A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales

Many studies have identified the role of localized and distributed cognitive functionality by mapping either local task-related activity or distributed functional connectivity (FC). However, few studies have directly explored the relationship between a brain region’s localized task activity and its distributed task FC. Here we systematically evaluated the differential contributions of task-related activity and FC changes to identify a relationship between localized and distributed processes across the cortical hierarchy. We found that across multiple tasks, the magnitude of regional task-evoked activity was high in unimodal areas, but low in transmodal areas. In contrast, we found that task-state FC was significantly reduced in unimodal areas relative to transmodal areas. This revealed a strong negative relationship between localized task activity and distributed FC across cortical regions that was associated with the previously reported principal gradient of macroscale organization. Moreover, this dissociation corresponded to hierarchical cortical differences in the intrinsic timescale estimated from resting-state fMRI and region myelin content estimated from structural MRI. Together, our results contribute to a growing literature illustrating the differential contributions of a hierarchical cortical gradient representing localized and distributed cognitive processes. Highlights Task activations and functional connectivity changes are negatively correlated across cortex Task activation and connectivity dissociations reflect differences in localized and distributed processes in cortex Differences in localized and distributed processes are associated with differences in intrinsic timescale organization Differences in localized and distributed processes are associated with differences in cortical myelin content Cortical heterogeneity in localized and distributed processes revealed by activity flow mapping prediction error

[1]  B T Thomas Yeo,et al.  Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[2]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[3]  A. Bernacchia,et al.  Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography , 2018, Nature Neuroscience.

[4]  M. Cohen,et al.  Measuring and interpreting neuronal correlations , 2011, Nature Neuroscience.

[5]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[6]  Mariel G Kozberg,et al.  Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons , 2016, Proceedings of the National Academy of Sciences.

[7]  Christopher L. Asplund,et al.  Functional Specialization and Flexibility in Human Association Cortex. , 2016, Cerebral cortex.

[8]  K. Amunts,et al.  How to Characterize the Function of a Brain Region , 2018, Trends in Cognitive Sciences.

[9]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[10]  Jonathan D. Power,et al.  Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.

[11]  Joaquín Goñi,et al.  Centralized and distributed cognitive task processing in the human connectome , 2018, Network Neuroscience.

[12]  Kai Hwang,et al.  Frontoparietal Activity Interacts With Task-Evoked Changes in Functional Connectivity , 2019, Cerebral cortex.

[13]  Nancy Kanwisher,et al.  Language-Selective and Domain-General Regions Lie Side by Side within Broca’s Area , 2012, Current Biology.

[14]  Biyu J. He Spontaneous and Task-Evoked Brain Activity Negatively Interact , 2013, The Journal of Neuroscience.

[15]  D. Heeger,et al.  Slow Cortical Dynamics and the Accumulation of Information over Long Timescales , 2012, Neuron.

[16]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[17]  Thomas T. Liu,et al.  The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures , 2013, NeuroImage.

[18]  Evan M. Gordon,et al.  Evidence for Two Independent Factors that Modify Brain Networks to Meet Task Goals. , 2016, Cell reports.

[19]  J. Wallis Decoding Cognitive Processes from Neural Ensembles , 2018, Trends in Cognitive Sciences.

[20]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[21]  Adam Gazzaley,et al.  Measuring functional connectivity during distinct stages of a cognitive task , 2004, NeuroImage.

[22]  J. Mattingley,et al.  A hierarchy of timescales explains distinct effects of local inhibition of primary visual cortex and frontal eye fields , 2016, eLife.

[23]  Haochang Shou,et al.  On testing for spatial correspondence between maps of human brain structure and function , 2018, NeuroImage.

[24]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[25]  Xiao-Jing Wang Macroscopic gradients of synaptic excitation and inhibition in the neocortex , 2020, Nature Reviews Neuroscience.

[26]  John D. Murray,et al.  Generative modeling of brain maps with spatial autocorrelation , 2020, NeuroImage.

