Table 1.
Demographic details of all participants of two cohorts in this study.
Fig 1.
Descriptions of the proposed framework in this study.
Block (a) presents the 3-D feature measure extractions from preprocessed fMRI scans. Block (b) describes the LOO-CV and 10-fold-CV cross validation for ADNI2 and in-house cohorts, respectively. Block (c) presents the multivariate feature reduction techniques using LASSO and SVM-RFE. The combined univariate t-test and multivariate LASSO as well as SVM-RFE informative features are trained by ELM and SVM classifiers as illustrated in block (d). Finally, the trained classifiers and testing features are used to evaluate the performance as in block (e).
Table 2.
Detailed information of the seeds for seed-based rsFC measures.
Fig 2.
An example of one-fold univariate statistical two-sample t-test on ReHo maps between two training analytical groups, i.e., AD against CN (left subfigure) and MCI against CN (right subfigure). The threshold was set to p-value<0.05 with cluster size of 85 voxels (2295 mm3), which corresponded to a corrected p-value<0.05. The t-test maps are overlaid on the anatomical image. The hot and cold colours represent positive and negative changes.
Fig 3.
Illustration of the hybrid combination of univariate t-test and MVPA feature reduction techniques (SVM-RFE and LASSO) on the 3-D cross-validated fMRI measures.
Fig 4.
An example of cross-validated MSE of LASSO fit with a parameter lambda (λ).
Table 3.
Leave-One-Out cross-validation mean classification performance for AD versus CN of multi-measure features at p-value = 0.05 with ADNI2 cohort.
Table 4.
Leave-One-Out cross-validation mean classification performance for MCI against CN of multi-functional features at p-value = 0.05 with ADNI2 cohort.
Table 5.
10-fold cross-validation mean classification performance for AD against CN of multi-functional features at p-value = 0.05 with In-house cohort.
Table 6.
10-fold cross-validation mean classification performance for MCI against CN of multi-functional features at p-value = 0.05 with In-house cohort.
Table 7.
The effects of significant p-values on the classification performances reported with ADNI2 and in-house cohorts.
Table 8.
The effects of multivariate feature optimization methods (LASSO and SVM-RFE) on the ELM classification performances reported with ADNI2 and in-house cohorts.
Table 9.
Comparison of classification accuracy of AD/MCI subjects with state-of-the-art methods using rs-fMRI.
Table 10.
Comparison of classification performances of AD/MCI patients on ADNI cohort with hybrid MVPA feature selections.
Fig 5.
Univariate t-statistical difference maps between AD and CN groups of ten measures extracted from in-house cohort.
Voxels with p-value<0.05 and cluster size of 85 voxels (2295 mm3) corresponding to a corrected p-value<0.05 were used to identify the significant clusters. Hot and cold colours indicate AD-related measures increases and decreases, respectively.
Fig 6.
Univariate t-statistical difference maps between MCI and CN groups of ten measures extracted from in-house cohort.
Voxels with p-value<0.05 and cluster size of 85 voxels (2295 mm3) corresponding to a corrected p-value<0.05 were used to identify the significant clusters. Hot and cold colours indicate MCI-related measures increases and decreases, respectively.