Partial correlation as a tool for mapping functional-structural correspondence in human brain connectivity

Brain structure-function coupling has been studied in health and disease by many different researchers in recent years. Most of the studies have estimated functional connectivity matrices as correlation coefficients between different brain areas, despite well-known disadvantages compared with partia...

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Veröffentlicht in:Network neuroscience (Cambridge, Mass.) Jg. 9; H. 3; S. 1065 - 1086
Hauptverfasser: Santucci, Francesca, Jimenez-Marin, Antonio, Gabrielli, Andrea, Bonifazi, Paolo, Ibáñez-Berganza, Miguel, Gili, Tommaso, Cortes, Jesus M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA MIT Press 29.09.2025
Schlagworte:
fMRI BOLD resting state
Functional connectivity
Partial correlation
Precision matrix
Regularization
Structural-functional coupling
Functional connectivity
Precision matrix
Regularization
Structural-functional coupling
fMRI BOLD resting state
Partial correlation
ISSN:2472-1751, 2472-1751
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Zusammenfassung:Brain structure-function coupling has been studied in health and disease by many different researchers in recent years. Most of the studies have estimated functional connectivity matrices as correlation coefficients between different brain areas, despite well-known disadvantages compared with partial correlation connectivity matrices. Indeed, partial correlation represents a more sensible model for structural connectivity since, under a Gaussian approximation, it accounts only for direct dependencies between brain areas. Motivated by this and following previous results by different authors, we investigate structure-function coupling using partial correlation matrices of functional magnetic resonance imaging brain activity time series under various regularization (also known as noise-cleaning) algorithms. We find that, across different algorithms and conditions, partial correlation provides a higher match with structural connectivity retrieved from density-weighted imaging data than standard correlation, and this occurs at both subject and population levels. Importantly, we also show that regularization and thresholding are crucial for this match to emerge. Finally, we assess neurogenetic associations in relation to structure-function coupling, which presents promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders. A precise understanding of how brain structure and function interact is fundamentally relevant to understanding disease. For the functional representation, most of the previous research has used correlation methods, which have limitations. Our study explores a different approach called partial correlation methods, which more accurately reflect the brain’s direct connections. We found that partial correlation aligns better with the brain’s structural connectivity than standard methods, both in individuals and groups. Additionally, we identified promising links between brain connectivity and genetics, offering new insights into brain disorders. Our work highlights the importance of using advanced connectivity methods to improve our understanding of the brain’s structure-function relationship, paving the way for future research in brain health and disease.
Bibliographie:2025
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SourceType-Scholarly Journals-1
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ISSN:2472-1751
2472-1751
DOI:10.1162/NETN.a.22