Summary: Researchers have developed a new algorithm that integrates Alzheimer’s indicators from MRI measurements to predict patients with the neurodegenerative disease.
Source: Case Western Reserve.
A new machine learning program developed by researchers at Case Western Reserve University appears to outperform other methods for diagnosing Alzheimer’s disease before symptoms begin to interfere with every day living, initial testing shows.
More than 5 million Americans may have Alzheimer’s disease, according to estimates, and the numbers are growing as the population ages. The disease is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. And while there is no cure, several drugs can delay or prevent symptoms from worsening for up to five years or more, according to the National Institute on Aging and published research.
Meanwhile, early diagnosis and treatment–the goal of the new computer based program–is key to allowing those with the disease to remain independent longer.
The computer program integrates a range of Alzheimer’s disease indicators, including mild cognitive impairment. In two successive stages, the algorithm selects the most pertinent to predict who has Alzheimer’s.
“Many papers compare the healthy to those with the disease, but there’s a continuum,” said Anant Madabhushi, F. Alex Nason professor II of biomedical engineering at Case Western Reserve. “We deliberately included mild cognitive impairment, which can be a precursor to Alzheimers, but not always.”
In a study published in the journal Scientific Reports, Madabhushi, Asha Singanamalli, who recently earned her biomedical engineering master’s degree and Haibo Wang, a former postdoctoral researcher, tested the algorithm using data from 159 patients collected via the Alzheimer’s Disease Neuroimaging Initiative.
The team developed what it calls Cascaded Multi-view Canonical Correlation (CaMCCo) algorithm, which integrates measurements from magnetic resonance imaging (MRI) scans, features of the hippocampus, glucose metabolism rates in the brain, proteomics, genomics, mild cognitive impairment and other parameters.
Madabhushi’s lab has repeatedly found that integrating dissimilar information is valuable for identifying cancers. This is the first time he and his team have done so for diagnosis and characterization of Alzheimer’s disease.
“The algorithm assumes each parameter provides a different view of the disease, as if each were a different set of colored spectacles,” Madabhushi said.
The program then assesses the variables in a two-stage cascade. First, the algorithm selects the parameters that best distinguish between someone who’s healthy and someone who’s not. Second, the algorithm selects from the unhealthy variables those that best distinguish who has mild cognitive impairment and who has Alzheimer’s disease.
“The remaining views are combined to give the best picture,” Madabhushi said.
In predicting which patients in the study had Alzheimer’s disease, CaMCCo outperformed individual indicators as well as methods that combine them all without selective assessment. It also was better at predicting who had mild cognitive impairment than other methods that combine multiple indicators.
The researchers continue to validate and fine-tune the approach with data from multiple sites. They also plan to use the software in an observational mode: As a collaborating neurologist compiles tests on patients, the computer would run the data. If CaMCCo proves useful in predicting early Alzheimer’s, Madabhushi expects to pursue a clinical trial for prospective validation.
Funding: National Institutes of Health, National Center for Research Resources, DOD Prostate Cancer Synergistic Idea DevelopmentAward (PC120857), DOD Lung Cancer Idea Development New Investigator Award, DOD Prostate Cancer Idea Development Award funded this study.
Source: Kevin Mayhood – Case Western Reserve Image Source: NeuroscienceNews.com image is in the public domain. Original Research: Full open access research for “Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features” by Asha Singanamalli, Haibo Wang & Anant Madabhushi in Scientific Reports. Published online August 15 2017 doi:10.1038/s41598-017-03925-0
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[cbtabs][cbtab title=”MLA”]Case Western Reserve “New Machine Learning Program Shows Promise For Early Alzheimer’s Diagnosis.” NeuroscienceNews. NeuroscienceNews, 15 August 2017. <https://neurosciencenews.com/machine-learning-alzheimers-7305/>.[/cbtab][cbtab title=”APA”]Case Western Reserve (2017, August 15). New Machine Learning Program Shows Promise For Early Alzheimer’s Diagnosis. NeuroscienceNew. Retrieved August 15, 2017 from https://neurosciencenews.com/machine-learning-alzheimers-7305/[/cbtab][cbtab title=”Chicago”]Case Western Reserve “New Machine Learning Program Shows Promise For Early Alzheimer’s Diagnosis.” https://neurosciencenews.com/machine-learning-alzheimers-7305/ (accessed August 15, 2017).[/cbtab][/cbtabs]
Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features
The introduction of mild cognitive impairment (MCI) as a diagnostic category adds to the challenges of diagnosing Alzheimer’s Disease (AD). No single marker has been proven to accurately categorize patients into their respective diagnostic groups. Thus, previous studies have attempted to develop fused predictors of AD and MCI. These studies have two main limitations. Most do not simultaneously consider all diagnostic categories and provide suboptimal fused representations using the same set of modalities for prediction of all classes. In this work, we present a combined framework, cascaded multiview canonical correlation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic categories and optimizes classification by selectively combining a subset of modalities at each level of the cascade. CaMCCo is evaluated on a data cohort comprising 149 patients for whom neurophysiological, neuroimaging, proteomic and genomic data were available. Results suggest that fusion of select modalities for each classification task outperforms (mean AUC = 0.92) fusion of all modalities (mean AUC = 0.54) and individual modalities (mean AUC = 0.90, 0.53, 0.71, 0.73, 0.62, 0.68). In addition, CaMCCo outperforms all other multi-class classification methods for MCI prediction (PPV: 0.80 vs. 0.67, 0.63).
“Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features” by Asha Singanamalli, Haibo Wang & Anant Madabhushi in Scientific Reports. Published online August 15 2017 doi:10.1038/s41598-017-03925-0