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Review
. 2019 Nov;32(6):926-942.
doi: 10.1007/s10548-019-00744-6. Epub 2019 Nov 9.

Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks

Lucina Q Uddin  1   2 , B T Thomas Yeo  3 , R Nathan Spreng  4   5
Affiliations
Review

Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks

Lucina Q Uddin et al. Brain Topogr. 2019 Nov.

Abstract

The past decade has witnessed a proliferation of studies aimed at characterizing the human connectome. These projects map the brain regions comprising large-scale systems underlying cognition using non-invasive neuroimaging approaches and advanced analytic techniques adopted from network science. While the idea that the human brain is composed of multiple macro-scale functional networks has been gaining traction in cognitive neuroscience, the field has yet to reach consensus on several key issues regarding terminology. What constitutes a functional brain network? Are there "core" functional networks, and if so, what are their spatial topographies? What naming conventions, if universally adopted, will provide the most utility and facilitate communication amongst researchers? Can a taxonomy of functional brain networks be delineated? Here we survey the current landscape to identify six common macro-scale brain network naming schemes and conventions utilized in the literature, highlighting inconsistencies and points of confusion where appropriate. As a minimum recommendation upon which to build, we propose that a scheme incorporating anatomical terminology should provide the foundation for a taxonomy of functional brain networks. A logical starting point in this endeavor might delineate systems that we refer to here as "occipital", "pericentral", "dorsal frontoparietal", "lateral frontoparietal", "midcingulo-insular", and "medial frontoparietal" networks. We posit that as the field of network neuroscience matures, it will become increasingly imperative to arrive at a taxonomy such as that proposed here, that can be consistently referenced across research groups.

Keywords: Coactivation; Functional connectivity; Human connectome; Network neuroscience.

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Figures

Figure 1.
Figure 1.. Taxonomy of functional brain networks.
In our proposed taxonomy, networks are referred to by anatomical names that best describe six ubiquitous large-scale functional systems. The names in blue refer to the broad cognitive domains with which a given anatomical system is most commonly associated. Only 1-2 core nodes of each network are depicted here, though it is understood that multiple additional cortical, subcortical, and cerebellar nodes may be affiliated with a given network.
Figure 2.
Figure 2.. Occipital network.
A) Medial (120), occipital pole (220), and lateral (320) visual areas (Smith et al. 2009). RSN = resting state network, BM = BrainMap meta-analytic activation maps. B) Purple and red visual networks in 17-network parcellation (Yeo et al. 2011). C) Medial (tan) and lateral (blue) visual networks (Gordon et al. 2017c).
Figure 3.
Figure 3.. Pericentral network.
A) Sensorimotor areas in 20 (left) and 70 (right) component ICA solutions (Smith et al. 2009). RSN = resting state network, BM = BrainMap meta-analytic activation maps. B) Blue network in 7-network parcellation (Yeo et al. 2011). C) Hand (light blue), face (orange), and foot (green) somatomotor comprise three networks. Another network labeled auditory/premotor/parietal memory is also included (Gordon et al. 2017c).
Figure 4.
Figure 4.. Dorsal frontoparietal network.
A) Coactivation map based on coordinates in left intraparietal cortex (Toro et al. 2008). B) Green network in 7-network parcellation (Yeo et al. 2011). C) Dorsal attention network (yellow) (Corbetta and Shulman 2011). IPS/SPL = intraparietal sulcus/superior parietal lobule, FEF = frontal eye fields, IFJ = inferior frontal junction. D) Dorsal attention network (green) (Gordon et al. 2017c).
Figure 5.
Figure 5.. Lateral frontoparietal network.
A) “Left and right frontoparietal” (920 and 1020)(Smith et al. 2009). RSN = resting state network, BM = BrainMap meta-analytic activation maps. B) Orange network in 7-network parcellation (Yeo et al. 2011). C) Cognitive control/executive function network from meta-analysis (Niendam et al. 2012). D) Fronto-parietal network (yellow) (Gordon et al. 2017c).
Figure 6.
Figure 6.. Midcingulo-insular network.
A) Salience network (Seeley et al. 2007). B) Functional connectivity of different nodes of the ventral attention network (Yeo et al. 2011). C) Ventral attention network (Corbetta and Shulman 2011). SMG = supramarginal gyrus, STG = superior temporal gyrus, IFJ = inferior frontal junction, IFG = inferior frontal gyrus, Ins = insula. D) Cingulo-opercular network (violet) from cortical-subcortical atlas (Ji et al. 2019). E) Cingulo-opercular, salience, and ventral attention networks (Gordon et al. 2017c).
Figure 7.
Figure 7.. Medial frontoparietal network.
a) Functional connectivity of posterior cingulate seed (Greicius et al. 2003). b) Default mode network (420) (Smith et al. 2009). RSN = resting state network, BM = BrainMap meta-analytic activation maps. c) Functional connectivity of different nodes of the default network (Yeo et al. 2011). d) Medial temporal subsystem (green), dorsal medial subsystem (blue) and core (yellow) of the default network (Andrews-Hanna et al. 2014). e) Default network (red) and adjacent language network (teal) from cortical-subcortical atlas (Ji et al. 2019). f) Default network (red) (Gordon et al. 2017c).
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