## 3 Methods
### Image Preprocessing  
For each subject, the DICOM images were acquired from the ADNI database, The _dcm2 nii_ tool, included in the _MRlcron_ suite, was used to convert the DICOM images into the NIFTI format. The NIFTI images were then organized in the standard BIDS format. The other processing steps, from image preprocessing to connectome reconstruction, were carried out with tools provided within the _MRtrix3_ software package ([[http://www.mrtrix.org/](http://www.mrtrix.org/)]([http://www.mrtrix.org/](http://www.mrtrix.org/))) and the _FMRIB Software Library (FSL)_ ([[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki)]([https://fsl.fmrib.ox.ac.uk/fsl/fslwiki](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki))). The main steps of the whole processing, which are well-established in the literature, are shown in Figure 1 and they are outlined in the following. First, a denoising step was performed in order to enhance the signal-to-noise ratio of the diffusion weighted MR signals so as to reduce the thermal noise. This noise is due to the stochastic thermal motion of the water molecules and their interaction with the surrounding micro-structure [19]. Head motion and eddy current distortions were corrected by aligning the DWI images of each subject to the average \(b_{0}\) image. The _brain extraction tool_ (BET) was then used for the skull-stripping of the brain [20]. The bias-field correction was used to correct all DWI volumes. Similarly, the T1 weighted scans were preprocessed by performing the following steps: reorientation to the standard image MNI152, automatic cropping, bias-field correction, registration to the linear and non-linear standard space, brain extraction. The next step was the inter-modal registration of the diffusion weighted and T1 weighted image. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **HC (40)** & **AD (40)** & _p-Value_ \\ \hline
**Age** & 73.06 \(\pm\) 5.54 & 75.8 \(\pm\) 9.16 & 0.11 \\
**Gender** & 21 M/19 F & 25 M/15 F & 0.18 \\
**MMSE** & 29.14 \(\pm\) 1.02 & 23.38 \(\pm\) 1.88 & \textless{}0.0001 \\
**ADAS 11** & 5.42 \(\pm\) 3.14 & 20.85 \(\pm\) 9.16 & \textless{}0.0001 \\
**ADAS 13** & 8.78 \(\pm\) 4.75 & 31.01 \(\pm\) 8.23 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic and clinical characteristics of the study participants. For the clinical assessment, the mini-mental state examination test (MMSE), Alzheimer’s disease assessment scale (ADAS) 11 [17] and ADAS 13 [18] scores are reported. According to the t-test statistics, MMSE, ADAS 11 and ADAS 13 are significantly different between healthy controls (HC) and Alzheimer’s disease (AD). For age and gender, the chi-squared test was performed. After the preprocessing and co-registration steps, we performed the structural connectome generation. First, we generated a tissue-segmented image tailored to the anatomically constrained tractography [21]. Then, we performed an unsupervised estimation of WM, gray matter and cerebro-spinal fluid. In the next step, the fiber orientation distributions for spherical deconvolution was estimated [22]. We then performed a probabilistic tractography [23] using dynamic seeding [24] and anatomically-constrained tractography [25], which improves the tractography reconstruction by using anatomical information through a dynamic thresholding strategy. We applied the spherical-deconvolution informed filtering of tractograms (SIFT2) methodology [24], which not only provides more biologically meaningful estimates of the structural connection density, but also a more efficient solution to the streamlines connectivity quantification problem. The obtained streamlines were mapped through a T1 parcellation scheme by using the AAL2 atlas [26], which is a revised version of the automated anatomical atlas (AAL) including 120 regions. Finally, a robust structural connectome construction was performed for generating the connectivity matrices [27]. The pipeline here described has been used in recent structural connectivity studies, for example [28; 29].