# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
## 2 Materials and Methods
### Preprocessing of MRI Data and Estimation of Structural Networks  
We developed a pipeline management software package [26] offering a robust, fault-tolerant, and extensible platform to execute the processing workflows of the ADNI data. The pipeline code was containerized by using Apptainer [27] to facilitate reproducibility as well as environment management during pipeline execution on each of the supercomputing clusters in which software was executed. We sought to process all ADNI3 participant sessions that contained both a T1-weighted image and a diffusion-weighted image. A total of 961 participants, totaling 1873 exam sessions, were considered for our study. After excluding scans without DWI images or bval/bvec files, 264 participants represented in 434 sessions were processed by our pipeline. The preprocessing pipeline to extract tractography metrics from the ADNI dataset has been described elsewhere [28; 29]. For the ADNI dataset, T1w MRI images were segmented into sub-regions by using the FreeSurfer software package, version 7.2 [30], with cortical [31] and subcortical [30] labeling pipelines. The white matter volume generated by Freesurfer's recon-all was further separated into 181 regions of interest (ROIs) by using the Freesurfer program mri_extract_label. Labels were extracted by using the Desikan-Killiany atlas [31]. The diffusion-weighted image was registered to the T1w image before orientation distribution function (ODF) estimation was performed. ODF maps were created from the preprocessed DWI images by using the Diffusion Toolkit (DTK v0.6.4) [32] software package. The HARDI/Q-ball imaging model [33] with a fiber orientation distribution function was estimated at each voxel. The Fiber Assignment by Continuous (FACT)-alike tracking algorithm [32; 34] was employed for deterministic fiber tracking. Seed points for tractography were generated throughout the entire brain volume where valid diffusion data existed, using a 35\({}^{\circ}\) angle threshold for stopping criteria. This whole-brain tract file was constructed by using the odf_recon and odf_tracker utilities from DTK. \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Diagnosis at Imaging** & **Progression Profile** & **Subjects (F-M)** & **Mean Age (F-M)** & **SD (F-M)** \\ \hline CN & CN & 126:92 & 71:78 & 7.4:7.7 \\ CN & CN \(\rightarrow\) MCI & 5:4 & 74:79 & 10.8:8.6 \\ CN & CN \(\rightarrow\) AD & 1:0 & 88: - & \(\sim\)- \\ MCI & MCI & 60:75 & 76:76 & 7.7:6.9 \\ MCI & CN \(\rightarrow\) MCI & 4:5 & 73:82 & 13.4:5.8 \\ MCI & MCI \(\rightarrow\) AD & 2:1 & 82:83 & 8.1: \\ AD & AD & 28:27 & 78:76 & 7.9:8.4 \\ AD & CN \(\rightarrow\) AD & 1:0 & 87: - & \(\sim\)- \\ AD & MCI \(\rightarrow\) AD & 2:1 & 83:85 & 7.6:7.7 \\ \hline \hline \end{tabular} Note: CN = cognitively normal, MCI = mild cognitive impairment, and AD = Alzheimer’s disease.  
\end{table} 
Table 2: Stratification of ADNI images and associated demographic and clinical details. The generated tract file and individual white matter ROI masks were postprocessed for the extraction of 32,580 tracts (cartesian product of ROIs), with multiple measures extracted for each, including mean tract length, tract volume, mean fractional anisotropy (FA), FA standard deviation, mean diffusivity (MD) calculated by using the mean of the three eigenvalues, MD standard deviation, and the corresponding left-right asymmetries of each of these measurements (12 measurements in total). We derived these measurements for tracts that started or terminated in our ROIs as well as tracts that passed through our ROIs, for a total of 781,920 measurements per scan session. White matter tract identification was assessed by using q-ball imaging [33; 35].