Situating the default-mode network along a principal gradient of macroscale cortical organization

Abstract

Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/ motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.key assumption in neuroscience is that the topographical structure of the cerebral cortex provides an organizing principle that constrains its cognitive processes. Recent advances in the field of human connectomics have revealed multiple largescale networks (1-3), each characterized by distinct functional profiles (4). Some are related to basic primary functions, such as movement or perceiving sounds and images; some serve welldocumented, domain-general functions, such as attention or cognitive control (5-8); and some have functional characteristics that remain less well-understood, such as the default-mode network (DMN) (9, 10). Although the topography of these distinct distributed networks has been described using multiple methods (1-3), the reason for their particular spatial relationship and how this constrains their function remain unclear.Advances in mapping local processing streams have revealed spatial gradients that support increasingly abstract levels of representation, often extending along adjacent cortical regions in a stepwise manner (11). In the visual domain, for example, the ventral occipitotemporal object stream transforms simple visual features, coded by neurons in primary visual cortex, into more complex visual descriptions of objects in anterior inferior temporal cortical regions and ultimately, contributes to multimodal semantic representations in the middle temporal cortex and the most anterior temporal cortex that capture the meaning of what we see, hear, and do (12)(13)(14)(15). Similarly, in the prefrontal cortex, a rostral-caudal gradient has been proposed, whereby goals become increasingly abstract in anterior areas more distant from motor cortex...

Large-Scale Gradients in Human Cortical Organization

“…Each of these axes represents a gradient, along which cortical locations are ordered according to their similarity in connections to the rest of the cortex. In line with the gradients emerging from primary sensorimotor seeds, the main axis of variance in human resting-state functional connectivity data is captured by a principal gradient that spans from primary sensorimotor areas to the transmodal peaks of the DMN [35] (Figure 1B). A corresponding gradient was found in the macaque monkey cortex based on anatomical connectivity data from tract-tracing studies [35].…”

“…In line with the gradients emerging from primary sensorimotor seeds, the main axis of variance in human resting-state functional connectivity data is captured by a principal gradient that spans from primary sensorimotor areas to the transmodal peaks of the DMN [35] (Figure 1B). A corresponding gradient was found in the macaque monkey cortex based on anatomical connectivity data from tract-tracing studies [35]. The main variance in functional connectivity patterns spans between primary sensorimotor areas and transmodal regions of the DMN.…”

“…Connectivity analyses have often taken the form of dividing the cortex into discrete networks of strongly interconnected areas, but recent studies have demonstrated continuous spatial patterns of connectivity in the human cerebral cortex [31][32][33][34][35][36][37]. These studies simplify the complex connectivity matrix -representing how each cortical area is connected to every other area -to a small set of connectivity gradients.…”

“…In a more data-driven approach, connectivity gradients can be obtained by identifying the main axes of variance in the connectivity matrix [33][34][35][36][37] (Box 2). Each of these axes represents a gradient, along which cortical locations are ordered according to their similarity in connections to the rest of the cortex.…”

Studying neuroanatomy using MRI

“…The diverse modalities represented by these maps emphasizes the fact that structural neuroimaging provides just one of many ways of modelling the brain, but also reinforces the fundamental importance of anatomy as the common spatial framework within which all other phenotypic properties of the brain are embedded 132 . This broad point is well illustrated by several relatively recent studies that use structural neuroimaging data to calculate physical distances between different brain regions, and then demonstrate that these distance-based representations of the brain can predict spatial patterns of functional connectivity, metabolism, gene expression and cognitive specialization within the human brain 133,134 . Thus, the in vivo measures of brain organization provided by structural neuroimaging are not only highly informative and discriminative phenotypes in their own right, but describe the basic anatomical scaffold within which our brains evolve, develop and operate.…”