Evaluation of the spatial variability in the major resting‐state networks across human brain functional atlases

Abstract

The human brain is intrinsically organized into resting‐state networks (RSNs). Currently, several human brain functional atlases are used to define the spatial constituents of these RSNs. However, there are significant concerns about interatlas variability. In response, we undertook a quantitative comparison of the five major RSNs (default mode [DMN], salience, central executive, sensorimotor, and visual networks) across currently available brain functional atlases (n = 6) in which we demonstrated that (a) similarity between atlases was modest and positively linked to the size of the sample used to construct them; (b) across atlases, spatial overlap among major RSNs ranged between 17 and 76% (mean = 39%), which resulted in variability in their functional connectivity; (c) lower order RSNs were generally spatially conserved across atlases; (d) among higher order RSNs, the DMN was the most conserved across atlases; and (e) voxel‐wise flexibility (i.e., the likelihood of a voxel to change network assignment across atlases) was high for subcortical regions and low for the sensory, motor and medial prefrontal cortices, and the precuneus. In order to facilitate RSN reproducibility in future studies, we provide a new freely available Consensual Atlas of REsting‐state Networks, based on the most reliable atlases.

The effect of optimistic expectancies on attention bias: Neural and behavioral correlates

“…AI) as the network masks we used slightly overlapped. This is due to the fact that the SN and ECN networks are not identical across the different atlases at the basis of CAREN 33 . Such overlap between SN and ECN masks might further relate to interactions between SN and ECN.…”

“…Such ROI analyses were performed for the SN and ECN using the small volume correction option of SPM12. Bilateral masks of the SN and ECN (obtained from CAREN) 33 were used for ROI analyses. The datasets generated and/or analyzed in the current study are available from the corresponding authors on reasonable request.…”

Topological Data Analysis reveals robust alterations in the whole-brain and frontal lobe functional connectomes in Attention-Deficit/Hyperactivity Disorder

“…In addition, considering that the Rips filtration does not depend on a particular connectivity threshold, but instead explores all the filtration values with a change in the topology of the network, this methodology contributes to provide a more complete picture of the brain network, overcoming one of the main limitations of other approaches. Taken together, these are potentially important advantages compared to other methods applied to brain networks, such as graph theory, which has been shown to be highly dependent on the brain parcellation scheme (Wang et al, 2009a;Chen et al, 2018;Doucet et al, 2019), and on the selection of a connectivity threshold or connectivity cost (van den Heuvel et al, 2008;Fornito et al, 2010;Tomasi and Volkow, 2010;Gracia-Tabuenca et al, 2018;Termenon et al, 2016).…”

Atlas55+: Brain Functional Atlas of Resting-state Networks for Late Adulthood

“…functions as part of the intrinsic system (default-mode [DMN], executive control [ECN], and salience [SAL] networks) and those supporting externally driven, specialized sensory and motor processing as part of the extrinsic system (visual [VIS] and sensorimotor [SMN] networks) (Buckner et al, 2013;De Luca et al, 2006;Doucet et al, 2011;Doucet et al, 2019;Smith et al, 2009). Each of these brain networks relies on established white matter pathways (Greicius et al, 2009;Toosy et al, 2004;van den Heuvel et al, 2009) and has been consistently and robustly defined by their spatiotemporal configuration and functional roles (Damoiseaux et al, 2006;Doucet et al, 2011;Doucet et al, 2019;Elliott et al, 2019;Smith et al, 2009;van den Heuvel and Hulshoff Pol, 2010).…”