Our goal is to use large sample sizes to identify network components representing individual differences, regardless of sample size. We’ll apply dimensionality reduction techniques to brain structural and functional data. By comparing components across different sample sizes, we aim to determine a reliable threshold. While statistical dimensionality reduction helps, we need to clarify which components are interpretable and sample-independent, requiring analysis from larger samples. This research is expected to take two to three years to complete. This research is part of a study on personalized education and support. The findings will help us tailor learning methods to each person’s needs and enhance functionality by considering brain structure or classifying individual types.
