Table 4.

Comparison of our proposed approaches (cFSGL and nFSGL) and existing approaches (Ridge and TGL) on longitudinal MMSE and ADAS-Cog prediction using MRI+META features (M+E) in terms of normalized mean squared error (nMSE), average correlation coefficient (R) and mean squared error (MSE) for each time point. 90 percent of data is used as training data.

	Ridge	TGL	cFSGL1	cFSGL2	cFSGL3	nFSGL1	nFSGL2
Target: MMSE
nMSE	0.404 ± 0.056	0.320 ± 0.044	0.310 ± 0.042	0.311 ± 0.042	0.312 ± 0.043	0.308 ± 0.046	0.303 ± 0.046
R	0.788 ± 0.032	0.839 ± 0.027	0.842 ± 0.026	0.841 ± 0.026	0.840 ± 0.026	0.839 ± 0.027	0.843 ± 0.027
M06 MSE	2.188 ± 0.194	1.943 ± 0.161	1.918 ± 0.155	1.912 ± 0.153	1.907 ± 0.149	1.935 ± 0.150	1.906 ± 0.149
M12 MSE	2.744 ± 0.638	2.366 ± 0.722	2.355 ± 0.716	2.356 ± 0.713	2.357 ± 0.711	2.374 ± 0.696	2.326 ± 0.707
M24 MSE	3.113 ± 0.560	2.821 ± 0.664	2.790 ± 0.653	2.823 ± 0.656	2.875 ± 0.675	2.766 ± 0.601	2.730 ± 0.604
M36 MSE	3.150 ± 0.517	2.933 ± 0.657	2.851 ± 0.635	2.878 ± 0.640	2.905 ± 0.646	2.755 ± 0.550	2.792 ± 0.523
M48 MSE	3.639 ± 0.959	3.544 ± 1.136	3.233 ± 1.070	3.098 ± 1.013	2.956 ± 0.924	2.942 ± 0.928	2.961 ± 0.969
Target: ADAS-Cog
nMSE	0.314 ± 0.036	0.278 ± 0.034	0.238 ± 0.033	0.233 ± 0.035	0.235 ± 0.035	0.238 ± 0.035	0.243 ± 0.035
R	0.840 ± 0.015	0.868 ± 0.016	0.882 ± 0.013	0.886 ± 0.014	0.886 ± 0.014	0.884 ± 0.015	0.880 ± 0.013
M06 MSE	3.972 ± 0.415	3.560 ± 0.469	3.566 ± 0.380	3.553 ± 0.375	3.617 ± 0.362	3.659 ± 0.356	3.535 ± 0.403
M12 MSE	4.365 ± 0.469	4.080 ± 0.598	3.742 ± 0.394	3.678 ± 0.389	3.659 ± 0.393	3.739 ± 0.367	3.742 ± 0.430
M24 MSE	6.028 ± 1.128	5.888 ± 1.641	5.226 ± 1.201	5.115 ± 1.277	5.122 ± 1.338	5.111 ± 1.222	5.257 ± 1.337
M36 MSE	5.824 ± 1.076	5.639 ± 1.339	4.871 ± 0.894	4.747 ± 0.957	4.712 ± 1.002	4.737 ± 0.917	5.055 ± 1.033
M48 MSE	6.192 ± 2.327	6.337 ± 2.487	5.133 ± 1.499	5.065 ± 1.446	5.103 ± 1.527	4.968 ± 1.339	5.404 ± 1.802