# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
## 2 Materials and Methods
### Adaptions to Methodology  
After reflecting on the results obtained while answering Questions 1-4 in our research methodology, it was important to consider alternative mechanisms for understanding the probability with which a participant will exhibit further disease progression. We found limited evidence that tractography metrics could be useful to predict established neuropsychological and neurobiological biomarkers (Table 6), and we found considerable evidence by using linear mixed effects modeling that tractography metrics exhibit an effect on disease stages while accounting for age (Table 7). Given the high dimensionality of our data and probable loss of meaning resulting from aggressive feature selection, we considered an approach whereby we condensed our features into aggregate measures at the seed level to provide a representative feature as an alternative to feature reduction strategies. Formula (1) represents the \(Z\)-score for a given region measurement of a participant's session. \[Z_{ijkl}=\frac{X_{ijkl}\ -\mu_{k}}{\sigma_{k}} \tag{1}\]  
where \(x_{ijkl}\) is the \(k^{\text{th}}\) measurement (e.g., those identified in Figure 5) for the \(i^{\text{th}}\) participant in the \(j^{\text{th}}\) session from the \(l^{\text{th}}\) seed region targeting a specific region. \(\mu_{kl}\) is the mean of the \(k^{\text{th}}\) measurement, and \(\sigma_{k}\) represents the standard deviation for the \(k^{\text{th}}\) measurement. After scaling the measurements, we determined the mean \(Z\)-score for each participant, session, and ROI (Formula (2)). A value at this aggregated level gives insights into how a participant's ROI may be different from the same region in other participants. For \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Highlights** & **Participants** & **Performance** \\ \hline Classification of current disease stage & & & \\ The study investigated white matter alterations in the Alzheimer’s continuum by using diffusion tensor imaging, finding widespread changes correlated with Tau pathology, particularly in the cingulum, which may serve as a promising biomarker for preclinical Alzheimer’s disease. & 236 ADNI3 subjects (176 CN, 36 MCI, and 24 AD) & 74\% Acc, 69\% AUC, 58\% Sens, and 78\% Spec \\ [PERSON] (2023) [62] & & & \\ [PERSON] (2023) [63] & & & \\ [PERSON] (2023) [63] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative analysis of our proposed model with other models applied to the ADNI dataset. example, mean tract length of all tracts connected to the paracentral cortex for a given participant's session. \[\overline{Z}_{ijk}=\frac{1}{M}\sum_{l=1}^{M}Z_{ijkl} \tag{2}\]  
With a more condensed set of features to move forward with, we continued to rely on an ElasticNet grid search to perform feature selection, resulting in a reduction from 2994 features to 2, relying on an \(\alpha\) value of 0.6. These two features identified were the features representing the hemispheric lateralization of mean tract volume for tracts originating from the supramarginal and paracentral regions. Relying on a dataset with only two features representing these diffusion-derived anatomical measurements, we applied several machine learning models to predict the classification of individuals into two groups: Normal (no conversion) and those who would convert to Alzheimer's within three years. The models tested included eXtreme gradient boosting (XGB), multivariate adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), with results presented in Table 9.  
Overall, the random forest model performed the best, achieving an Area Under the Receiver Operating Characteristic (AUROC) of 0.74, indicating a good ability to differentiate between \"Converter\" and \"Normal\" classes. The Area Under the Precision-Recall Curve (AUPRC) was 0.75, reflecting a strong performance in capturing the \"Converter\" class, which is particularly important given the class imbalance of our original data (see Figure 6).