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2016 Biomarkers Across Neurodegenerative Diseases Grant (BAND)

Cross-Disease Brain Image Modeling

Can a novel method for analyzing brain images be used to improve the early diagnosis of Alzheimer’s and Parkinson’s disease?

Yuanfang Guan, Ph.D.
University of Michigan
Ann Arbor, MI - United States


In the past decade, scientists around the world have collaborated on major research efforts with the goal of defining ways to diagnose brain diseases using imaging methods, such as magnetic resonance imaging (MRI). These efforts have greatly increased our understanding of how the brain changes during the early stages of Alzheimer’s and other neurodegenerative diseases.

For neurodegenerative diseases such as Alzheimer’s and Parkinson’s there can be an overlap of brain changes present in both diseases that can make diagnosing and differentiating the diseases challenging. To improve the usefulness of brain imaging, scientists are developing sophisticated methods for combining information about numerous features of brain images as a way to improve their accuracy and reliability for detection, diagnosis and differentiation of neurodegenerative diseases.

Research Plan

Yuanfang Guan, Ph.D., and colleagues have developed a sophisticated, computer-based method for analyzing brain images known as customized Gaussian Process Regression (cGPR). With this method the computer learns to recognize patterns of changes in brain images from people who have already been diagnosed with a neurodegenerative disease. The computer can than analyze these patterns and identify features that are predictive of different brain diseases. Dr. Guan and colleagues have demonstrated the success of this method in previous work. For their current studies, they will further test and refine the method using large databases of brain images from people known to have early-stage Alzheimer’s or Parkinson’s disease.


This research may lead to the development of sophisticated and sensitive methods for analyzing brain images that allow clinicians to more accurately diagnose Alzheimer’s disease and other brain diseases in their earliest stages. The researchers plan to make these methods publicly available so researchers and clinicians can use this tool to accelerate studies on the prediction and detection of different neurodegenerative diseases.

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