Dynamic fingerprinting of the human functional connectome

Ghaffari, A.; Zhao, Y.; Chen, X.; Langley, J.; Hu, X.

doi:10.1101/2025.02.20.637919

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bioRxiv · 10.1101/2025.02.20.637919

Dynamic fingerprinting of the human functional connectome

Ghaffari, A.·Zhao, Y.·Chen, X.·Langley, J.·Hu, X.

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Abstract

Resting-state functional connectivity (FC) have distinct, personalized patterns that could serve as a unique fingerprint of each individuals brain. While previous brain fingerprinting methods have used functional connectivity maps over a scanning session (static method), it has been shown that the brain is a dynamic system that switches between several metastable states, each of which having a different FC map. Taking the dynamic nature of brain connectivity into account will likely lead to more subject-specific information and better individual identification. In this paper, we derived the state-specific FCs using sliding window correlation and clustering and evaluated their performance in individual identification and cognitive score prediction. The resultant dynamic fingerprints outperformed the static fingerprints in identification accuracy. Furthermore, some of the brain states were more accurate in predicting cognitive scores, indicating that connectivity in some brain states is informative of cognition abilities, possibly useful as biomarkers for brain disorders. Impact StatementOur findings suggest that state-specific functional connectivity patterns of the brain are unique for each individual. These brain states predicted participants cognitive performance, suggesting that they have the potential to be used as biomarkers for cognitive function or neurological disorders. Integrating state-based functional connectivity into clinical frameworks could potentially enhance early diagnosis and patient stratification, and lead to targeted interventions for neuropsychiatric and neurodegenerative conditions.

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Ghaffari, A., Zhao, Y., Chen, X., Langley, J., Hu, X.. 2025-02-25. Dynamic fingerprinting of the human functional connectome. https://doi.org/10.1101/2025.02.20.637919

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