Large-Scale Gradients in Human Cortical Organization
Authors: Julia M. Huntenburg, Pierre-Louis Bazin, Daniel S. Margulies
Journal: Trends in Cognitive Sciences (2017)

Advances in neuroimaging technologies and analytics have enabled the discovery of gradients in microstructure, connectivity, gene expression, and function in the human cerebral cortex. The notion that functional processing hierarchies are confined to sensorimotor systems is challenged by recent descriptions of global hierarchies, extending throughout transmodal association areas. An innovative line of research has uncovered a cortical hierarchy in the temporal domain that accounts for spatially distributed functional specialization. Recent advances in mapping cortical areas in the human brain provide a basis for investigating the significance of their spatial arrangement. Here we describe a dominant gradient in cortical features that spans between sensorimotor and transmodal areas. We propose that this gradient constitutes a core organizing axis of the human cerebral cortex, and describe an intrinsic coordinate system on its basis. Studying the cortex with respect to these intrinsic dimensions can inform our understanding of how the spectrum of cortical function emerges from structural constraints. Recent advances in mapping cortical areas in the human brain provide a basis for investigating the significance of their spatial arrangement. Here we describe a dominant gradient in cortical features that spans between sensorimotor and transmodal areas. We propose that this gradient constitutes a core organizing axis of the human cerebral cortex, and describe an intrinsic coordinate system on its basis. Studying the cortex with respect to these intrinsic dimensions can inform our understanding of how the spectrum of cortical function emerges from structural constraints. For more than a century, neuroscientists have studied the cerebral cortex by delineating individual cortical areas (see Glossary) and mapping their function [1Vogt C. Vogt O. Allgemeinere ergebnisse unserer hirnforschung.J. Psychol. Neurol. 1919; 25: 279-468Google Scholar]. This agenda has substantially advanced in recent years, as automated parcellation methods improve and data sets of unprecedented size and quality become available [2Amunts K. Zilles K. Architectonic mapping of the human brain beyond Brodmann.Neuron. 2015; 88: 1086-1107Abstract Full Text Full Text PDF PubMed Scopus (230) Google Scholar, 3Glasser M.F. et al.A multi-modal parcellation of human cerebral cortex.Nature. 2016; 536: 171-178Crossref PubMed Scopus (1992) Google Scholar, 4Eickhoff S.B. et al.Topographic organization of the cerebral cortex and brain cartography.Neuroimage. 2017; (Published online February 20, 2017)https://doi.org/10.1016/j.neuroimage.2017.02.018Crossref Scopus (82) Google Scholar]. Nevertheless, our understanding of how the complex structure of the cerebral cortex emerges and gives rise to its elaborate functions remains fragmentary. To complement the description of individual cortical areas, we propose an inquiry into the significance of their spatial arrangement, asking the basic question: Why are cortical areas located where they are? Early formulations of this question date to theories from classical neuroanatomy [1Vogt C. Vogt O. Allgemeinere ergebnisse unserer hirnforschung.J. Psychol. Neurol. 1919; 25: 279-468Google Scholar, 5Brockhaus H. Die cyto-und myeloarchitektonik des cortex claustralis und des claustrum beim menschen.J. Psychol. Neurol. 1940; 49: 249-348Google Scholar, 6Sanides F. Die Architektonik des Menschlichen Stirnhirns. Springer, 1962Crossref Google Scholar, 7Pandya D. et al.Cerebral Cortex: Architecture, Connections, and the Dual Origin Concept. Oxford University Press, 2015Crossref Google Scholar]. They state that the spatial layout of cortical areas is not arbitrary, but a consequence of developmental mechanisms, shaped through evolutionary selection. The location of an area among its neighbors thus provides insight into its microstructural characteristics [6Sanides F. Die Architektonik des Menschlichen Stirnhirns. Springer, 1962Crossref Google Scholar], its connections to other parts of the brain [7Pandya D. et al.Cerebral Cortex: Architecture, Connections, and the Dual Origin Concept. Oxford University Press, 2015Crossref Google Scholar], and eventually its position in global processing hierarchies [8Mesulam M.M. From sensation to cognition.Brain. 