Table 10.
Prediction of continuous variables
| Classification challenge | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Modality | Feature selection/dimensionality reduction | Classifier | N | ADAS-cog | MMSE | RALVT | Unique approach | Reference | |
| MRI Cortical surface | Multivariate regression: l 2,1 norm | SVM | 718 | RMSE | 0.7663 ± 0.0375 | 0.8325 ± 0.0399 | 0.9167 ± 0.0471 | Propose sparse multitask learning model Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) to select cortical surface markers | [440] |
| CC | 0.6438 ± 0.0258 | 0.5277 ± 0.0539 | 0.3985 ± 0.0533 | ||||||
| MRI | Multitask learning: relational function + l 2,1 regression | SVM | 202 | CC(AD vs. MCI) | 0.680 | 0.682 | Introduces a function to conserve information about feature-feature relations, response-response relations, and sample-sample relations while jointly solving both classification and prediction of continuous variables | [410] | |
| PET | CC (MCI vs. CN) | 0.520 | 0.508 | ||||||
| CC (MCIc vs. MCInc) | 0.591 | 0.622 | |||||||
| MRI | Canonical correlation analysis + novel sparse multitask learning | SVM | 202 | CC | 0.740 ±0.18 | 0.675 ± 0.23 | Uses canonical correlation analysis to determine correlations between features of different modalities | [441] | |
| FDG PET | |||||||||
| Note: for AD vs. MCI vs. CN | |||||||||
| MRI | Canonical correlation analysis + novel sparse multitask learning | SVM | 202 | RMSE | 4.201 ± 0.82 | 2.110 ± 0.41 | Similar to [441] | [442] | |
| FDG PET | CC | 0.719 ± 0.81 | 0.655 ± 0.31 | Note: for AD vs. MCI vs. CN | |||||
| MRI | Multitask learning: graph-guided + latent | 199 | CC | 0.740 ± 0.002 | 0.745 ± 0.002 | Uses new latent group LASSO penalty combined with an undirected graph approach to select correlated features that can jointly predict class label and clinical scores. | [412] | ||
| FDG PET | LASSO group penalty | ||||||||
| CSF | |||||||||
| MRI | Multivariate regression: sparse model | Sparse Bayesian | 393 | CC | 0.767 | 0.758 | Models cognitive scores as nonlinear functions of neuroimaging variables; models correlations between regression coefficients. Derives sparse Bayesian learning algorithm for learning. | [443] | |
| MRI (TBM) | Multivariate TBM + Convex Fused Sparse Group LASSO | Prediction of continuous variables: ADAS-cog | 616 | Prediction of ADAS-cog scores (RMSE) | Uses Convex Fused Sparse Group LASSO for multitask learning using multivariate TBM data to predict ADAS-cog scores at 6, 12, 24, 36, and 48 months. | [444] | |||||
| HC shape, Clinical, APOE4, baseline MMSE | Months | 6 | 12 | 24 | 36 | 48 | |||||
| w/o mTBM | 5.259 ± 0.87 | 5.653 ± 1.14. 7 | 5.532 ± 1.03 | 4.777 ± 0.83 | 4.367 ± 1.18 | ||||||
| With mTBM | 4.534 ± 0.88 | 4.989 ± 1.13 | 4.885 ± 1.09 | 4.055 ± 1.02 | 3.164 ± 1.09 | ||||||
Abbreviations: ADAS-cog, Alzheimer’s Disease Assessment Scale–cognitive subscale; MMSE, Mini–Mental State Examination; RAVLT, Rey’s Auditory Verbal Learning Test; MRI, magnetic resonance imaging; SVM, support vector machine; RMSE, root mean square error; CC, correlation coefficient; PET, positron emission tomography (FDG and Aβ); AD, Alzheimer’s disease; MCIc, mild cognitive impairment converters (progressive MCI); MCInc, mild cognitive impairment nonconverters (stable MCI); FDG PET, 18F-flurodeoxyglucose positron emission tomography; MCI, mild cognitive impairment; CSF, cerebrospinal fluid; CN, cognitively normal; TBM, tensor-based morphometry; HC, hippocampus; APOE4, apolipoprotein ε4 allele; mTBM, multivariate tensor-based morphometry; LASSO, least absolute shrinkage and selection operator.