The inclusion of participants is visualised in Fig. 1. Table 2 shows the demographics and MMSE scores of the participants (24 AD, 33 FTD, 34 CN). Four patients were excluded because of poor ASL data quality, i.e. motion artefacts or noise. Included FTD disease subtypes were as follows: behavioural variant FTD (bvFTD, n = 12), PPA (n = 16, including ten with semantic dementia [SD] and four with progressive non-fluent aphasia [PNFA]), and five patients with unknown subtype. In the AD group, six patients had <1 year follow-up (range 0–7 months), and the diagnosis of 18 patients was confirmed by >1 year follow-up (range 12–45 months). In the FTD group, 12 patients had <1 year follow-up (range 0–11 months), and 21 patients had >1 year follow-up (range 12–47 months).
Figure 2 shows the classification performance using T1w, ASL, and DTI voxel-wise features (Fig. 2a: AUC; 2b: accuracy). Table 3 shows non-parametric testing for significant differences between classifications.
For AD-CN classification, mean AUCs were 92% (VBM-GM), 87% (VBM-WM), 94% (VBM-Brain), 89% (CBF), 89% (FA), 95% (GM combination), 91% (WM combination), and 98% (Full combination). Classification accuracy was slightly lower than AUC in general. The performance using CBF and FA features was similar to that of the VBM features. The feature combinations yielded slightly higher performance than the VBM features, but differences were not significant.
For FTD-CN classification, AUCs using VBM were somewhat higher than for AD-CN, but combination with FA and CBF did not improve performance. AUCs were 95% (VBM-GM), 96% (VBM-WM), 95% (VBM-Brain), 87% (CBF), 91% (FA), 93% (GM combination), 95% (WM combination), and 96% (Full combination).
For differential diagnosis of AD versus FTD, AUCs were 78% (VBM-GM), 76% (VBM-WM), 72% (VBM-Brain), 81% (CBF), 80% (FA), 84% (GM combination), 81% (WM combination), and 84% (Full combination). Combination with CBF and FA features improved performance over the use of VBM features only.For multi-class diagnosis of AD, FTD, and CN, mean AUCs were 85% (VBM-GM), 83% (VBM-WM), 84% (VBM-Brain), 82% (CBF), 83% (FA), 87% (GM combination), 85% (WM combination), and 90% (Full combination). Classification accuracy was lower, but it should be noted that for this three-class diagnosis, the accuracy for random guessing would be only ~33%. For multi-class classification, AUCs were highest for the combination methods. The method that combined VBM-Brain with CBF and FA yielded a significantly higher AUC (90 vs. 84%, p = 0.03) and accuracy (75 vs. 70%, p = 0.05) than VBM-Brain by itself. This is reflected in the examples of confusion matrices for one iteration of the cross-validation (Appendix C; Table C1), which show a higher number of correctly classified patients and controls for Full combination than for VBM-Brain. However, combining VBM with ASL or DTI may also reduce the number of correctly classified patients, e.g. GM Combination has a lower number of correctly classified FTD patients than VBM-GM, while accuracy is improved.
Using SVM p-maps (Figs. 3, 4, and 5, Appendix B Figs. B1 and B2), we evaluated which voxels contributed significantly to the classifications. For VBM-GM (Fig. 3), we noted major influence of the perihippocampal region on the classifier; overall we observed a larger number of significant voxels in the left than in the right hemisphere. For differential diagnosis of AD-FTD, mainly voxels in the anterior temporal lobe were involved.
For VBM-WM (Fig. B1), we observed most clusters of significantly contributing voxels in the temporal lobe and around the ventricles. For AD-CN and FTD-CN classification, a smaller cluster of significant voxels in the corpus callosum was found. The temporal lobe clusters were present mainly in the left hemisphere, especially for AD-FTD differentiation.
For VBM-Brain (Fig. B2), p-maps were very smooth as the feature is formed by the Jacobian determinant of the spatially smooth deformation to template space. Smoothness is lost in VBM-GM and VBM-WM by multiplying the Jacobian determinant with the probabilistic tissue segmentations. For AD-CN, the classification was driven mainly by periventricular and left temporal lobe features. For FTD-CN, the temporal lobe contributed with the largest clusters of significant voxels. For AD-FTD, small clusters were found in the middle frontal gyrus, temporal lobe and periventricular regions.
For CBF (Fig. 4), p-maps showed small clusters of significant voxels in multiple brain regions. For AD-CN, significant voxels were observed mainly in the GM of the parietal lobe, precuneus, posterior cingulate gyrus, posterior temporal lobe and the insula. For FTD-CN, the main regions with significant voxels were the posterior cingulate gyrus, superior frontal gyrus, the straight gyrus, lingual gyrus and the putamen. For AD-FTD, the classification relied mainly on voxels from the posterior cingulate gyrus, parietal lobe, caudate nucleus, insula, temporal lobe and the cuneus.
For FA (Fig. 5), clusters of voxels in the corpus callosum and around the globus pallidus and putamen contributed significantly to the AD-CN classification. In addition, clusters of voxels in the visual and motor tracts contributed. For FTD-CN, the clusters of significant voxels were observed mainly in the anterior temporal lobe, the frontal WM, the corpus callosum, and language-associated tracts (uncinate fasciculus, superior longitudinal fasciculus). For the differential diagnosis of AD-FTD, fewer voxels were significant with only a cluster of significant voxels in the uncinate fasciculus.