Dataset Information

Cognitive biomarker prioritization in Alzheimer's Disease using brain morphometric data.

ABSTRACT:

Background

Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization.

Method

We adapt a newly developed learning-to-rank approach [Formula: see text] to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend [Formula: see text] to better separate the most effective cognitive assessments and the less effective ones.

Results

Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features.

Conclusions

The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.

SUBMITTER: Peng B

PROVIDER: S-EPMC7709267 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Cognitive biomarker prioritization in Alzheimer's Disease using brain morphometric data.

Peng Bo B Yao Xiaohui X Risacher Shannon L SL Saykin Andrew J AJ Shen Li L Ning Xia X

BMC medical informatics and decision making 20201202 1

<h4>Background</h4>Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning ...[more]

PMID: 33267852

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Project description:Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid-β42, total tau, phosphorylated tau), [18F ]florbetapir, hippocampal volume and brain-age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R2 = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two-way interaction models. The best performing model included an interaction between amyloid-β-PET and P-tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker 'space', providing information for personalised or stratified healthcare or clinical trial design.

Project description:BACKGROUND:Alzheimer's disease (AD) biomarkers are emerging as critically important for disease detection and monitoring. Most biomarkers are obtained through invasive, resource-intense procedures. A cognitive marker, intra-individual cognitive variability (IICV) may provide an alternative or adjunct marker of disease risk for individuals unable or disinclined to undergo lumbar puncture. OBJECTIVE:To contrast risk of incident AD and mild cognitive impairment (MCI) associated with IICV to risk associated with well-established biomarkers: cerebrospinal fluid (CSF) phosphorylated tau protein (p-tau181) and amyloid-? 42 (A?42) peptide. METHODS:Dispersion in cognitive performance, IICV, was estimated with a published algorithm, and included Trail Making Test A and B, Rey Auditory Verbal Learning Test (RAVLT), and the American National Adult Reading Test (ANART). CSF biomarkers were expressed as a ratio: p-tau181/A?42, wherein high values signified pathognomonic profiles. Logistic regression models included longitudinal data from 349 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who completed lumbar puncture. All subjects were cognitively healthy (n?=?105) or diagnosed with MCI (n?=?244) at baseline. We examined odds of conversion associated with baseline elevations in IICV and/or ratio of CSF p-tau181/A?42. RESULTS:When included in models alone or in combination with CSF p-tau181/A?42, one standard IICV unit higher was associated with an estimated odds ratio for incident AD or MCI of 2.81 (95% CI: 1.83-4.33) in the most inclusive sample, and an odds ratio of 3.41 (95% CI: 2.03-5.73) when restricted to participants with MCI. Iterative analyses suggested that IICV independently improved model fit even when individual index components were included in comparative models. CONCLUSIONS:These analyses provide preliminary support for IICV as a marker of incident AD and MCI. This easily-disseminated, non-invasive marker compared favorably to well-established CSF biomarkers.

Dataset Information

Cognitive biomarker prioritization in Alzheimer's Disease using brain morphometric data.

Background

Method

Results

Conclusions

Publications

Cognitive biomarker prioritization in Alzheimer's Disease using brain morphometric data.

Similar Datasets

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