  The brain is a complex system comprising a myriad of interacting elements,
posing significant challenges in understanding its structure, function, and
dynamics. Network science has emerged as a powerful tool for studying such
intricate systems, offering a framework for integrating multiscale data and
complexity. Here, we discuss the application of network science in the study of
the brain, addressing topics such as network models and metrics, the
connectome, and the role of dynamics in neural networks. We explore the
challenges and opportunities in integrating multiple data streams for
understanding the neural transitions from development to healthy function to
disease, and discuss the potential for collaboration between network science
and neuroscience communities. We underscore the importance of fostering
interdisciplinary opportunities through funding initiatives, workshops, and
conferences, as well as supporting students and postdoctoral fellows with
interests in both disciplines. By uniting the network science and neuroscience
communities, we can develop novel network-based methods tailored to neural
circuits, paving the way towards a deeper understanding of the brain and its
functions.
