Recent advancements in neuroimaging data acquisition are revolutionizing the field of neuroscience, enhancing understanding of brain function/dysfunction, and improving clinical applications for diagnosis/treatment. Open-source multimodal data initiatives such as the Human Connectome Project and UK Biobank are making multimodal data accessible to researchers and clinicians worldwide, enabling collective and collaborative research. However, it is critical to address issues such as harmonizing datasets acquired at different centers with varying scanner settings and developing multimodal methods to combine information to fully exploit the data. While laudable efforts are on for creating these datasets, development of methods for analysis needs further progress. To characterize neuronal disorders and disease progression from these datasets, an extensive exploration of analytical methods is needed. Researchers with a background in signal/image processing, and machine/deep learning have collaborated successfully in several areas such as speech, NLP, vision, thereby benefiting all the involved fields. Likewise, neuroimaging is poised to benefit from such a collaborative effort. This workshop aims to attract researchers across fields and introduce them to these datasets and their analysis. By fostering collaboration and innovation on Neuroimaging datasets, we aim to advance analytical methods and data analytics techniques to significantly impact neuroscience research and clinical applications.