Resting State Networks' Corticotopy: The Dual Intertwined Rings Architecture

Abstract

How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called “the dual intertwined rings architecture”) that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or “corticotopy”). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs. We estimated: (1) the RSN functional roles by using a projection of the results on task based networks (TBNs) as referenced in large databases of fMRI activation studies; and (2) relationship of the RSNs with the Brodmann Areas. In both classifications, the 32 RSNs are organized into a remarkable architecture of two intertwined rings per hemisphere and so four rings linked by homotopic connections. The first ring forms a continuous ensemble and includes visual, somatic, and auditory cortices, with interspersed bimodal cortices (auditory-visual, visual-somatic and auditory-somatic, abbreviated as VSA ring). The second ring integrates distant parietal, temporal and frontal regions (PTF ring) through a network of association fiber tracts which closes the ring anatomically and ensures a functional continuity within the ring. The PTF ring relates association cortices specialized in attention, language and working memory, to the networks involved in motivation and biological regulation and rhythms. This “dual intertwined architecture” suggests a dual integrative process: the VSA ring performs fast real-time multimodal integration of sensorimotor information whereas the PTF ring performs multi-temporal integration (i.e., relates past, present, and future representations at different temporal scales).

Are schizophrenia, autistic, and obsessive spectrum disorders dissociable on the basis of neuroimaging morphological findings?: A voxel‐based meta‐analysis

“…Of note, these two distinct clusters, and in particular the first one, partially resemble the “dual intertwined rings architecture” described by Mesmoudi et al [].…”

Semantically defined subdomains of functional neuroimaging literature and their corresponding brain regions

“…In recent years, there have been many meta‐analyses, mega‐analyses (analyses of pooled data across many studies), and other large‐scale analyses of data within neuroimaging. In general, the aims of such analyses are to (a) test or refute findings and hypotheses (Wager, Lindquist, & Kaplan, ), (b) build a consensus around particular models, hypotheses, or theories (Salimi‐Khorshidi et al, ), (c) estimate consistency of findings (Wager, Lindquist, Nichols, Kober, & Van Snellenberg, ), (d) help define related brain regions and networks (Toro, Fox, & Paus, ; Mesmoudi et al, ), (e) interpret functional maps (Laird et al, ), or (f) segment the brain in new ways with resting‐state f MRI measurements (Yeo et al, ; Power et al, ) or using massive multimodal data (Glasser et al, ).…”

Principal Component Regression Predicts Functional Responses across Individuals

“…Local models outperform regional or more global models; however, we conjecture that randomized or multiscale models that average the predictions across spatial models could perform better. The regions that show high predictability are mostly those that show high average activation across subjects and they tend to coincide with the visualsensorimotor-auditory network described in [12], with the addition of the attentional network. Most importantly, the fact that predictability is correlated with average activation means that traditional mass-univariate random effects models used in neuroimaging studies are suboptimal, as they handle the random effects as an error term, while they actually represent more complex structures (mismatch in the coregistration, physiological variability) and may be indicative of relevant anatomical and functional differences.…”