Benchmarking functional connectivity by the structure and geometry of the human brain

被引：12
作者：
Liu Z.-Q. [1 ]
Betzel R.F. [2 ]
Misic B. [1 ]
机构：
[1] McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal
[2] Psychological and Brain Sciences, Indiana University, Bloomington, IN
来源：
Network Neuroscience | 2022年 / 6卷 / 04期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Connectome; Embedding; Functional connectivity; Geometry; Gradient; Hierarchy; Structural connectivity; Transmodal;
D O I：
10.1162/netn_a_00236
中图分类号：
学科分类号：
摘要：
The brain’s structural connectivity supports the propagation of electrical impulses, manifesting as patterns of coactivation, termed functional connectivity. Functional connectivity emerges from the underlying sparse structural connections, particularly through polysynaptic communication. As a result, functional connections between brain regions without direct structural links are numerous, but their organization is not completely understood. Here we investigate the organization of functional connections without direct structural links. We develop a simple, data-driven method to benchmark functional connections with respect to their underlying structural and geometric embedding. We then use this method to reweigh and reexpress functional connectivity. We find evidence of unexpectedly strong functional connectivity among distal brain regions and within the default mode network. We also find unexpectedly strong functional connectivity at the apex of the unimodal-transmodal hierarchy. Our results suggest that both phenomena—functional modules and functional hierarchies— emerge from functional interactions that transcend the underlying structure and geometry. These findings also potentially explain recent reports that structural and functional connectivity gradually diverge in transmodal cortex. Collectively, we show how structural connectivity and geometry can be used as a natural frame of reference with which to study functional connectivity patterns in the brain. © 2022 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
引用
收藏
页码：937 / 949
页数：12
相关论文
共 64 条
[1]  
Akarca D., Vertes P.E., Bullmore E.T., Astle D.E., A generative network model of neurodevelopmental diversity in structural brain organization, Nature Communications, 12, 1, (2021)
[2]  
Alexander-Bloch A.F., Shou H., Liu S., Satterthwaite T.D., Glahn D.C., Shinohara R.T., Raznahan A., On testing for spatial correspondence between maps of human brain structure and function, Neuroimage, 178, pp. 540-551, (2018)
[3]  
Aquino K.M., Fulcher B.D., Parkes L., Sabaroedin K., Fornito A., Identifying and removing widespread signal deflec-tions from fMRI data: Rethinking the global signal regression problem, Neuroimage, 212, (2020)
[4]  
Baum G.L., Cui Z., Roalf D.R., Ciric R., Betzel R.F., Larsen B., Satterthwaite T.D., Development of structure–function coupling in human brain networks during youth, Proceedings of the National Academy of Sciences of the United States of Amer-Ica, 117, 1, pp. 771-778, (2020)
[5]  
Bazinet V., Vos de Wael R., Hagmann P., Bernhardt B.C., Misic B., Multiscale communication in cortico-cortical networks, Neuroimage, 243, (2021)
[6]  
Bellec P., Rosa-Neto P., Lyttelton O.C., Benali H., Evans A.C., Multi-level bootstrap analysis of stable clusters in resting-state fMRI, Neuroimage, 51, 3, pp. 1126-1139, (2010)
[7]  
Betzel R.F., Avena-Koenigsberger A., Goni J., He Y., de Reus M.A., Griffa A., Sporns O., Generative models of the human connectome, Neuroimage, 124, pp. 1054-1064, (2016)
[8]  
Betzel R.F., Griffa A., Hagmann P., Misic B., Distance-dependent consensus t hr eshol ds f or gener at i ng gr oup-representative structural brain networks, Network Neuroscience, 3, 2, pp. 475-496, (2018)
[9]  
Bullmore E., Sporns O., The economy of brain network organization, Nature Reviews Neuroscience, 13, 5, pp. 336-349, (2012)
[10]  
Burt J.B., Demirtas M., Eckner W.J., Navejar N.M., Ji J.L., Martin W.J., Murray J.D., Hierarchy of transcriptomic specialization across human cortex captured by structural neuro-imaging topography, Nature Neuroscience, 21, 9, pp. 1251-1259, (2018)
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