<ul data-eligibleForWebStory="true"><li>Neuroimaging-based patient stratification holds promise for precision neuropsychiatry.</li><li>Dataset characteristics such as cluster separation, size imbalance, noise, and disease-related effects influence clustering algorithm success.</li><li>Four widely used stratification algorithms were evaluated on synthetic brain-morphometry cohorts.</li><li>Data complexity was found to be more crucial than the choice of algorithm for successful stratification.</li><li>Well-separated clusters yielded high accuracy, while overlapping or unequal-sized clusters reduced accuracy.</li><li>SuStaIn had limitations in scaling, HYDRA's accuracy varied with data heterogeneity, SmileGAN and SurrealGAN detected patterns but did not assign discrete labels.</li><li>The study stresses the importance of dataset properties in shaping algorithm success and calls for realistic dataset distributions in algorithm development.</li></ul>

Dataset Properties Shape the Success of Neuroimaging-Based Patient Stratification: A Benchmarking Analysis Across Clustering Algorithms

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