Machine Learning Identifies Most Accurate Time Frames to Predict Alzheimer's Disease Progression

Machine Learning Identifies Most Accurate Time Frames to Predict Alzheimer’s Disease Progression

A Cornell-led collaboration used machine learning to identify the most accurate ways and timeframes to predict the course of Alzheimer’s disease in cognitively normal or mildly cognitively impaired people.

Modeling has shown that it is easier and more accurate to predict future dementia decline in people with mild cognitive impairment than in cognitively normal or asymptomatic people. At the same time, the researchers found that predictions for cognitively normal subjects are less accurate for longer time horizons, but for people with mild cognitive impairment the reverse is true.

Modeling also demonstrated that magnetic resonance imaging (MRI) is a useful prognostic tool for people in both stages, while tools that track molecular biomarkers, such as positron emission tomography (PET), are more useful for people with mild cognitive impairment.

The team’s paper, “Machine Learning Based Multi-Modal Prediction of Future Decline Toward Alzheimer’s Disease: An Empirical Study,” was published Nov. 16 in PLOS ONE. The lead author is Batuhan Karaman, a PhD student in the field of electrical and computer engineering.

Alzheimer’s disease can take years, sometimes decades, to progress before a person shows symptoms. Once diagnosed, some people decline rapidly, but others may live with mild symptoms for years, making it difficult to predict the rate of disease progression.

“When we can say with confidence that a person has dementia, it’s too late. A lot of damage has already happened to the brain, and it’s irreversible damage,” said lead author Mert Sabuncu, associate professor of Electrical and Computer Engineering at the College of Engineering. and Electrical Engineering in Radiology at Weill Cornell Medicine.

We really need to be able to catch Alzheimer’s disease early and know who is going to progress quickly and who is going to progress slower, so that we can stratify the different risk groups and be able to deploy all the treatment options that we have. “

Mert Sabuncu, associate professor of electrical and computer engineering in the College of Electrical and Electrical Engineering in Radiology at Weill Cornell Medicine

Clinicians often focus on a single “time horizon” – usually three or five years – to predict the progression of Alzheimer’s disease in a patient. The delay may seem arbitrary, according to Sabuncu, whose lab specializes in analyzing biomedical data — particularly imaging data, with a focus on neuroscience and neurology.

Sabuncu and Karaman teamed up with longtime collaborator and co-author Elizabeth Mormino of Stanford University to use neural network machine learning that could analyze five years of data on people who were cognitively normal or had mild cognitive impairment. The data, captured in a study by the Alzheimer’s Disease Neuroimaging Initiative, encompassed everything from an individual’s genetic history to PET and MRI scans.

“What we were really interested in is, can we look at this data and tell if a person is going to progress in the coming years?” said Sabuncu. “And more importantly, can we do a better job of predicting when we combine all the tracking data points we have on individual subjects?”

The researchers found several notable patterns. For example, predicting that a person will go from asymptomatic to mildly symptomatic is much easier for a time horizon of one year, compared to five years. However, predicting whether someone will progress from mild cognitive impairment to Alzheimer’s dementia is more accurate over a longer period, with the “sweet spot” being around four years.

“That might tell us something about the underlying mechanism of the disease and how it evolves over time, but that’s something we haven’t probed yet,” Sabuncu said.

Regarding the effectiveness of different types of data, modeling has shown that MRI scans are more informative for asymptomatic cases and are particularly useful in predicting whether someone will develop symptoms over the next three years, but less useful for predicting people with mild cognitive impairment. . Once a patient has developed mild cognitive impairment, PET scans, which measure certain molecular markers such as amyloid and tau proteins, appear to be more effective.

One of the advantages of the machine learning approach is that the neural networks are flexible enough to be able to operate despite missing data, such as patients who may have skipped an MRI or PET scan.

In future work, Sabuncu plans to further modify the modeling so that it can process full imaging or genomic data, rather than just summary measurements, to harvest more information that will improve predictive accuracy.

The research was supported by the National Institutes of Health, National Library of Medicine and National Institute on Aging, and the National Science Foundation.


Journal reference:

Karaman, United Kingdom, et al. (2022) Machine learning-based multimodal prediction of future decline to Alzheimer’s disease: an empirical study. PLOS ONE.

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