Revisiting correlation-based functional connectivity and its relationship with structural connectivity

• Published in: Network neuroscience (Cambridge, Mass.) Vol. 4; no. 4; pp. 1235 - 1251
• Main Authors: Liégeois, Raphael, Santos, Augusto, Matta, Vincenzo, Van De Ville, Dimitri, Sayed, Ali H.
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 2020; MIT Press Journals, The; The MIT Press
• Subjects: Brain; Correlation; fMRI; Functional anatomy; Functional connectivity; Informatics; Medical imaging; Multimodal modeling; Neural networks; Neuroimaging; Neurosciences; Partial correlation; Precision matrix; Structural connectivity; Structure-function relationships; Time series; Functional connectivity; fMRI; Precision matrix; Structural connectivity; Multimodal modeling; Partial correlation
• ISSN: 2472-1751; 2472-1751
• DOI: 10.1162/netn_a_00166

Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from between functional time series, and more recently from or their unnormalized version encoded in the matrix. The latter FC metrics yield more meaningful comparisons to SC because they capture ‘direct’ statistical dependencies, that is, discarding the effects of mediators, but their use has been limited because of estimation issues. With the rise of high-quality and large neuroimaging datasets, we revisit the relevance of different FC metrics in the context of SC-FC comparisons. Using data from 100 unrelated Human Connectome Project subjects, we first explore the amount of functional data required to reliably estimate various FC metrics. We find that precision-based FC yields a better match to SC than correlation-based FC when using 5 minutes of functional data or more. Finally, using a linear model linking SC and FC, we show that the SC-FC match can be used to further interrogate various aspects of brain structure and function such as the timescales of functional dynamics in different resting-state networks or the intensity of anatomical self-connections.