The recent advancements in neuroimaging research have significantly enhanced our understanding of brain function and structure through innovative fusion techniques and novel data analysis methods. A notable trend is the integration of multiple imaging modalities, such as functional MRI (fMRI) and structural MRI (sMRI), to capture both temporal and spatial dynamics of brain networks. This approach not only preserves the rich temporal information of fMRI but also leverages the structural insights from sMRI, leading to more comprehensive models of brain activity. Additionally, the application of topological data analysis, particularly persistent homology, has shown promise in classifying mild cognitive impairment (MCI) subtypes by capturing subtle topological changes in brain connectivity. These methods are proving to be highly sensitive in detecting early signs of neurodegeneration, which is crucial for early diagnosis and intervention. Furthermore, advancements in deep learning and generative models are being utilized to forecast brain activity, enhancing the classification and interpretation of conditions like Alzheimer's disease. These models, when combined with data augmentation techniques, are overcoming the limitations posed by small datasets, thereby improving the generalizability and accuracy of predictions. Lastly, novel frameworks for anatomical feature embedding are addressing the challenge of establishing cross-subject correspondences in brain networks, which is essential for constructing reliable connectomes. These developments collectively underscore the transformative potential of integrating advanced computational methods with neuroimaging data to advance our understanding of brain function and pathology.