1998; 121: 1013-1052Crossref PubMed Scopus (2101) Google Scholar]. Consider, for example, the well-researched visual system of the macaque monkey [9Felleman D.J. Van Essen D.C. Distributed hierarchical processing in the primate cerebral cortex.Cereb. Cortex. 1991; 1: 1-47Crossref PubMed Scopus (5448) Google Scholar, 10Markov N.T. et al.A weighted and directed interareal connectivity matrix for macaque cerebral cortex.Cereb. Cortex. 2014; 24: 17-36Crossref PubMed Scopus (467) Google Scholar]. Along the visual hierarchy, low-level visual features are increasingly abstracted and integrated with information from other systems. Traditionally, areas are ordered based on their degree of microstructural differentiation, and the classification of their connections as feedforward or feedback [11Barbas H. Pattern in the laminar origin of corticocortical connections.J. Comp. Neurol. 1986; 252: 415-422Crossref PubMed Scopus (188) Google Scholar]. The framework we advocate emphasizes that an area's position in the visual processing hierarchy – and thus many of its microstructural and connectional features – is strongly related to its distance from the primary visual area [12Wagstyl K. et al.Cortical thickness gradients in structural hierarchies.Neuroimage. 2015; 111: 241-250Crossref PubMed Scopus (93) Google Scholar, 13Güçlü U. van Gerven M.A.J. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream.J. Neurosci. 2015; 35: 10005-10014Crossref PubMed Scopus (436) Google Scholar]. More generally, we propose that the spatial arrangement of areas along a global gradient between sensorimotor and transmodal regions is a key feature of human cortical organization. A gradient is an axis of variance in cortical features, along which areas fall in a spatially continuous order. Areas that resemble each other with respect to the feature of interest occupy similar positions along the gradient. As we will point out, there is a strong relationship between the similarity of two areas – that is, their relative position along the gradient – and their relative position along the cortical surface. This important role of cortical location prompts a new perspective in which we try to understand the cerebral cortex with respect to its own, intrinsic dimensions. Toward this goal, we will first review converging findings on cortical microstructure, connectivity, and gene expression, which point to a dominant sensorimotor-to-transmodal gradient of cortical organization. Next, we will explain how this gradient can help us understand how the spectrum of cognitive functions arises from cortical structure. Finally, we will propose a gradient-based intrinsic coordinate system of the human cerebral cortex. As will be outlined in the following section, there is now comprehensive evidence for a global gradient in human cortical organization, which spans between primary sensorimotor and transmodal regions, and is reflected in cortical microstructure, connectivity, and gene expression. While these cortical features have previously been used to delineate discrete areas, the concept of an overarching gradient does not discount the existence of such areas, but moves their respective spatial relationships into focus. The theory of cortical gradients is rooted in classical neuroanatomy (e.g., [1Vogt C. Vogt O. Allgemeinere ergebnisse unserer hirnforschung.J. Psychol. Neurol. 1919; 25: 279-468Google Scholar, 5Brockhaus H. Die cyto-und myeloarchitektonik des cortex claustralis und des claustrum beim menschen.J. Psychol. Neurol. 