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Multiple Neuroimaging Modality Risk Score for Predicting Alzheimer’s Disease Diagnosis
Neuroimaging plays a key role in the diagnosis and management of Alzheimer’s disease (AD) and other forms of dementia. However, imaging is usually limited to a single modality and analyzed alone rather than in conjunction with other clinical and genetic data. The clinical utility of combining multiple neuroimaging modalities with genetic and other data remains unknown and of significant importance to both clinical management decisions and the design of clinical trials.
In his 2017 Canon USA/RSNA Research Medical Student Grant, “Development of a Multi-modal Imaging Risk Gradient Score for Alzheimer’s Disease Prediction,” Luke Bonham, BS, University of California, San Francisco (UCSF), investigated the role of multimodal imaging (structural MRI and amyloid PET) and common genetic variants as predictors of AD risk in cognitively normal individuals.
Bonham led a collaboration between teams at UCSF and the University of California, San Diego (UCSD). Utilizing structural MRI and amyloid PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, the team identified brain regions from each modality that were especially vulnerable to AD.
Bonham combined data from the most sensitive MRI and PET regions into a continuous multimodal imaging risk gradient (MIRG) score that captured the AD-related changes that occur when a person progresses from health to disease. In cognitively normal older adults, the MIRG score predicted progression to mild cognitive impairment (MCI) and from MCI to Alzheimer’s disease. Taking the MIRG score a step further, the team demonstrated that incorporating the MIRG with an individualized AD genetic risk score, (Desikan et al, 2017; Plos Medicine), improved their ability to identify individuals most at risk for developing mild cognitive impairment and AD.
“Our results indicate that imaging and genetics are complementary information sources and can help identify those most at risk for cognitive decline,” Bonham said. “The genome offers a clear view into an individual’s baseline risk for Alzheimer’s disease while neuroimaging captures a ‘snapshot’ of where a person stands along the spectrum of normal cognitive aging to neurodegenerative disease. These findings are promising and we are working to translate them into clinically actionable tools that can be used to enhance clinical trial design and care management.”