# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 2 Methods and Procedure
### Data Acquisition and Preprocessing  
All subjects used in this study were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Data were selected based on the availability of the resting-state fMRI dataset for age-matched healthy normal subjects and AD patients. A total of 69 subjects, 33 Alzheimer's disease (18 females, 15 males, age \(\pm\) SD = 72.72 \(\pm\) 7.52 years, mini-mental state = 22.6667 \(\pm\) 2.45, with a median value of 22) and 36 healthy controls (21 females, 15 males, age \(\pm\) SD = 72.51 \(\pm\) 5.40 years, mini-mental state = 26.6667 \(\pm\) 1.36, with a median value of 29), of the ADNI-2 database were considered. For each subject, only one acquisition was employed. The first acquisition of each subject was used, resulting in a total of 69 acquisitions. Subjects' demographic data are summarized in Table 1.  
This database includes more subjects (around one thousand). However, not every subject presents both morphological and functional images. Furthermore, some patients in the database may be initially classified as AD (or HC), and then, their clinical status is changed. Those patients were excluded from our study, i.e., we employed subjects that did not change their ADNI classification. Moreover, a subject can present more MRI and/or fMRI acquisitions taken at different time steps. The data used in this study were the only that presented contemporary anatomical MRI, functional MRI, and MMSE for each acquisition in the ADNI database. The MMSE feature was employed in this study because it is an accurate indicator of the clinical status and since it is widely used in clinical practice and largely available for most of the subjects included in the ADNI database. Not all subjects from the ADNI database presented the NPI-Q and/or the CDR indexes [29; 30]. Additionally, as regards the Mild Cognitive Impairment (MCI) subjects contained in the database, it should be noted that they present only anatomical MRI or fMRI data, and for this reason, we did not include them in our study. Moreover, in this work, images acquired with the same machine model and sequences were used to avoid problems caused from using different apparatuses and to overcame bias, as well as other problems due to different acquisition methods [4; 32; 35]. A major claim related to the number of subjects used in this study (i.e., 69) could be that the dataset dimension is not sufficient for both statistical analysis and the employment of a supervised approach for patients' classification. However, from the current available literature, studies such as [23; 38] have used a number of subjects between 19 and 28 to investigate the differences between AD patients and healthy subjects. Therefore, the use of a dataset 2.5-times larger should be regarded as an added value. On the other hand, studies make use of a spread and wide dataset (max. six hundreds patients) to perform statistical analysis, investigate the pathology using the graph theory or rely on automatic classification algorithms [26; 39; 40], and have employed a single imaging technique [41; 42], i.e., or MRI or fMRI or PET were used, but never combined together. Moreover, many of them specify the conditions used for all subjects, which would surely be different, thus probably leading to a bias in their results. Our paper has the aim of establishing the effectiveness of analyzing morphological, functional, and clinical features in AD subjects in a synergistic way using a reliable dataset. Resting state data and structural MRI scans were acquired with a 3.0 T Philips Medical System scanner. The acquisitions were performed by ADNI according to its acquisition protocol [26]. The MRI data included high-resolution \(T_{1}\)-weighted scans and an 8-channel receive-only head coil. The parameters included: sagittal plane, slices = 170, TR = 6.77 ms, TE = 3.13 ms, TI= 0 ms, FA = 9\({}^{\circ}\), matrix = 256 \(\times\) 256, and voxel size = 1.0 \(\times\) 1.0 \(\times\) 1.2 mm\({}^{3}\).