A Practical Guide to Identifying Robust Clusters in Neuroimaging Data

Abstract

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 this issue 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 steps for rigorous validation strategies. We further show how to apply these steps using both synthetic and real data, and provide code to facilitate their application. 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.