1940; 49: 249-348Google Scholar, 6Sanides F. Die Architektonik des Menschlichen Stirnhirns. Springer, 1962Crossref Google Scholar, 7Pandya D. et al.Cerebral Cortex: Architecture, Connections, and the Dual Origin Concept. Oxford University Press, 2015Crossref Google Scholar]). In these studies, histological sections of postmortem brains are examined for the distribution of cells (cytoarchitecture) or myelinated fibers (myeloarchitecture) in horizontal layers of the cortical sheet. While this labor-intensive approach provides a detailed account of cortical microstructure, the resulting descriptions typically remain qualitative and observer dependent (but see [14Schleicher A. et al.Observer-independent method for microstructural parcellation of cerebral cortex: a quantitative approach to cytoarchitectonics.Neuroimage. 1999; 9: 165-177Crossref PubMed Scopus (250) Google Scholar]). A more recent line of research takes a simplified view of cortical microstructure by reducing it to a single quantity: the number of neurons within a unit of surface area [15Collins C.E. et al.Neuron densities vary across and within cortical areas in primates.Proc. Natl. Acad. Sci. U. S. A. 2010; 107: 15927-15932Crossref PubMed Scopus (230) Google Scholar, 16Cahalane D.J. et al.Systematic, balancing gradients in neuron density and number across the primate isocortex.Front. Neuroanat. 2012; 6: 28Crossref PubMed Scopus (70) Google Scholar, 17Charvet C.J. et al.Systematic, cross-cortex variation in neuron numbers in rodents and primates.Cereb. Cortex. 2015; 25: 147-160Crossref PubMed Scopus (95) Google Scholar, 18Charvet C.J. et al.Gradients in cytoarchitectural landscapes of the isocortex: diprotodont marsupials in comparison to eutherian mammals.J. Comp. Neurol. 2017; 525: 1811-1826Crossref PubMed Scopus (8) Google Scholar]. These studies have demonstrated a rostrocaudal gradient in neuron number in the cortices of a broad range of mammalian species, including several rodents, marsupials, and non-human primates. Neuron numbers are generally high in caudal portions of the cortex, such as the occipital lobe, and gradually decrease toward more rostral regions. This rostrocaudal organization in neuron number aligns with known neurodevelopmental gradients in the mammalian cortex and has been suggested to arise from the temporal sequence of neurogenesis [16Cahalane D.J. et al.Systematic, balancing gradients in neuron density and number across the primate isocortex.Front. Neuroanat. 2012; 6: 28Crossref PubMed Scopus (70) Google Scholar, 19Cahalane D.J. et al.Modeling local and cross-species neuron number variations in the cerebral cortex as arising from a common mechanism.Proc. Natl. Acad. Sci. U. S. A. 2014; 111: 17642-17647Crossref PubMed Scopus (49) Google Scholar, 20Charvet C.J. Finlay B.L. Evo-devo and the primate isocortex: the central organizing role of intrinsic gradients of neurogenesis.Brain Behav. Evol. 2014; 84: 81-92Crossref PubMed Scopus (30) Google Scholar] (Box 1).Box 1Neurodevelopmental GradientsIt has been suggested that the spatial distribution of cortical features in the adult brain is established by neurodevelopmental gradients. The rostrocaudal gradient in neuron number can be related to timing differences in neurogenesis, which begins uniformly across the cortex, but terminates earlier in rostral regions. In the macaque monkey, neurogenesis ends about 20 days earlier in the rostral pole than in the most caudal regions [61Rakic P. Neurogenesis in adult primate neocortex: an evaluation of the evidence.Nat. Rev. Neurosci. 2002; 3: 65-71Crossref PubMed Scopus (381) Google Scholar]. Caudal regions thus undergo a higher number of cell cycles, which accounts for the higher number of neurons in these areas. In rostral regions, more time can in turn be devoted to the growth of large neurons that form many connections [62Elston G.N. Pyramidal cells of the frontal lobe: all the more spinous to think with.J. Neurosci. 2000; 20: RC95Crossref PubMed Google Scholar]. The rostrocaudal gradient therefore signifies a shift in computational capacity, from a high number of processing units in caudal regions to a lower number of highly connected units in rostral regions [16Cahalane D.J. et al.Systematic, balancing gradients in neuron density and number across the primate isocortex.Front. Neuroanat. 