# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
## 2 Materials and Methods
### Statistics
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Phenotypic Characteristic** & **Range** & **Description** \\ \hline FAQ (neuropsychological) & 0-30 & The Functional Activities Questionnaire (FAQ) measures instrumental activities of daily living such as preparing balanced meals and preparing finances. A cut-point of 9 (dependent in 3 or more activities) is recommended to indicate impaired function and possible cognitive impairment. \\ \hline MMSE (neuropsychological) & 0-30 & Mini Mental State Examination. It is an 11-question measure that tests five areas of cognitive function: orientation, registration, attention and calculation, recall, and language. The maximum score is 30. A score of 23 or lower is indicative of cognitive impairment. \\ \hline MoCA (neuropsychological) & 0-30 & Montreal Cognitive Assessment Test for Dementia. This test is a 30-item test of language, memory, visual and spatial thinking, reasoning, and orientation skills. A score of 26 or above is considered normal. \\ \hline RAVLT Immediate (neuropsychological) & 0-30 & Rey Auditory Verbal Learning Test evaluating short-term memory, working memory, and long-term memory. RAVLT Immediate is the sum of scores from the 5 first trials. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._and gradient boosting [47]. The repeated cross-validation approach minimized variability in performance metrics due to random partitioning. We evaluated our results by using RMSE, MAE, and R2 metrics. This multi-step process is depicted in Figure 1.  
Q3. Does baseline tract volume change with cognitive decline? Brain atrophy is a major symptom of AD observed in vivo [48; 49; 50]. There is compelling evidence to suggest that A\(\beta\) facilitates the spread of Tau neurofibrillary tangles, which may then drive neurodegeneration, atrophy, and subsequent dementia [20]. While almost all aged brains show characteristic changes linked to neurodegeneration [51], Alzheimer's disease has different neurodegenerative processes compared with normal ageing, with distinctive neuron loss profiles [6]. This atrophy is understood to begin in the entorhinal cortex, progressing then to the hippocampus, temporal, frontal, and parietal areas, before spreading to the entire cerebral cortex [19; 22]. DWI tractography enables the reconstruction of white matter tract bundles by estimating the principal directions of diffusion within a voxel, thereby enabling the segmenting of tracts and providing tract-specific measures such as volume, MD, and FA. Since DWI does not directly measure neurons themselves, but rather assesses the diffusion characteristics of water in tissue, these metrics represent water, not the neurons. We investigated the changes in tract volume, MD, and FA across different stages of cognitive impairment. Our analyses aimed to elucidate the relationship between these neuroimaging biomarkers and the progression from cognitively normal (CN) status to mild cognitive impairment (MCI) and Alzheimer's disease. Linear mixed effects models were employed to assess the effects of diagnosis and age on tract volume, MD, and FA, accounting for random effects due to individual differences. Disease-related changes were examined by using linear mixed effects models to understand the interaction of disease stages, age, tract volume, MD, FA, and tract length. We employed the lmer function from the R package lme4 [52]. Specifically, the model was formulated as \(y_{ij}=\beta_{0}+\beta_{1}\times x_{1 ij}+\beta_{2}\times x_{2 ij}+u_{j}+e_{ij}\), where \(y_{ij}\) represents the tractography measurement (tract volume, MD, FA, and length were each considered separately) for the \(i^{th}\) observation within the \(j^{th}\) subject. The predictors \(x_{1 ij},x_{2 ij}\) represent the fixed effects disease stage and age. The term \(u_{j}\) is the random intercept for subject \(j\), assumed to follow  
Figure 1: Processing steps of the feature selection framework and the subsequent regression of the neuropsychological and neurobiological response variables.