Table 1.
Demographic information of the studied population.
Fig 1.
(a) Six subcortical structures including caudate, putamen, globus pallidus, hippocampus, amygdala, and thalamus were selected as ROIs. (b) Five cerebral cortical regions including frontal, parietal, occipital, temporal, and cingulate were also selected as ROIs.
Fig 2.
Comparison between different subjects in their deformable registration, deformation fields, and quantified distances.
The symmetric log-domain diffeomorphic demons registration well aligned the reference and source images, providing informative deformation fields that accurately reflect morphological difference between subjects.
Fig 3.
(a) Classification sensitivity (green), specificity (blue), and accuracy (red) of normal elderly controls versus AD patients with different ROIs. The highest accuracy (96.5%) was achieved using the whole-brain gray matter as ROI with 93.85% sensitivity and 97.78% specificity. The algorithm obtained high sensitivity and specificity (>90%) with half of the ROIs. (b) The ROC curve of the prediction accuracy between normal controls versus AD. The AUCs were larger than 0.98 for the whole-brain gray matter and white matter (left), amygdala and hippocampus (middle), parietal and temporal lobes (right).
Table 2.
Classification results of normal elderly controls vs AD.
Fig 4.
(a) Classification sensitivity (green), specificity (blue), and accuracy (red) of normal elderly controls versus progressive MCI subjects with different ROIs. Using the amygdala, and hippocampus as ROI, the algorithm obtained classification accuracy of 91.74%. With the other six ROIs (temporal, GM, frontal, parietal, caudate, and cingulate), the accuracy exceeded 80%. (b) The ROC curve of the prediction accuracy between normal controls versus progressive MCI. The AUC reached up to 0.971 for amygdala (middle-blue curve), and was larger than 0.91 for hippocampus (0.963), temporal lobe (0.947), the whole-brain gray matter (0.928), parietal lobe (0.928), and frontal lobe (0.912).
Table 3.
Classification results of normal elderly controls vs pMCI.
Fig 5.
(a) Classification sensitivity (green), specificity (blue), and accuracy (red) of stable MCI versus progressive MCI subjects with different ROIs. High ranked ROIs included amygdala, hippocampus, the whole-brain gray matter, frontal lobe, and cingulate cortex, with which classification accuracy exceeded 85%. Sensitivity for globus pallidus, thalamus, and putamen was substantially low which resulted in bad performance in discrimination. (b) The ROC curve of the prediction accuracy between stable MCI versus progressive MCI. The AUC reached up to 0.932 for amygdala (middle-blue curve), and 0.918 for hippocampus (middle-maroon curve).
Table 4.
Classification results of sMCI vs pMCI.
Table 5.
Classification performance comparison for different ROIs.
Table 6.
Comparison between the proposed method and existing methods.