Flexible Coordinator and Switcher Hubs for Adaptive Task Control

Functional connectivity studies have identified at least two large-scale neural systems that constitute cognitive control networks – the frontoparietal network (FPN) and cingulo-opercular network (CON). Control networks are thought to support goal-directed cognition and behavior. It was previously shown that the FPN flexibly shifts its global connectivity pattern according to task goal, consistent with a “flexible hub” mechanism for cognitive control. Our aim was to build on this finding to develop a functional cartography (a multi-metric profile) of control networks in terms of dynamic network properties. We quantified network properties in (male and female) humans using a high-control-demand cognitive paradigm involving switching among 64 task sets. We hypothesized that cognitive control is enacted by the FPN and CON via distinct but complementary roles reflected in network dynamics. Consistent with a flexible “coordinator” mechanism, FPN connections were varied across tasks, while maintaining within-network connectivity to aid cross-region coordination. Consistent with a flexible “switcher” mechanism, CON regions switched to other networks in a task-dependent manner, driven primarily by reduced within-network connections to other CON regions. This pattern of results suggests FPN acts as a dynamic, global coordinator of goal-relevant information, while CON transiently disbands to lend processing resources to other goal-relevant networks. This cartography of network dynamics reveals a dissociation between two prominent cognitive control networks, suggesting complementary mechanisms underlying goal-directed cognition. Significance Statement Cognitive control supports a variety of behaviors requiring flexible cognition, such as rapidly switching between tasks. Furthermore, cognitive control is negatively impacted in a variety of mental illnesses. We used tools from network science to characterize the implementation of cognitive control by large-scale brain systems. This revealed that two systems – the frontoparietal (FPN) and cingulo-opercular (CON) networks – have distinct but complementary roles in controlling global network reconfigurations. The FPN exhibited properties of a flexible coordinator (orchestrating task changes), while CON acted as a flexible switcher (switching specific regions to other systems to lend processing resources). These findings reveal an underlying distinction in cognitive processes that may be applicable to clinical, educational, and machine learning work targeting cognitive flexibility.

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

[2]  Danielle S. Bassett,et al.  Cognitive Network Neuroscience , 2015, Journal of Cognitive Neuroscience.

[3]  M. D’Esposito,et al.  Functional Characterization of the Cingulo-Opercular Network in the Maintenance of Tonic Alertness. , 2015, Cerebral cortex.

[4]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[5]  Walter Schneider,et al.  Controlled and Automatic Human Information Processing: 1. Detection, Search, and Attention. , 1977 .

[6]  N. Kriegeskorte,et al.  Revealing representational content with pattern-information fMRI--an introductory guide. , 2009, Social cognitive and affective neuroscience.

[7]  Mason A. Porter,et al.  Robust Detection of Dynamic Community Structure in Networks , 2012, Chaos.

[8]  R. Adolphs,et al.  Damage to the prefrontal cortex increases utilitarian moral judgements , 2007, Nature.

[9]  Horacio G. Rotstein,et al.  Task-evoked activity quenches neural correlations and variability across cortical areas , 2019, bioRxiv.

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

[11]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[12]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[13]  Michael L. Anderson,et al.  Split-Sample Strategies for Avoiding False Discoveries , 2017 .

[14]  D. Shohamy,et al.  Ventromedial prefrontal-subcortical systems and the generation of affective meaning , 2012, Trends in Cognitive Sciences.

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

[16]  Justin L. Vincent,et al.  Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.

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

[18]  Christopher L. Asplund,et al.  Functional Specialization and Flexibility in Human Association Cortex. , 2015, Cerebral cortex.

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

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

[21]  Michael W. Cole,et al.  The task novelty paradox: Flexible control of inflexible neural pathways during rapid instructed task learning , 2017, Neuroscience & Biobehavioral Reviews.

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

[23]  N. Sigala,et al.  Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.

[24]  Daniel J Mitchell,et al.  Task Encoding across the Multiple Demand Cortex Is Consistent with a Frontoparietal and Cingulo-Opercular Dual Networks Distinction , 2016, The Journal of Neuroscience.

[25]  Andres Hoyos Idrobo,et al.  Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines , 2016, NeuroImage.

[26]  Gustavo Deco,et al.  Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling , 2015, PLoS Comput. Biol..

[27]  R. Desimone,et al.  Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.

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

[29]  Marvin M Chun,et al.  Category-selective background connectivity in ventral visual cortex. , 2012, Cerebral cortex.

[30]  Michael W. Cole,et al.  Functional connectivity change as shared signal dynamics , 2016, Journal of Neuroscience Methods.

[31]  Jonathan D. Power,et al.  Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.

[32]  Jari Saramäki,et al.  Reorganization of functionally connected brain subnetworks in high‐functioning autism , 2015, Human brain mapping.

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

[34]  Walter Schneider,et al.  The cognitive control network: Integrated cortical regions with dissociable functions , 2007, NeuroImage.

[35]  Michael W. Cole,et al.  From connectome to cognition: The search for mechanism in human functional brain networks , 2017, NeuroImage.

[36]  Richard F. Betzel,et al.  Modular Brain Networks. , 2016, Annual review of psychology.

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

[38]  M. Botvinick,et al.  Motivation and cognitive control: from behavior to neural mechanism. , 2015, Annual review of psychology.