2012; 6: 28Crossref PubMed Scopus (70) Google Scholar]. The gradient is more pronounced in larger cortices [17Charvet C.J. et al.Systematic, cross-cortex variation in neuron numbers in rodents and primates.Cereb. Cortex. 2015; 25: 147-160Crossref PubMed Scopus (95) Google Scholar, 20Charvet C.J. Finlay B.L. Evo-devo and the primate isocortex: the central organizing role of intrinsic gradients of neurogenesis.Brain Behav. Evol. 2014; 84: 81-92Crossref PubMed Scopus (30) Google Scholar], which have longer developmental schedules, leading to more pronounced differences in neurogenesis termination across areas [19Cahalane D.J. et al.Modeling local and cross-species neuron number variations in the cerebral cortex as arising from a common mechanism.Proc. Natl. Acad. Sci. U. S. A. 2014; 111: 17642-17647Crossref PubMed Scopus (49) Google Scholar]. In the human cortex a rostrocaudal difference of up to 70 days has been predicted [20Charvet C.J. Finlay B.L. Evo-devo and the primate isocortex: the central organizing role of intrinsic gradients of neurogenesis.Brain Behav. Evol. 2014; 84: 81-92Crossref PubMed Scopus (30) Google Scholar].However, the microstructural gradient in the human cortex appears to not simply be steeper, but to deviate from the rostrocaudal distribution. Cortical development is less understood in humans compared with other mammals and the deviation in the adult cortex could be rooted in spatially more complex developmental gradients. This hypothesis is in line with the observation that gene expression patterns in the human cortex also diverge from the rostrocaudal organization described for other mammals [42Krienen F.M. et al.Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: E469-E478Crossref PubMed Scopus (118) Google Scholar]. For example, transmodal regions of the DMN, although located at distant points of the rostrocaudal axis, have similar gene expression patterns. These regions also show protracted developmental expansion and maturation in humans [63Hill J. et al.Similar patterns of cortical expansion during human development and evolution.Proc. Natl. Acad. Sci. U. S. A. 2010; 107: 13135-13140Crossref PubMed Scopus (379) Google Scholar]. While much remains to be learned about neurodevelopmental gradients in the human cerebral cortex, such research can help to integrate observations across different cortical features and establish an informed developmental basis for the proposed intrinsic coordinate system. It has been suggested that the spatial distribution of cortical features in the adult brain is established by neurodevelopmental gradients. The rostrocaudal gradient in neuron number can be related to timing differences in neurogenesis, which begins uniformly across the cortex, but terminates earlier in rostral regions. In the macaque monkey, neurogenesis ends about 20 days earlier in the rostral pole than in the most caudal regions [61Rakic P. Neurogenesis in adult primate neocortex: an evaluation of the evidence.Nat. Rev. Neurosci. 2002; 3: 65-71Crossref PubMed Scopus (381) Google Scholar]. Caudal regions thus undergo a higher number of cell cycles, which accounts for the higher number of neurons in these areas. In rostral regions, more time can in turn be devoted to the growth of large neurons that form many connections [62Elston G.N. Pyramidal cells of the frontal lobe: all the more spinous to think with.J. Neurosci. 2000; 20: RC95Crossref PubMed Google Scholar]. The rostrocaudal gradient therefore signifies a shift in computational capacity, from a high number of processing units in caudal regions to a lower number of highly connected units in rostral regions [16Cahalane D.J. et al.Systematic, balancing gradients in neuron density and number across the primate isocortex.Front. Neuroanat. 2012; 6: 28Crossref PubMed Scopus (70) Google Scholar]. The gradient is more pronounced in larger cortices [17Charvet C.J. et al.Systematic, cross-cortex variation in neuron numbers in rodents and primates.Cereb. Cortex. 