## 2 Similar Studies  
Researchers have widely investigated advanced neuroimaging and machine learning techniques for the early detection of AD and prediction of MCI conversion. Along these lines, the authors in Reference [16] proposed a multitask learning method based on multimodality data including MRI, fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF). They reported accuracies of 84.8%, 73.9%, and 62% for MRI with AD/HC, MCI/HC, and pMCI/sMCI samples, respectively. Moreover, they achieved accuracies of 93.30%, 83.2%, and 73.9% for the combination of MRI, CSF, and PET data with AD/HC, MCI/HC, and pMCI/sMCI samples, respectively. In Reference [18], the authors constructed structural connectivity networks of cortical morphometry based on individual gray matter images for AD classification and MCI conversion prediction. Using a histogram-based feature generation technique of structural connectivity networks, they achieved accuracies of 84.17%, 61.05%, 67.59%, and 70.38% with AD/HC, pMCI/sMCI, AD/sMCI,and MCI/HC samples. The authors in Reference [19] proposed a semi-supervised SVM for predicting sMCI from pMCI using MRI. They achieved a sensitivity (SEN) of 78.8%, specificity (SPE) of 51%, and area under the curve (AUC) of 61% with a supervised method and SEN of 79.4%, SPE of 51.9%, and AUC of 69% with a proposed semi-supervised method. The authors in Reference [20] investigated a multivariate data analysis method using multimodality data (i.e., MRI and CSF). They reported accuracies of 87.0%, 71.8%, and 61.5% for AD/HC, MCI/HC, and pMCI/sMCI using MRI only and accuracies of 91.8%, 77.6%, and 66.7 for AD/HC, MCI/HC, and pMCI/sMCI by combining MRI and CSF. In Reference [21], the authors proposed the use of Spatial Pattern of Abnormalities for Recognition of Early AD (SPARE-AD) based on MRI. They reported accuracies of 55.8% and 61.7% in the prediction of sMCI from pMCI by MRI and MRI + t-tau, respectively. In Reference [22], the authors presented a multimodality classification framework based on pairwise connectivity measures derived by random forest classifiers. Using MRI, they reported accuracies of 81.6% in the classification of AD/HC, 66.9% in the classification of MCI/HC, and 55.1% for the prediction of pMCI/sMCI. In addition, they reported accuracies of 89.0%, 74.6%, and 58% for the classification of AD/HC, MCI/HC, and sMCI/pMCI groups by combining MRI with CSF, FDG-PET, and genetic data. The authors in Reference [23] investigated a Gaussian process approach using several combined multimodality data sources (i.e., MRI, PET, CSF, and APOE genotype). They stated balanced accuracies of 61.5% using MRI and of 74.1% using several combined multimodality data sources (i.e., MRI, PET, CSF, and APOE genotype) for sMCI/pMCI groups. Using voxel-based morphometry (VBM) analysis of gray matter (GM) as a feature followed by semi-supervised learning, the authors in Reference [13] presented a robust framework for MCI conversion prediction with accuracies of 74.74% based on MRI and of 82% by combining MRI features with age and cognitive measures in pMCI/sMCI samples. In another study [24], the authors calculated 55 volumetric variables from MRI data using FreeSurfer software ([[http://surfer.nmr.mgh.harvard.edu/](http://surfer.nmr.mgh.harvard.edu/)]([http://surfer.nmr.mgh.harvard.edu/](http://surfer.nmr.mgh.harvard.edu/)), Version 5.1.0, Athinoula A. Martinos Center at the Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA). They reported accuracies of 78%, 66.7%, and 62.1% in the classification of AD/HC, amnestic-MCI/HC, and nonamnestic-MCI/HC using MRI data. In addition, they achieved accuracies of 92.4%, 74.9%, and 74.1% by combining MRI data with the Mini-Mental State Examination (MMSE) score in a classification of AD/HC, amnestic-MCI/HC, and nonamnestic-MCI/HC, respectively. The use of multiscale feature extraction based on baseline MRI data was presented in Reference [25] with accuracies of 84.13% and 76.69% for AD/HC and pMCI/sMCI, respectively.