A dual-networks architecture of top-down control

Abstract

Complex systems ensure resilience through multiple controllers acting at rapid and slower timescales. The need for efficient information flow through complex systems encourages small-world network structures. On the basis of these principles, a group of regions associated with top-down control was examined. Functional magnetic resonance imaging showed that each region had a specific combination of control signals; resting-state functional connectivity grouped the regions into distinct 'fronto-parietal' and 'cingulo-opercular' components. The fronto-parietal component seems to initiate and adjust control; the cingulo-opercular component provides stable 'set-maintenance' over entire task epochs. Graph analysis showed dense local connections within components and weaker 'long-range' connections between components, suggesting a small-world architecture. The control systems of the brain seem to embody the principles of complex systems, encouraging resilient performance.

Figure 1

Schematic depicting regular, small-world and random graphs. Small-world graphs can be generated by replacing local connections in regular (lattice) graphs with longer-range short-cuts. (a) Sample regular graph (lattice) containing 22 nodes and 52 edges. In regular graphs, each node is only connected to the next n nodes around the ring in a regular pattern. Regular graphs have long Lp and high Cp. (b) Data-derived graph containing 22 nodes [regions of interest (ROIs)] and 52 edges (functional connections) that seems small-world-like. Densely intraconnected clusters (black, yellow and blue) are linked through long-range short cuts (highlighted in red). Small-world networks are ’clumpy’, as reflected by high Cp and much shorter Lp than regular graphs. This type of network organization enables faster information transfer between any pair of nodes. (c) Sample random graph containing 22 nodes and 52 edges. Random graphs have moderately short Lp and low Cp. Functional connectivity diagrams of actual brain regions are neither regular nor random but more small-world like.

Figure 2

Distinct fronto-parietal and cingulo-opercular control networks. (a) The network structure of human control networks is displayed in a two-dimensional graph layout. Black lines indicate strong resting state functional connections between brain regions. The thickness of the lines indicates the relative connection strength (r). A spring-embedding algorithm (Net-Draw) was used to generate the 2D graph layout [17]. This algorithm treats each connection as a spring; thus, brain regions with similar patterns of connections are brought closer together in 2D space. This method arranges the nodes of a graph in ‘connection space’ rather than anatomical space. Regions sharing connections are placed close together, whereas minimally connected regions are spatially distant. For example, the left and right IPS have similar connectivity profiles and are therefore positioned closely adjacent in the network graph. For each region (circle), the central color indicates which network it belongs to (black = cingulo-opercular; blue = cerebellar and yellow = fronto-parietal). The outer color indicates the predominant control signal type of each region (red = set-maintenance; blue = error-related and yellow = start cue-related). At the displayed correlation threshold (r ≥ 0.15), the cingulo-opercular and fronto-parietal networks are not directly connected to each other but each network is connected to the cerebellar error-network through regions that also carry error information (the thalamus, dlPFC and IPL). This architecture suggests that both networks might be communicating error signals (or codes) to and from the cerebellum, in parallel. (b) Distinct cingulo-opercular (black) and fronto-parietal (yellow) control networks, in addition to cerebellar regions (blue circles) are shown on an inflated surface rendering of the human brain [55].

Figure 3

Model of proposed parallel rapid-adaptive and set-maintenance networks for human top-down control. Thin arrows schematize strong functional connections and boxed arrows schematize putative flow of information. The fronto-parietal and cingulo-opercular control networks might be organized in parallel. Both networks would thus interpret cues, implement top-down control and process bottom-up feedback. The fronto-parietal network might maintain task-relevant information in a more readily accessible form to adjust control rapidly. The cingulo-opercular network might stably maintain task sets across entire task epochs [9], perhaps in a less easily accessible, or remote, form. Adapted, with permission, from [9] Proceedings of the National Academy of Sciences. Copyright (2007) National Academy of Sciences, U.S.A.