A Practical Guide to Identifying Robust Clusters in Neuroimaging Data.

Area of Science:

• Neuroscience
• Computational Biology
• Data Science

Background:

• Clustering algorithms are vital for uncovering hidden structures in complex datasets.
• In neuroimaging, clustering aids in identifying intricate relationships within data.
• Exploratory data analysis techniques, including clustering, require rigorous validation to prevent erroneous findings.

Purpose of the Study:

• To examine and validate three common clustering approaches: K-means, community detection, and hierarchical clustering.
• To address concerns regarding the reliability of exploratory data analysis in neuroimaging.
• To provide practical guidelines and code for applying robust validation strategies to clustering methods in neuroscience.

Main Methods:

• Methodologies, applications, and limitations of K-means, community detection, and hierarchical clustering were reviewed.
• Critical steps for rigorous validation strategies were discussed.
• Validation steps were demonstrated using both synthetic and real neuroimaging data.

Main Results:

• The study highlights the importance of validation for clustering algorithms in neuroimaging.
• Demonstrated the application of validation strategies using synthetic and real data.
• Provided functional code to facilitate the application of these validation techniques.

Conclusions:

• Clustering, when appropriately applied and validated, is a powerful tool for data-driven research in neuroscience.
• Robust methodological frameworks enhance the reliability of clustering-based analyses.
• The findings offer practical guidelines for the effective use of clustering in neuroimaging and related fields.