[27]  Thomas L. Griffiths,et al.  Supplementary Information for Natural Speech Reveals the Semantic Maps That Tile Human Cerebral Cortex , 2022 .

[28]  L de Arcangelis,et al.  Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks. , 2017, Chaos.

[29]  Michael W. Cole,et al.  Discovering the Computational Relevance of Brain Network Organization , 2019, Trends in Cognitive Sciences.

[30]  Joern Diedrichsen,et al.  Parcellation of motor sequence representations in the human neocortex , 2018, bioRxiv.

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

[32]  Jonathan Winawer,et al.  Compressive Temporal Summation in Human Visual Cortex , 2017, The Journal of Neuroscience.

[33]  Jonathan D. Power,et al.  Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.

[34]  Stephen M. Smith,et al.  Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data , 2017, NeuroImage.

[35]  Scott T. Grafton,et al.  Structural foundations of resting-state and task-based functional connectivity in the human brain , 2013, Proceedings of the National Academy of Sciences.

[36]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[37]  Matthew T. Kaufman,et al.  Neural population dynamics during reaching , 2012, Nature.

[38]  Earl K. Miller,et al.  Task-evoked activity quenches neural correlations and variability across cortical areas , 2020, PLoS Comput. Biol..

[39]  Daniele Marinazzo,et al.  Advancing functional connectivity research from association to causation , 2019, Nature Neuroscience.

[40]  Boris C. Bernhardt,et al.  Gradients of structure–function tethering across neocortex , 2019, Proceedings of the National Academy of Sciences.

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

[42]  Geoffrey E. Hinton,et al.  A general framework for parallel distributed processing , 1986 .

[43]  Gopal Santhanam,et al.  Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach. , 2006, Journal of neurophysiology.

[44]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

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

[46]  Michael W. Cole,et al.  Task activations produce spurious but systematic inflation of task functional connectivity estimates , 2018, NeuroImage.

[47]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[48]  Graham L. Baum,et al.  Development of structure–function coupling in human brain networks during youth , 2019, Proceedings of the National Academy of Sciences.

[49]  David J. Freedman,et al.  A hierarchy of intrinsic timescales across primate cortex , 2014, Nature Neuroscience.

[50]  Brian Maniscalco,et al.  Initial-state-dependent, robust, transient neural dynamics encode conscious visual perception , 2017, bioRxiv.

[51]  Alberto Llera,et al.  Disentangling common from specific processing across tasks using task potency , 2019, NeuroImage.

[52]  Christos Davatzikos,et al.  Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.

[53]  A. Pouget,et al.  Variance as a Signature of Neural Computations during Decision Making , 2011, Neuron.

[54]  Krzysztof J. Gorgolewski,et al.  The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance , 2015, Neuron.

[55]  Everton J. Agnes,et al.  Inhibitory Plasticity: Balance, Control, and Codependence. , 2017, Annual review of neuroscience.

[56]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[57]  Michael Cole,et al.  Cognitive task information is transferred between brain regions via resting-state network topology , 2017 .

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

[59]  N. Kanwisher,et al.  The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.

[60]  M K Habib,et al.  Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.

[61]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[62]  G. Deco,et al.  Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain , 2019, Science Advances.

[63]  D. Heeger,et al.  A Hierarchy of Temporal Receptive Windows in Human Cortex , 2008, The Journal of Neuroscience.

[64]  John D. Murray,et al.  Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics , 2019, Neuron.

[65]  Jonathan D. Power,et al.  Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data , 2018, Proceedings of the National Academy of Sciences.

[66]  Walter Schneider,et al.  Identifying the brain's most globally connected regions , 2010, NeuroImage.

[67]  Guillaume Hennequin,et al.  The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability , 2018, Neuron.

[68]  Mark Jenkinson,et al.  MSM: A new flexible framework for Multimodal Surface Matching , 2014, NeuroImage.

[69]  Michael W. Cole,et al.  Activity flow over resting-state networks shapes cognitive task activations , 2016, Nature Neuroscience.

[70]  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.

[71]  R. Poldrack Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding , 2011, Neuron.

[72]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[73]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.