## 5 Discussion  
Recent advances in machine learning research suggest that AD patients can be recognized at early stages of the disease via different neuroimaging modalities such as MRI [12; 43; 44], FDG-PET [45; 46; 47], and diffusion tensor imaging [48]. Among different neuroimaging modalities applied to AD, MRI is widely used because of its widespread availability and completely noninvasive nature [12]. Generally, AD classification studies using neuroimaging data suffer from the curse of dimensionality. Different dimensionality reduction and feature selection approaches have been proposed to overcome this drawback and improve the classification performance. For instance, the Principal Component Analysis (PCA) technique was proposed for the reduction of data extracted from SPECT and PET modalities [49]. Various AD studies additionally used the Partial Least Squares technique to transfer the data from a high-dimensional space into a lower dimensional vector [50; 51; 52; 53]. Parallel to these approaches, a feature ranking-based strategy has been proposed for sorting the extracted features from high-dimensional space on the basis of their importance and then selecting the optimal subset of top-ranked features using Fisher criterion [54] and classification error [55].  
In this study, we developed an automatic CAD framework followed by voxel-wise MRI features for AD detection and MCI conversion prediction. Although the voxel-wise metric is widely used for AD classification and brain-age estimation [31], it suffers from the curse of dimensionality (i.e., a high-dimensional feature vector compared with the number of samples). To address this limitation, we presented an MFS procedure that reduces dimensionality by eliminating redundant features (i.e., voxel intensities). The performance of the proposed CAD framework was assessed using 507 MRI scans taken at baseline from the J-ADNI data set. The experimental results indicated that the proposed MFS can identify the most informative features (i.e., voxel intensities) from high-dimensional space and that the proposed CAD system is highly competitive with state-of-the-art techniques using only MRI reported in the literature.  
Lately, a comprehensive study was conducted in Reference [56] to explore the impact of different datasets (i.e., ADNI, AIBL, and OASIS), training set sizes, scanner protocols, preprocessing options, brain imaging modalities, classifiers, and feature types (i.e., voxel-wise and region-wise) on AD classification. In this study, we investigated the effect of different isotropic spatial resolutions on AD classification. Our experimental results demonstrate that spatial resolution may play an effective role in classification performance. Practically, the performance of the proposed CAD framework was improved by decreasing the spatial resolution. This may be because a lower spatial resolution (i.e., 2 mm) may help to trace atrophic regions in the AD brain. It is worth noting that a decrease in the spatial resolution increases the dimensionality and computational cost. For example, by decreasing  
Figure 4: ROC curves of the proposed CAD framework for the four pairs of clinical groups (i.e., AD/HC, pMCI/sMCI, pMCI/HC, and sMCI/AD) with a 2-mm spatial resolution.  
the spatial resolution from 8 mm to 2 mm, the dimensionality increases from 3747 to 238,955 features for each individual. Consequently, a tradeoff between the spatial resolution and computational cost is needed. For future work, we suggest to use the MFS technique for classification of AD and prediction of MCI on the basis of ROI-based metrics. In this study, we used only GM images. Another future direction will be to integrate the GM, white matter, and CSF components (i.e., data fusion) followed by MFS procedure to improve the classification accuracy for AD studies.