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

Significance We describe an overarching organization of large-scale connectivity that situates the default-mode network at the opposite end of a spectrum from primary sensory and motor regions. This topography, based on the differentiation of connectivity patterns, is also embedded in the spatial distance along the cortical surface between these respective systems. In addition, this connectivity gradient accounts for the respective positions of canonical networks and captures a functional spectrum from perception and action to more abstract cognitive functions. These results suggest that the default-mode network consists of regions at the top of a representational hierarchy that describe the current cognitive landscape in the most abstract terms. 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.

[1]  Keith A. Johnson,et al.  Stepwise Connectivity of the Modal Cortex Reveals the Multimodal Organization of the Human Brain , 2012, The Journal of Neuroscience.

[2]  M. Goodale,et al.  Separate visual pathways for perception and action , 1992, Trends in Neurosciences.

[3]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.

[4]  G. J. Romanes,et al.  The Neocortex of Macaca mulatta , 1948 .

[5]  William W. Graves,et al.  Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. , 2009, Cerebral cortex.

[6]  Karl J. Friston,et al.  A Hierarchy of Time-Scales and the Brain , 2008, PLoS Comput. Biol..

[7]  Daniel J Mitchell,et al.  Recruitment of the default mode network during a demanding act of executive control , 2015, eLife.

[8]  M. Petrides,et al.  Lateral and Dorsomedial Prefrontal Cortex and the Control of Cognition , 2015 .

[9]  Peter Stiers,et al.  Mapping the hierarchical layout of the structural network of the macaque prefrontal cortex. , 2014, Cerebral cortex.

[10]  Scott T. Grafton,et al.  Wandering Minds: The Default Network and Stimulus-Independent Thought , 2007, Science.

[11]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[12]  Elizabeth Jefferies,et al.  Both the Middle Temporal Gyrus and the Ventral Anterior Temporal Area Are Crucial for Multimodal Semantic Processing: Distortion-corrected fMRI Evidence for a Double Gradient of Information Convergence in the Temporal Lobes , 2012, Journal of Cognitive Neuroscience.

[13]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[14]  Joseph S. B. Mitchell,et al.  The Discrete Geodesic Problem , 1987, SIAM J. Comput..

[15]  O. Sporns,et al.  Network hubs in the human brain , 2013, Trends in Cognitive Sciences.

[16]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[17]  Joseph O'Rourke,et al.  Computational geometry column 35 , 1999, SIGA.

[18]  Koen V. Haak,et al.  Connectopic mapping with resting-state fMRI , 2016, NeuroImage.

[19]  Evan M. Gordon,et al.  Individual Variability of the System‐Level Organization of the Human Brain , 2015, Cerebral cortex.

[20]  Timothy E. J. Behrens,et al.  The topographic connectome , 2013, Current Opinion in Neurobiology.

[21]  M. Raichle.  The brain's default mode network. , 2015, Annual review of neuroscience.

[22]  P. Fox,et al.  Mapping context and content: the BrainMap model , 2002, Nature Reviews Neuroscience.

[23]  Polina Golland,et al.  Functional Geometry Alignment and Localization of Brain Areas , 2010, NIPS.

[24]  J. Kaas.  The organization of neocortex in mammals: implications for theories of brain function. , 1987, Annual review of psychology.

[25]  J. Duncan.  The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour , 2010, Trends in Cognitive Sciences.

[26]  Wen-Ming Luh,et al.  Goal-Congruent Default Network Activity Facilitates Cognitive Control , 2014, The Journal of Neuroscience.

[27]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[28]  Steve Majerus,et al.  Neural Correlates of Ongoing Conscious Experience: Both Task-Unrelatedness and Stimulus-Independence Are Related to Default Network Activity , 2011, PloS one.

[29]  Timothy E. J. Behrens,et al.  Organizing conceptual knowledge in humans with a gridlike code , 2016, Science.

[30]  K. Christoff,et al.  Experience sampling during fMRI reveals default network and executive system contributions to mind wandering , 2009, Proceedings of the National Academy of Sciences.

[31]  Simon B Eickhoff,et al.  Meta-analysis in human neuroimaging: computational modeling of large-scale databases. , 2014, Annual review of neuroscience.

[32]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[33]  Leslie G. Ungerleider,et al.  Contribution of striate inputs to the visuospatial functions of parieto-preoccipital cortex in monkeys , 1982, Behavioural Brain Research.

[34]  Robin W. Wilkins,et al.  Creativity and the default network: A functional connectivity analysis of the creative brain at rest , 2014, Neuropsychologia.

[35]  Timothy O. Laumann,et al.  Informatics and Data Mining Tools and Strategies for the Human Connectome Project , 2011, Front. Neuroinform..

[36]  P. Haggard.  Human volition: towards a neuroscience of will , 2008, Nature Reviews Neuroscience.

[37]  Nils Kolling,et al.  A neural mechanism underlying failure of optimal choice with multiple alternatives , 2014, Nature Neuroscience.

[38]  Anthony Randal McIntosh,et al.  Hundreds of brain maps in one atlas: Registering coordinate-independent primate neuro-anatomical data to a standard brain , 2012, NeuroImage.

[39]  C. Frith,et al.  Meeting of minds: the medial frontal cortex and social cognition , 2006, Nature Reviews Neuroscience.

[40]  Richard L. Magin,et al.  The intrinsic geometry of the human brain connectome , 2015, Brain Informatics.

[41]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[42]  Ann B. Lee,et al.  Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[44]  M. Mesulam,et al.  From sensation to cognition. , 1998, Brain : a journal of neurology.

[45]  Karl J. Friston.  Life as we know it , 2013, Journal of The Royal Society Interface.

