. “This motivates the development of tools that can detect these changes much earlier.”
Raji and his colleagues recruited 61 subjects from the Alzheimer's Disease Neuroimaging Initiative, a longitudinal multicenter study designed to develop biomarkers for the early detection and tracking of Alzheimer's. Thirty individuals declined from normal cognition to Alzheimer's disease, while the remaining 31 remained cognitively normal. The average age of the cohort was 73.5 years, and it took an average of 2.6 years for patients to decline from normal cognition to Alzheimer’s.
All subjects received a single set of structural MRI scans along with DTI scans at the beginning of the study for a baseline measurement. For the DTI scans, the authors looked at a metric of white matter integrity called fractional anisotropy, which measures how well water molecules move along white matter tracts. A low value of fractional anisotropy likely indicates damage to the white matter.
“We used DTI because we hypothesized that we would see more subtle abnormalities than any other tools that are currently used in clinical practice,” said Raji. “In a typical clinical setting, they already use MRI to rule out organic causes of dementia like a brain tumor or stroke, so we could simply add a DTI scan to that.”
When analyzing the DTI scans of individuals who developed dementia compared to those who didn't, Raji and his colleagues found that baseline abnormalities could predict who would fall into which group. People who went on to have Alzheimer's disease had lower fractional anisotropy, which pointed to white matter damage.
Taking into account the fractional anisotropy along with other DTI metrics of all the white matter in the brain, the authors achieved 89 percent accuracy in predicting who would go on to develop Alzheimer’s disease. A more detailed analysis of specific white matter tracts in 40 patients led to a 95 percent accuracy. Cognitive questionnaires and genetic testing of the APOE4 gene -- a gene variant associated with a higher risk of Alzheimer's disease -- had accuracy values of 78 percent and 71 percent, respectively.
George Perry, a professor in neurobiology at the University of Texas at San Antonio who was not involved in the study, thought that the DTI-based dementia risk assessment technique appeared quite impressive despite being at the early stages of research. Clearly, a larger study would need to confirm these promising results, he said, but the methods of the study looked sound. Perry also said that the greatest value of such a technique would be in differential diagnosis, when there is a question of the patient having either Alzheimer's disease or another issue like depression.
“It is important that differential diagnosis can be accomplished with noninvasive technology that is broadly available. This technique fits that and provides a compelling differential in converters versus nonconverters,” said Perry. “Significantly the data shows the differential is in the white matter, which is an important and surprising insight as most [studies] have focused on the cortex.”