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

[40]  Michael W. Cole,et al.  Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in undiagnosed individuals , 2017, bioRxiv.

[41]  Michael W. Cole,et al.  Global connectivity of the fronto-parietal cognitive control network is related to depression symptoms in the general population , 2018, Network Neuroscience.

[42]  Andrea Tagarelli,et al.  Revisiting Resolution and Inter-Layer Coupling Factors in Modularity for Multilayer Networks , 2017, ASONAM.

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

[44]  G. E. Smith The Human Brain , 1924, Nature.

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

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

[47]  D. Bassett,et al.  Dynamic reconfiguration of frontal brain networks during executive cognition in humans , 2015, Proceedings of the National Academy of Sciences.

[48]  Michael W. Cole,et al.  Mapping the human brain's cortical-subcortical functional network organization , 2018, NeuroImage.

[49]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[50]  Mark D'Esposito,et al.  Fronto-Parietal Interactions with Task-Evoked Functional Connectivity During Cognitive Control , 2017, bioRxiv.

[51]  Walter Schneider,et al.  The Brain’s Learning and Control Architecture , 2012 .

[52]  R. Desimone,et al.  Attentional control of visual perception: cortical and subcortical mechanisms. , 1990, Cold Spring Harbor symposia on quantitative biology.

[53]  Michael W. Cole,et al.  Prefrontal Dynamics Underlying Rapid Instructed Task Learning Reverse with Practice , 2010, The Journal of Neuroscience.

[54]  Tobias Egner,et al.  Measuring Adaptive Control in Conflict Tasks , 2019, Trends in Cognitive Sciences.

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

[56]  J. Seamans,et al.  The principal features and mechanisms of dopamine modulation in the prefrontal cortex , 2004, Progress in Neurobiology.

[57]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[58]  S. Petersen,et al.  A dual-networks architecture of top-down control , 2008, Trends in Cognitive Sciences.

[59]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

[60]  Michael L. Waskom,et al.  Frontoparietal Representations of Task Context Support the Flexible Control of Goal-Directed Cognition , 2014, The Journal of Neuroscience.

[61]  J. Fuster,et al.  Functional interactions between inferotemporal and prefrontal cortex in a cognitive task , 1985, Brain Research.

[62]  Jerome Feldman,et al.  The neural binding problem(s) , 2013, Cognitive Neurodynamics.

[63]  Michael W. Cole,et al.  Rapid instructed task learning: A new window into the human brain’s unique capacity for flexible cognitive control , 2013, Cognitive, affective & behavioral neuroscience.

[64]  Abraham Z. Snyder,et al.  A method for using blocked and event-related fMRI data to study “resting state” functional connectivity , 2007, NeuroImage.

[65]  Walter Schneider,et al.  Controlled & automatic processing: behavior, theory, and biological mechanisms , 2003, Cogn. Sci..

[66]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[67]  Roger Guimerà,et al.  Cartography of complex networks: modules and universal roles , 2005, Journal of statistical mechanics.

[68]  Mark D'Esposito,et al.  Alpha-Band Phase Synchrony Is Related to Activity in the Fronto-Parietal Adaptive Control Network , 2012, The Journal of Neuroscience.

[69]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[70]  A. Kleinschmidt,et al.  Intrinsic Connectivity Networks, Alpha Oscillations, and Tonic Alertness: A Simultaneous Electroencephalography/Functional Magnetic Resonance Imaging Study , 2010, The Journal of Neuroscience.

[71]  M. Botvinick Conflict monitoring and decision making: Reconciling two perspectives on anterior cingulate function , 2007, Cognitive, affective & behavioral neuroscience.

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

[73]  Mason A. Porter,et al.  Task-Based Core-Periphery Organization of Human Brain Dynamics , 2012, PLoS Comput. Biol..

[74]  J. Maunsell,et al.  Attention improves performance primarily by reducing interneuronal correlations , 2009, Nature Neuroscience.

[75]  Kristina M. Visscher,et al.  A Core System for the Implementation of Task Sets , 2006, Neuron.

[76]  Michael W. Cole,et al.  Cingulate cortex: Diverging data from humans and monkeys , 2009, Trends in Neurosciences.

[77]  Andrew Zalesky,et al.  Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning , 2017, The Journal of Neuroscience.

[78]  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.

[79]  R. Blair,et al.  An alternative method for significance testing of waveform difference potentials. , 1993, Psychophysiology.

[80]  Stefanie E. Kuchinsky,et al.  The Cingulo-Opercular Network Provides Word-Recognition Benefit , 2013, The Journal of Neuroscience.

[81]  Sharon L. Thompson-Schill,et al.  A Functional Cartography of Cognitive Systems , 2015, PLoS Comput. Biol..

[82]  Edward T. Bullmore,et al.  On the use of correlation as a measure of network connectivity , 2012, NeuroImage.

[83]  J. Mattingley,et al.  Dynamic cooperation and competition between brain systems during cognitive control , 2013, Trends in Cognitive Sciences.

[84]  N. Fox,et al.  NIH Toolbox for Assessment of Neurological and Behavioral Function , 2013, Neurology.

[85]  Boleslaw K. Szymanski,et al.  Community Detection via Maximization of Modularity and Its Variants , 2014, IEEE Transactions on Computational Social Systems.