The field of neuroimaging analysis is moving towards more sophisticated and nuanced methods for analyzing and interpreting brain imaging data. This is driven by the need to better understand the complex relationships between brain structure and function, and to develop more accurate and reliable diagnostic tools for neurological disorders. Recent developments have focused on incorporating multi-frequency information into functional connectivity networks, harmonizing diffusion-weighted MRI data across different acquisition sites, and developing more effective methods for synthesizing missing modalities in MRI data. These advances have the potential to improve our understanding of neurodegenerative diseases and to enhance clinical decision-making. Notable papers in this area include: Ada-FCN, which proposes a novel framework for adaptive frequency-coupled network analysis; Clinical-ComBAT, which introduces a method for harmonizing diffusion-weighted MRI data in clinical applications; Pattern-Aware Diffusion Synthesis, which presents a new approach for synthesizing fMRI and dMRI data; and Cancer-Net PCa-MultiSeg, which demonstrates the effectiveness of synthetic correlated diffusion imaging for enhancing prostate cancer lesion segmentation.