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Mild Cognitive Impairment Diagnosis Using Extreme Learning Machine Combined With Multivoxel Pattern Analysis on Multi-Biomarker Resting-State FMRI | IEEE Conference Publication | IEEE Xplore
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Mild Cognitive Impairment Diagnosis Using Extreme Learning Machine Combined With Multivoxel Pattern Analysis on Multi-Biomarker Resting-State FMRI


Abstract:

This paper proposed a classification framework that integrates hybrid multivoxel pattern analyses (MVPA) and extreme learning machine (ELM) for automated Mild Cognitive I...Show More

Abstract:

This paper proposed a classification framework that integrates hybrid multivoxel pattern analyses (MVPA) and extreme learning machine (ELM) for automated Mild Cognitive Impairment (MCI) diagnosis applied on concatenations of multi-biomarker resting-state fMRI. Given three-dimensional (3D) regional coherences and functional connectivity patterns measured during resting state, we performed 3D univariate t-tests to obtain initial univariate features which show the significant changes. To enhance discriminative patterns, we employed multivariate feature reductions using recursive feature elimination in combination with univariate t-test. The maximal amount of information changes were achieved by concatenations of multiple functional metrics. The classifications were performed by an ELM, and its efficiency was compared to SVMs. This study reported mean accuracies using 10-fold cross-validation, followed by permutation tests to assess the statistical significance of discriminative results. In diagnosis of MCI, the proposed method achieved a maximal accuracy of 97.86% (p<; 0.001) in ADNI2 cohort and thus has potentials to assist the clinicians in MCI diagnosis.
Date of Conference: 23-27 July 2019
Date Added to IEEE Xplore: 07 October 2019
ISBN Information:

ISSN Information:

PubMed ID: 31946035
Conference Location: Berlin, Germany

References

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