2015; 25: 147-160Crossref PubMed Scopus (95) Google Scholar, 20Charvet C.J. Finlay B.L. Evo-devo and the primate isocortex: the central organizing role of intrinsic gradients of neurogenesis.Brain Behav. Evol. 2014; 84: 81-92Crossref PubMed Scopus (30) Google Scholar], which have longer developmental schedules, leading to more pronounced differences in neurogenesis termination across areas [19Cahalane D.J. et al.Modeling local and cross-species neuron number variations in the cerebral cortex as arising from a common mechanism.Proc. Natl. Acad. Sci. U. S. A. 2014; 111: 17642-17647Crossref PubMed Scopus (49) Google Scholar]. In the human cortex a rostrocaudal difference of up to 70 days has been predicted [20Charvet C.J. Finlay B.L. Evo-devo and the primate isocortex: the central organizing role of intrinsic gradients of neurogenesis.Brain Behav. Evol. 2014; 84: 81-92Crossref PubMed Scopus (30) Google Scholar]. However, the microstructural gradient in the human cortex appears to not simply be steeper, but to deviate from the rostrocaudal distribution. Cortical development is less understood in humans compared with other mammals and the deviation in the adult cortex could be rooted in spatially more complex developmental gradients. This hypothesis is in line with the observation that gene expression patterns in the human cortex also diverge from the rostrocaudal organization described for other mammals [42Krienen F.M. et al.Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: E469-E478Crossref PubMed Scopus (118) Google Scholar]. For example, transmodal regions of the DMN, although located at distant points of the rostrocaudal axis, have similar gene expression patterns. These regions also show protracted developmental expansion and maturation in humans [63Hill J. et al.Similar patterns of cortical expansion during human development and evolution.Proc. Natl. Acad. Sci. U. S. A. 2010; 107: 13135-13140Crossref PubMed Scopus (379) Google Scholar]. While much remains to be learned about neurodevelopmental gradients in the human cerebral cortex, such research can help to integrate observations across different cortical features and establish an informed developmental basis for the proposed intrinsic coordinate system. In some areas neuron numbers deviate from what would be anticipated given their location along the rostrocaudal gradient. For example, elevated neuron numbers have been reported in rostrolateral regions mapping to putative primary somatosensory cortex in multiple species [15Collins C.E. et al.Neuron densities vary across and within cortical areas in primates.Proc. Natl. Acad. Sci. U. S. A. 2010; 107: 15927-15932Crossref PubMed Scopus (230) Google Scholar, 16Cahalane D.J. et al.Systematic, balancing gradients in neuron density and number across the primate isocortex.Front. Neuroanat. 2012; 6: 28Crossref PubMed Scopus (70) Google Scholar, 18Charvet C.J. et al.Gradients in cytoarchitectural landscapes of the isocortex: diprotodont marsupials in comparison to eutherian mammals.J. Comp. Neurol. 2017; 525: 1811-1826Crossref PubMed Scopus (8) Google Scholar]. Such observations raise the question of whether regular spatial patterns beyond the rostrocaudal gradient can be identified in cortical microstructure. In particular when considering the human cortex, a simple rostrocaudal gradient appears insufficient to explain its microstructural organization. There is little quantitative data on the distribution of neuron numbers in the human cortex. However, recent advances in high-resolution and quantitative MRI [21Weiskopf N. et al.Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology.Curr. Opin. Neurol. 2015; 28: 313-322Crossref PubMed Scopus (117) Google Scholar, 22Marques J.P. et al.Studying cyto and myeloarchitecture of the human cortex at ultra-high field with quantitative imaging: R1, R2(*) and magnetic susceptibility.Neuroimage. 2017; 147: 152-163Crossref PubMed Scopus (56) Google Scholar] together with improved processing tools (e.g., [23Goebel R. BrainVoyager – past, present, future.Neuroimage. 2012; 62: 748-756Crossref PubMed Scopus (101) Google Scholar, 24Bazin P.L. et al.A computational framework for ultra-high resolution cortical segmentation at 7Tesla.Neuroimage. 