Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer's Disease via Fusion of Clinical, Imaging and Omic Features - PubMed (original) (raw)

Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer's Disease via Fusion of Clinical, Imaging and Omic Features

Asha Singanamalli et al. Sci Rep. 2017.

Abstract

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).

PubMed Disclaimer

Conflict of interest statement

Conflict of interest disclosures for Anant Madabhushi: Inspirata-Stock Options/Consultant/Scientific Advisory Board Member, Elucid Bioimaging Inc.-Stock Options, Siemens, GE-NIH Academic Industrial Partnership.

Figures

Figure 1

Figure 1

The cascade and the modalities for fusion at each level of the cascade were determined on training set and validated on independent testing set. Neurophysiological test scores (ADAS-Cog) are fused with CSF proteomics and APOE at the first level of the cascade to identify healthy controls (HC). At the second level, ADAS-Cog scores are combined with PET to distinguish between patients with Alzheimer’s Disease (AD) and mild cognitive impairment (MCI).

Figure 2

Figure 2

Cascaded multiview canonical correlation analysis (CaMCCo) algorithm for constructing the joint multimodal data fusion and multiclass classification framework.

Figure 3

Figure 3

Performance of single and multi modality cascaded classifiers. Area under the ROC curve (AUC) for prediction of (a) healthy control (HC) from all cognitive impairments, and (b) mild cognitive impairment (MCI) from Alzheimer’s Disease (AD).

References

    1. McKhann GM, et al. The diagnosis of dementia due to alzheimer’s disease: Recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for alzheimer’s disease. Alzheimer’s & Dementia. 2011;7:263–269. doi: 10.1016/j.jalz.2011.03.005. -DOI -PMC -PubMed
    1. Association A, et al. Alzheimer’s disease facts and figures. Alzheimer’s & dementia: the journal of the Alzheimer’s Association. 2012;8:131. doi: 10.1016/j.jalz.2012.02.001. -DOI -PubMed
    1. Brookmeyer R, Gray S, Kawas C. Projections of alzheimer’s disease in the united states and the public health impact of delaying disease onset. American journal of public health. 1998;88:1337–1342. doi: 10.2105/AJPH.88.9.1337. -DOI -PMC -PubMed
    1. Knopman D, et al. Practice parameter: Diagnosis of dementia (an evidence-based review) report of the quality standards subcommittee of the american academy of neurology. Neurology. 2001;56:1143–1153. doi: 10.1212/WNL.56.9.1143. -DOI -PubMed
    1. Carrillo MC, et al. Revisiting the framework of the national institute on aging-alzheimer’s association diagnostic criteria. Alzheimer’s & Dementia. 2013;9:594–601. doi: 10.1016/j.jalz.2013.05.1762. -DOI -PubMed

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources