A Practical Guide to Identifying Robust Clusters in Neuroimaging Data

Clustering algorithms are essential tools in data-driven research, enabling the discovery of hidden structures in complex datasets. In neuroimaging, data-driven research and clustering have been instrumental in identifying and unraveling hidden relationships. However, there are concerns associated with exploratory techniques in that they can provide erroneous results unless properly verified. Here we address such critiques by examining three widely used approaches: K-means, community detection via modularity-maximization, and hierarchical clustering. We first highlight their methodologies, applications, and limitations. We then discuss the critical elements for rigorous validation strategies. By contextualizing clustering within robust methodological frameworks, we demonstrate the potential of clustering-based analyses to reveal meaningful patterns and provide practical guidelines for their application in neuroscience and related fields. Clustering, when appropriately applied, is a powerful and indispensable computational method.