Abstract
Neuroimaging has significantly advanced our understanding of brain structure and function in both healthy and diseased states. However, due to the brain's complexity, a comprehensive approach involving multiple imaging techniques is necessary. This review provides an overview of the integration of various imaging modalities in neurology, particularly focusing on their applications in mapping the brain. It discusses how combining structural, functional, and molecular imaging methods such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Functional Magnetic Resonance Imaging (fMRI) allows for a more thorough examination of brain anatomy, connectivity, and understanding, thereby improving diagnosis and treatment effectiveness in neurological disorders. The review also explores recent progress in image acquisition, data fusion, and computational techniques that facilitate the integration of multimodal imaging data. Additionally, it examines emerging trends like machine learning and artificial intelligence, which offer potential for automating image analysis and identifying new disease biomarkers. Finally, by addressing challenges and future directions in the field, including the need for standardized protocols, initiatives for data sharing, and ethical considerations.