# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
## 4 Discussion
### Limitations  
Limited sample sizes in neuroimaging studies can compromise the reliability and validity of the findings reported [88]. Small sample sizes reduce the statistical power, increasing the likelihood of Type 1 (false positive) and Type 2 (false negative) errors. While the original ADNI-3 cohort is larger than many studies (960 subjects across 6050 scans), many subjects lacked diffusion-weighted imaging data. Our study included 264 participants with a limited number of scan sessions per participant (between one and five).  
We acknowledge the potential source of error resulting from the smoothing effects of interpolation as a result of registering DWI images to T1. Our pipeline strategy was initially developed for a large dataset of noisy clinical data [26; 29], where it was determined after many approaches that registration of DWI to T1-weighted images before ODF reconstruction offered the most reliable alignment approach with the highest number of successful registrations. It was felt that lower rates of successful registration were highly undesirable and could potentially skew the results of analyses more so than the error associated with the simple smoothing that results from the interpolation process.  
We relied on single-shell DWI, where diffusion measurements are acquired with a single b-value. Single-shell DWI has been shown to underperform in resolving complex fiber configurations within a voxel compared with multi-shell DWI [89]. The limitations of single-shell DWI mean that our study might not fully capture the complexity of white matter architecture, particularly in regions where multiple fiber pathways intersect [90]. This can potentially lead to the mischaracterization of fiber tract integrity and connectivity, especially with respect to the tract length measurement we considered in our study. Consequently, our findings regarding tract length might be less reliable than if we had access to more sensitive multi-shell images that offer opportunities for improved delineation of crossing fibers.  
Given the breadth of our data, we relied on aggressive feature reduction strategies to make machine learning feasible. This included only considering the features with the highest variability (top 10%) after removing features with low variance or highly correlated redundant features. This may have resulted in the exclusion of potentially important data that were never introduced during model training. As a consequence, there may be significant characteristics within our data that could have enhanced model performance but were excluded early in the process.  
The results of our adapted machine learning strategy are promising, though they offer room for improvement. While it is encouraging to achieve this classification accuracy with only two measures based on the hemispheric lateralization of mean tract volume for tracts originating from the supramarginal and paracentral regions, there are specific limitations that should be addressed. Our highly imbalanced proportion of Converter to Normal participants (14/434) necessitated a reliance on synthetic data to better balance for reliable predictions. This deficiency is likely a contributing factor to our low sensitivity scores, as synthetic data do not perfectly capture the complexity and variability of real-world data. The limited sample size restricts the statistical power and generalizability of our findings. More data would enhance the model training process of our ML models, allowing for better feature learning and reducing the risk of overfitting. In particular, increasing the number of individuals who exhibit progressive disease pathology would help provide a more balanced dataset.