TY - JOUR
T1 - A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity
AU - Tanner, Jacob
AU - Faskowitz, Joshua
AU - Teixeira, Andreia Sofia
AU - Seguin, Caio
AU - Coletta, Ludovico
AU - Gozzi, Alessandro
AU - Mišić, Bratislav
AU - Betzel, Richard F.
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features–e.g. diffusion parameters–or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.
AB - The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features–e.g. diffusion parameters–or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.
UR - http://www.scopus.com/inward/record.url?scp=85198386116&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85198386116&partnerID=8YFLogxK
U2 - 10.1038/s41467-024-50248-6
DO - 10.1038/s41467-024-50248-6
M3 - Article
C2 - 38997282
AN - SCOPUS:85198386116
SN - 2041-1723
VL - 15
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 5865
ER -