Tech & Tools
Machine Learning IDs Markers to Help Predict Alzheimer’s
Nearly 50 million people worldwide have Alzheimer’s disease or another form of dementia. While no cure exists, medications can delay the progression of symptoms for several years, extending the quality of life for patients. However, in order for these medications to be effective, the disease must be diagnosed at an early stage. Current research suggests that the brain damage associated with Alzheimer’s likely starts a decade or more prior to symptom onset.
Recently, a team from the U.S. Department of Energy’s Brookhaven National Laboratory, Columbia University Medical Center, Stony Brook University, and Ilsan Hospital in South Korea has shown that a combination of two different modes of magnetic resonance imaging (MRI), computer-based image analysis, and image classification using machine learning models may be a promising approach to accurately predicting Alzheimer’s risk.
“Such multimodal imaging analysis can enhance predictive power by identifying key diagnostic markers of the disease,” says team member Shinjae Yoo, PhD, a computational scientist in Brookhaven Lab’s Computational Science Initiative.
Brain Images Reveal Changes
Within the past decade, scientists have started investigating whether a different mode of MRI—called diffusion MRI—could provide additional information for physicians to improve predictive power. Diffusion MRI captures how water molecules move around in biological tissues, and mapping this diffusion process can reveal subtle changes in tissue microstructure.
Using diffusion MRI, scientists have found abnormalities in the white matter—a type of brain tissue—in patients with Alzheimer’s disease. White matter, which lies beneath the cortex, consists of millions of bundled nerve fibers (axons) that connect neurons in different gray matter regions of the brain—a structured network that runs like tracts of communication cables across the brain. The white color comes from the fatty layer of electrical insulation (myelin) that coats the axons, allowing them to more quickly send nerve impulses across the brain. Gray matter has relatively few myelinated axons, so it takes on the color of the nerve cell bodies of which it is composed.
A New Research Direction
Preliminary research suggests that the integrity of white matter declines in those at risk of Alzheimer’s disease. On MRI scans, the degeneration appears as bright white spots called hyperintensities. However, scientists are unsure to what extent the white matter “structural connectome”—the brain’s wiring system, or the unique pattern of connections between the billions of neurons in the brain—carries additional information for Alzheimer’s risk beyond what is shown by morphometry analysis based on structural MRI.
Prediction Powered by Machine Learning
To process and analyze the raw images, the team members designed a rigorous pipeline consisting of several existing algorithms. Next, they applied machine learning to train image-derived classification models on the brain “phenotypes” resulting from their analysis—estimations of brain shapes and volumes (morphometric data) and of white matter structural connectivity (tractography data) in patients from each diagnostic category. They then used the models to make predictions of diagnosis.
“In one study using data from a dementia clinic, we achieved up to 98% accuracy in detecting Alzheimer’s disease and 84% accuracy in predicting mild cognitive impairment, the precursor to Alzheimer’s,” says team member Jiook Cha, PhD, a research scientist and assistant professor of neurobiology in the department of psychiatry at Columbia University Medical Center. “The accuracy of our machine learning models trained on brain connectome estimates surpassed that of existing imaging-based markers used in clinical settings (e.g., white matter hyperintensities) by 10% and 29%, respectively. Using independent data from the Alzheimer’s Disease Neuroimaging Initiative, we replicated these results.”
By comparing the performance of their different machine learning models, the team members determined that the structural connectome may be a clinically useful imaging marker for Alzheimer’s disease.
“The model trained on both morphometric and connectome data more accurately classified Alzheimer’s disease and mild cognitive impairment than the model trained on morphometric data alone,” Yoo explains. “In addition, the connectome model classified mild cognitive impairment and subjective cognitive decline as accurately as the combined model—unlike the morphometry model, which did not classify accurately.”
These results suggest that diffusion MRI could be a valuable tool in the early detection of Alzheimer’s disease. Neuroscientists believe that mild cognitive impairment and subjective cognitive decline are precursors to Alzheimer’s, so abnormal changes in white matter that are detected in these preclinical stages could indicate patients who are at an increased risk of eventually developing Alzheimer’s. The ability to identify such microscopic changes years before more severe macroscopic changes set in could lead to better treatments and possibly even a cure.
“This study strongly indicates the feasibility of using multimodal MRI—particularly diffusion MRI–based analysis of the structural connectome—to accurately predict Alzheimer’s risk,” Cha says.
Follow-on studies based on retrospective patient data will further assess whether this approach could be implemented in clinical settings.
— Source: Brookhaven National Laboratory