2014; 93 Pt 2: 201-209Crossref PubMed Scopus (114) Google Scholar]) facilitate noninvasive assessment of microstructural features in the human brain. MRI only provides indirect measures of microstructural components, and its spatial resolution is still low compared with histological methods. However, MRI does not require slicing of the brain tissue and thus is ideally suited to capture global gradients in three-dimensional space. MRI studies of the human cerebral cortex have provided evidence for a microstructural gradient that is anchored in sensorimotor regions and radiates toward higher-order areas in the parietal, temporal, and prefrontal cortex. For example, MRI measures that are sensitive to intracortical myelin have revealed a pattern of high myelin content in primary sensorimotor regions, which systematically decreases toward transmodal areas in parietal, temporal, and particularly prefrontal cortex [3Glasser M.F. et al.A multi-modal parcellation of human cerebral cortex.Nature. 2016; 536: 171-178Crossref PubMed Scopus (1992) Google Scholar, 25Huntenburg J.M. et al.A systematic relationship between functional connectivity and intracortical myelin in the human cerebral cortex.Cereb. Cortex. 2017; 27: 981-997Crossref PubMed Scopus (110) Google Scholar, 26Tardif C.L. et al.Multi-contrast multi-scale surface registration for improved alignment of cortical areas.Neuroimage. 2015; 111: 107-122Crossref PubMed Scopus (52) Google Scholar, 27Whitaker K.J. et al.Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 9105-9110Crossref PubMed Scopus (241) Google Scholar, 28Burt J.B. et al.Hierarchy of transcriptomic specialization across human cortex captured by myelin map topography.bioRxiv. 2017; (Published online October 7, 2017)https://doi.org/10.1101/199703Crossref Google Scholar] (Figure 1A). A similar spatial distribution has been described for MRI-based measures of cortical thickness [12Wagstyl K. et al.Cortical thickness gradients in structural hierarchies.Neuroimage. 2015; 111: 241-250Crossref PubMed Scopus (93) Google Scholar] and myelinated thickness [29Rowley C.D. et al.Assessing intracortical myelin in the living human brain using myelinated cortical thickness.Front. Neurosci. 2015; 9: 396Crossref PubMed Scopus (37) Google Scholar]. Cortical thickness coarsely tracks changes in cytoarchitecture [30Beul S.F. et al.A predictive model of the cat cortical connectome based on cytoarchitecture and distance.Brain Struct. Funct. 2014; 220: 3167-3184Crossref PubMed Scopus (63) Google Scholar] and myelin content [25Huntenburg J.M. et al.A systematic relationship between functional connectivity and intracortical myelin in the human cerebral cortex.Cereb. Cortex. 2017; 27: 981-997Crossref PubMed Scopus (110) Google Scholar], and can be viewed as a pragmatic surrogate for cortical microstructure [12Wagstyl K. et al.Cortical thickness gradients in structural hierarchies.Neuroimage. 2015; 111: 241-250Crossref PubMed Scopus (93) Google Scholar]. Thus, a rostrocaudal gradient explains much of the spatial variation of cortical microstructure in the mammalian cortex. However, microstructural features in the human cortex depart from the simple rostrocaudal distribution and reveal a spatially more complex sensorimotor-to-transmodal gradient. While cortical microstructure is mostly used to describe local properties of individual areas, studies investigating cortical connections focus on the relationship between areas. 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 [31Sepulcre J. et al.Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain.J. Neurosci. 2012; 32: 10649-10661Crossref PubMed Scopus (181) Google Scholar, 32Taylor P. et al.The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions.Sci. Rep. 2015; 518112Crossref PubMed Scopus (53) Google Scholar, 33Langs G. et al.Decoupling function and anatomy in atlases of functional connectivity patterns: language mapping in tumor patients.Neuroimage. 2014; 103: 462-475Crossref PubMed Scopus (24) Google Scholar, 34Langs G. et al.Identifying shared brain networks in individuals by decoupling functional and anatomical variability.Cereb. Cortex. 