[46]  Kristina M. Visscher,et al.  The neural bases of momentary lapses in attention , 2006, Nature Neuroscience.

[47]  R. Nathan Spreng,et al.  Patterns of Brain Activity Supporting Autobiographical Memory, Prospection, and Theory of Mind, and Their Relationship to the Default Mode Network , 2010, Journal of Cognitive Neuroscience.

[48]  Mark D'Esposito,et al.  The hierarchical organization of the lateral prefrontal cortex , 2016, eLife.

[49]  Jon H Kaas,et al.  Topographic Maps are Fundamental to Sensory Processing , 1997, Brain Research Bulletin.

[50]  Tom Schoenemann.  From Monkey Brain to Human Brain: A Fyssen Foundation Symposium , 2006 .

[51]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[52]  Polina Golland,et al.  Learning an Atlas of a Cognitive Process in Its Functional Geometry , 2011, IPMI.

[53]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[54]  Michael W. Cole,et al.  Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence , 2012, The Journal of Neuroscience.

[55]  Francisco Aboitiz,et al.  Monkey Brain, Human Brain , 2017 .

[56]  M. D’Esposito,et al.  Is the rostro-caudal axis of the frontal lobe hierarchical? , 2009, Nature Reviews Neuroscience.

[57]  M. Petrides.  Lateral prefrontal cortex: architectonic and functional organization , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[58]  R. Buckner,et al.  Parcellating Cortical Functional Networks in Individuals , 2015, Nature Neuroscience.

[59]  Haakon G. Engen,et al.  Shaped by the Past: The Default Mode Network Supports Cognition that Is Independent of Immediate Perceptual Input , 2015, PloS one.

[60]  D. Schacter,et al.  The cognitive neuroscience of constructive memory: remembering the past and imagining the future , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[61]  Timothy S. Coalson,et al.  Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. , 2012, Cerebral cortex.

[62]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[63]  David C. Van Essen,et al.  Surface-based approaches to spatial localization and registration in primate cerebral cortex , 2004, NeuroImage.

[64]  Hao-Ting Wang,et al.  Representing Representation: Integration between the Temporal Lobe and the Posterior Cingulate Influences the Content and Form of Spontaneous Thought , 2016, PloS one.

[65]  Ann B. Lee,et al.  Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[66]  Randy L. Buckner,et al.  The evolution of distributed association networks in the human brain , 2013, Trends in Cognitive Sciences.

[67]  David Badre,et al.  Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes , 2008, Trends in Cognitive Sciences.

[68]  E. Jefferies.  The neural basis of semantic cognition: Converging evidence from neuropsychology, neuroimaging and TMS , 2013, Cortex.

[69]  Selen Atasoy,et al.  Human brain networks function in connectome-specific harmonic waves , 2016, Nature Communications.

[70]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[71]  John W. Harwell,et al.  Similar patterns of cortical expansion during human development and evolution , 2010, Proceedings of the National Academy of Sciences.

[72]  S. Debener,et al.  Default-mode brain dysfunction in mental disorders: A systematic review , 2009, Neuroscience & Biobehavioral Reviews.

[73]  Angela R. Laird,et al.  Definition and characterization of an extended social-affective default network , 2014, Brain Structure and Function.

[74]  Daniel L. Schacter,et al.  Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition , 2010, NeuroImage.

[75]  E. Goldberg,et al.  Gradiental approach to neocortical functional organization. , 1989, Journal of clinical and experimental neuropsychology.

[76]  Markus Diesmann,et al.  CoCoMac 2.0 and the future of tract-tracing databases , 2012, Front. Neuroinform..

[77]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[78]  Polina Golland,et al.  Detecting stable distributed patterns of brain activation using Gini contrast , 2011, NeuroImage.

[79]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[80]  T. Rogers,et al.  Where do you know what you know? The representation of semantic knowledge in the human brain , 2007, Nature Reviews Neuroscience.

[81]  E. Koechlin,et al.  The Architecture of Cognitive Control in the Human Prefrontal Cortex , 2003, Science.

[82]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[83]  B. Sahakian,et al.  Default Mode Dynamics for Global Functional Integration , 2015, The Journal of Neuroscience.

[84]  Stanislas Dehaene,et al.  From monkey brain to human brain : a Fyssen Foundation symposium , 2005 .

[85]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[86]  Elizabeth Jefferies,et al.  Down but not out in posterior cingulate cortex: Deactivation yet functional coupling with prefrontal cortex during demanding semantic cognition , 2016, NeuroImage.

[87]  M. Corbetta,et al.  Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex , 1997, Journal of Cognitive Neuroscience.

[88]  Polina Golland,et al.  Localization of Language Areas in Brain Tumor Patients by Functional Geometry Alignment , 2010 .

[89]  Jessica A. Turner,et al.  Behavioral Interpretations of Intrinsic Connectivity Networks , 2011, Journal of Cognitive Neuroscience.

[90]  M. Young,et al.  Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[91]  Polina Golland,et al.  Decoupling function and anatomy in atlases of functional connectivity patterns: Language mapping in tumor patients , 2014, NeuroImage.

[92]  Satrajit S. Ghosh,et al.  Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion , 2015, MICCAI.

[93]  D. V. van Essen,et al.  Surface-based approaches to spatial localization and registration in primate cerebral cortex. , 2004, NeuroImage.

[94]  L. Fellows,et al.  Annals of the New York Academy of Sciences Orbitofrontal Contributions to Value-based Decision Making: Evidence from Humans with Frontal Lobe Damage Is Economic Value Represented in the Brain? , 2022 .

[95]  K. Zilles,et al.  Differentiated parietal connectivity of frontal regions for “what” and “where” memory , 2012, Brain Structure and Function.