Table 3.

Comparison of our proposed approaches (cFSGL and nFSGL) and existing approaches (Ridge and TGL) on longitudinal MMSE and ADAS-Cog prediction using MRI features (M) 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	0.548 ± 0.057	0.449 ± 0.045	0.428 ± 0.052	0.400 ± 0.053	0.395 ± 0.052	0.412 ± 0.054	0.408 ± 0.056
R	0.689 ± 0.030	0.755 ± 0.029	0.772 ± 0.030	0.790 ± 0.032	0.796 ± 0.031	0.788 ± 0.031	0.792 ± 0.031
M06 MSE	2.269 ± 0.207	2.038 ± 0.262	2.117 ± 0.209	2.069 ± 0.209	2.071 ± 0.213	2.149 ± 0.194	2.181 ± 0.201
M12 MSE	3.266 ± 0.556	2.923 ± 0.643	2.900 ± 0.629	2.803 ± 0.662	2.762 ± 0.669	2.835 ± 0.662	2.793 ± 0.659
M24 MSE	3.494 ± 0.599	3.363 ± 0.733	3.125 ± 0.612	3.016 ± 0.624	3.000 ± 0.642	3.031 ± 0.604	2.979 ± 0.546
M36 MSE	4.003 ± 0.853	3.768 ± 0.962	3.456 ± 0.766	3.302 ± 0.781	3.265 ± 0.803	3.263 ± 0.785	3.211 ± 0.786
M48 MSE	4.328 ± 1.310	3.631 ± 1.226	2.857 ± 0.892	2.787 ± 0.871	2.871 ± 0.884	2.780 ± 0.855	2.766 ± 0.826
Target: ADAS-Cog
nMSE	0.532 ± 0.095	0.464 ± 0.067	0.444 ± 0.059	0.404 ± 0.055	0.391 ± 0.059	0.386 ± 0.060	0.381 ± 0.057
R	0.705 ± 0.043	0.747 ± 0.033	0.765 ± 0.032	0.791 ± 0.026	0.803 ± 0.024	0.809 ± 0.023	0.809 ± 0.023
M06 MSE	5.213 ± 0.522	4.820 ± 0.489	4.779 ± 0.421	4.543 ± 0.374	4.451 ± 0.340	4.458 ± 0.354	4.428 ± 0.351
M12 MSE	6.079 ± 0.775	5.813 ± 0.697	5.605 ± 0.622	5.363 ± 0.595	5.230 ± 0.589	5.183 ± 0.597	5.136 ± 0.617
M24 MSE	7.409 ± 1.154	6.835 ± 1.052	6.893 ± 0.950	6.456 ± 0.974	6.249 ± 0.996	6.174 ± 0.943	6.153 ± 0.911
M36 MSE	7.143 ± 1.351	6.938 ± 1.363	6.475 ± 1.135	6.101 ± 1.071	5.928 ± 1.064	5.819 ± 0.945	5.879 ± 0.972
M48 MSE	6.644 ± 2.750	6.000 ± 2.738	5.767 ± 2.189	5.751 ± 2.081	5.980 ± 1.979	5.889 ± 1.848	5.837 ± 2.160