Simone Di Plinio ^1* Georg Northoff ² Sjoerd J. Ebisch ¹

• ¹ University of Studies G. d'Annunzio Chieti and Pescara, Chieti, Italy
• ² Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada

This study explores the concept of degeneracy in the brain-behavior code, proposing methods to unveil multiple non-linear associations between psychometric profiles and resting-state functional connectivity (RSFC). Degeneracy refers to the ability of different neural configurations to support similar functions, highlighting the brain's adaptability and robustness. By analyzing RSFC data from 500 subjects, clustering using self-organized maps (SOM) revealed distinct archetypal connectivity profiles, each with unique within-and between-network connectivity patterns. These archetypes correlated with various cognitive, physical, and socioemotional outcomes, providing an advanced understanding of the brain-behavior relationship. Crucially, this study demonstrates that the interaction between different SOM dimensions is associated with specific psychometric profiles, including cognitive abilities, physical performance, and socioemotional dimensions. Our findings underscore the complexity of brain-behavior interactions and illustrate how multiple brain architectures can lead to similar behavioral outcomes. This research not only advances our knowledge of neural degeneracy but also showcases the utility of non-linear and multidimensional machine learning approaches in psychometrics and neuroscience, capturing complex patterns of functional connectivity that traditional linear models may overlook. Implications for understanding the role of neural degeneracy in cognitive and emotional health are discussed.