Decoupling of brain function from structure reveals regional behavioral specialization in humans

The brain is an assembly of neuronal populations interconnected by structural pathways. Brain activity is expressed on and constrained by this substrate. Therefore, statistical dependencies between functional signals in directly connected areas can be expected higher. However, the degree to which brain function is bound by the underlying wiring diagram remains a complex question that has been only partially answered. Here, we introduce the structural-decoupling index to quantify the coupling strength between structure and function, and we reveal a macroscale gradient from brain regions more strongly coupled, to regions more strongly decoupled, than expected by realistic surrogate data. This gradient spans behavioral domains from lower-level sensory function to high-level cognitive ones and shows for the first time that the strength of structure-function coupling is spatially varying in line with evidence derived from other modalities, such as functional connectivity, gene expression, microstructural properties and temporal hierarchy. The extent to which brain structure and function are coupled remains a complex question. Here, the authors show that coupling strength between structural connectivity and functional activity can be quantified and reveals a cortical gradient spanning from lower-level sensory areas to high-level cognitive ones.

[1]  Somwrita Sarkar,et al.  Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment , 2016, NeuroImage.

[2]  Dimitri Van De Ville,et al.  A Graph Signal Processing View on Functional Brain Imaging , 2017, ArXiv.

[3]  Dimitri Van De Ville,et al.  Determining significant connectivity by 4D spatiotemporal wavelet packet resampling of functional neuroimaging data , 2006, NeuroImage.

[4]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[5]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[6]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

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

[8]  Elizabeth Jefferies,et al.  Situating the default-mode network along a principal gradient of macroscale cortical organization , 2016, Proceedings of the National Academy of Sciences.

[9]  Alejandro Ribeiro,et al.  Functional Alignment with Anatomical Networks is Associated with Cognitive Flexibility , 2016, Nature Human Behaviour.

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

[11]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[12]  Gustavo Deco,et al.  How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model , 2012, Front. Comput. Neurosci..

[13]  Hervé Abdi,et al.  Differences in Human Cortical Gene Expression Match the Temporal Properties of Large-Scale Functional Networks , 2014, PloS one.

[14]  Dimitri Van De Ville,et al.  A Spectral Method for Generating Surrogate Graph Signals , 2016, IEEE Signal Processing Letters.

[15]  H. Kennedy,et al.  A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex , 2015, Neuron.

[16]  Leonardo L. Gollo,et al.  Metastable brain waves , 2018, Nature Communications.

[17]  Michael Breakspear,et al.  Spatiotemporal wavelet resampling for functional neuroimaging data , 2004, Human brain mapping.

[18]  Jean M. Vettel,et al.  Controllability of structural brain networks , 2014, Nature Communications.

[19]  Viktor K. Jirsa,et al.  The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging , 2013, Brain Connect..

[20]  Karl J. Friston,et al.  Tractography-based priors for dynamic causal models , 2009, NeuroImage.

[21]  Olaf Sporns,et al.  Graph theory methods: applications in brain networks , 2018, Dialogues in clinical neuroscience.

[22]  O. Sporns,et al.  Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.

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

[24]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

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

[26]  Michael Breakspear,et al.  Large-scale brain modes reorganize between infant sleep states and carry prognostic information for preterms , 2019, Nature Communications.

[27]  Julia M. Huntenburg,et al.  Large-Scale Gradients in Human Cortical Organization , 2018, Trends in Cognitive Sciences.

[28]  M. P. van den Heuvel,et al.  Linking contemporary high resolution magnetic resonance imaging to the von economo legacy: A study on the comparison of MRI cortical thickness and histological measurements of cortical structure , 2015, Human brain mapping.

[29]  Viktor K. Jirsa,et al.  Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire , 2008, PLoS Comput. Biol..

[30]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[31]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[32]  Michael Breakspear,et al.  Graph analysis of the human connectome: Promise, progress, and pitfalls , 2013, NeuroImage.

[33]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[34]  Jonathan W. Pillow,et al.  Discovering Event Structure in Continuous Narrative Perception and Memory , 2016, Neuron.

[35]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[36]  Gustavo Deco,et al.  Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD , 2017, bioRxiv.

[37]  D. Bassett,et al.  Spatial Embedding Imposes Constraints on Neuronal Network Architectures , 2018, Trends in Cognitive Sciences.

[38]  D. Long.  Networks of the Brain , 2011 .

[39]  Julia M. Huntenburg,et al.  A Systematic Relationship Between Functional Connectivity and Intracortical Myelin in the Human Cerebral Cortex , 2017, Cerebral cortex.

[40]  Fernando Calamante,et al.  The contribution of geometry to the human connectome , 2016, NeuroImage.

[41]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

[42]  Allan R. Jones,et al.  An anatomically comprehensive atlas of the adult human brain transcriptome , 2012, Nature.

[43]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

[44]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.