2015; 26: 4004-4014Crossref PubMed Scopus (37) Google Scholar, 35Margulies D.S. et al.Situating the default-mode network in a principal gradient of macroscale cortical organization.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 12574-12579Crossref PubMed Scopus (673) Google Scholar, 36Atasoy S. et al.Human brain networks function in connectome-specific harmonic waves.Nat. Commun. 2016; 710340Crossref PubMed Scopus (165) Google Scholar, 37Haak K.V. et al.Connectopic mapping with resting-state fMRI.Neuroimage. 2017; (Published online June 27, 2017)https://doi.org/10.1016/j.neuroimage.2017.06.075Crossref PubMed Scopus (94) Google Scholar]. 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. One method of simplification is to select a seed region and quantify its connectivity to every other cortical location. When selecting seeds in the different primary sensorimotor areas, gradients of decreasing connectivity strength from each of these seeds converge in multimodal integration areas, such as the anterior cingulate cortex and the occipitotemporal junction, and finally reach transmodal regions of the default mode network (DMN) in the medial prefrontal, posterior cingulate, and temporal cortex [31Sepulcre J. et al.Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain.J. Neurosci. 2012; 32: 10649-10661Crossref PubMed Scopus (181) Google Scholar, 32Taylor P. et al.The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions.Sci. Rep. 2015; 518112Crossref PubMed Scopus (53) Google Scholar]. In a more data-driven approach, connectivity gradients can be obtained by identifying the main axes of variance in the connectivity matrix [33Langs G. et al.Decoupling function and anatomy in atlases of functional connectivity patterns: language mapping in tumor patients.Neuroimage. 2014; 103: 462-475Crossref PubMed Scopus (24) Google Scholar, 34Langs G. et al.Identifying shared brain networks in individuals by decoupling functional and anatomical variability.Cereb. Cortex. 2015; 26: 4004-4014Crossref PubMed Scopus (37) Google Scholar, 35Margulies D.S. et al.Situating the default-mode network in a principal gradient of macroscale cortical organization.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 12574-12579Crossref PubMed Scopus (673) Google Scholar, 36Atasoy S. et al.Human brain networks function in connectome-specific harmonic waves.Nat. Commun. 2016; 710340Crossref PubMed Scopus (165) Google Scholar, 37Haak K.V. et al.Connectopic mapping with resting-state fMRI.Neuroimage. 2017; (Published online June 27, 2017)https://doi.org/10.1016/j.neuroimage.2017.06.075Crossref PubMed Scopus (94) Google Scholar] (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. 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 [35Margulies D.S. et al.Situating the default-mode network in a principal gradient of macroscale cortical organization.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 12574-12579Crossref PubMed Scopus (673) Google Scholar] (Figure 1B). A corresponding gradient was found in the macaque monkey cortex based on anatomical connectivity data from tract-tracing studies [35Margulies D.S. et al.Situating the default-mode network in a principal gradient of macroscale cortical organization.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 12574-12579Crossref PubMed Scopus (673) Google Scholar].Box 2Extracting Gradients from High-Dimensional Cortical DataIn the analysis of some cortical features more than one value is assigned to each cortical location. Examples are connectivity data, functional coactivation, gene or receptor expression, depth-resolved microstructure, and in particular multimodal data sets combining several of these features [3Glasser M.F. et al.A multi-modal parcellation of human cerebral cortex.Nature. 2016; 536: 171-178Crossref PubMed Scopus (1992) Google Scholar, 4Eickhoff S.B. et al.Topographic organization of the cerebral cortex and brain cartography.Neuroimage. 2017; (Published online February 20, 2017)https://doi.org/10.1016/j.neuroimage.2017.02.018Crossref Scopus (82) Google Scholar]. To extract intelligible information f