Modes of operation: A topographic neural gradient supporting stimulus dependent and independent cognition

&NA; Human cognition is flexible ‐ drawing on both sensory input, and representations from memory, to successfully navigate complex environments. Contemporary accounts suggest this flexibility is possible because neural function is organized into a hierarchy. Neural regions are organized along a macroscale gradient, anchored at one end by unimodal systems involved with perception and action, and at the other by transmodal systems, including the default mode network, supporting cognition less directly tied to immediate stimulus input. The current study tested whether this cortical hierarchy captures modes of behaviour that depend on immediate input, as well as those that depend on representations from memory. Participants made decisions regarding the location or identity of shapes using information in the environment (0‐back) or from a prior trial (1‐back). Using task based imaging we established that, regardless of the nature of the decision, medial and lateral visual cortex were recruited when decisions rely on immediate input, while transmodal regions were recruited when judgments depend on information from the prior trial. Using principal components analysis, we demonstrated that shifting decision‐making from perception to memory altered the focus of neural activity from unimodal to transmodal regions (and vice versa). Notably, the more pronounced these shifts in neural activity from unimodal to transmodal regions when decisions relied on memory, the more efficiently individuals performed this task. These data illustrate how the macroscale organization of neural function into a hierarchy allows cognition to rely on input, or information from memory, in a flexible and efficient manner.

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

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

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

[4]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[5]  Danyang Gui,et al.  Resting spontaneous activity in the default mode network predicts performance decline during prolonged attention workload , 2015, NeuroImage.

[6]  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.

[7]  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.

[8]  M P Young,et al.  Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[9]  Daniel L. Schacter,et al.  Autobiographical Planning and the Brain: Activation and Its Modulation by Qualitative Features , 2015, Journal of Cognitive Neuroscience.

[10]  David K. Menon,et al.  Default mode contributions to automated information processing , 2017, Proceedings of the National Academy of Sciences.

[11]  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.

[12]  Jonathan Smallwood,et al.  Converging evidence for the role of transmodal cortex in cognition , 2017, Proceedings of the National Academy of Sciences.

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

[14]  Kathryn M. McMillan,et al.  N‐back working memory paradigm: A meta‐analysis of normative functional neuroimaging studies , 2005, Human brain mapping.

[15]  O. Sporns,et al.  High-cost, high-capacity backbone for global brain communication , 2012, Proceedings of the National Academy of Sciences.

[16]  R. Nathan Spreng,et al.  The wandering brain: Meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes , 2015, NeuroImage.

[17]  Biyu J. He Scale-free brain activity: past, present, and future , 2014, Trends in Cognitive Sciences.

[18]  Kenneth Hugdahl,et al.  Prediction of human errors by maladaptive changes in event-related brain networks , 2008, Proceedings of the National Academy of Sciences.

[19]  T. Chomiak,et al.  Mechanisms of Hierarchical Cortical Maturation , 2017, Front. Cell. Neurosci..

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

[21]  R. Nathan Spreng,et al.  The Common Neural Basis of Autobiographical Memory, Prospection, Navigation, Theory of Mind, and the Default Mode: A Quantitative Meta-analysis , 2009, Journal of Cognitive Neuroscience.

[22]  Robert Leech,et al.  Exploring spatiotemporal network transitions in task functional MRI , 2014, Human brain mapping.

[23]  Hans Knutsson,et al.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[24]  J. Smallwood,et al.  The science of mind wandering: empirically navigating the stream of consciousness. , 2015, Annual review of psychology.

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

[26]  Hao-Ting Wang,et al.  Distant from input: Evidence of regions within the default mode network supporting perceptually-decoupled and conceptually-guided cognition , 2018, NeuroImage.

[27]  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.

[28]  Daniel J Mitchell,et al.  Neural Coding for Instruction‐Based Task Sets in Human Frontoparietal and Visual Cortex , 2016, Cerebral cortex.

[29]  Angela J. Yu,et al.  Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[30]  Peter J Hellyer,et al.  The Control of Global Brain Dynamics: Opposing Actions of Frontoparietal Control and Default Mode Networks on Attention , 2014, The Journal of Neuroscience.

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

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

[33]  R. N. Spreng,et al.  The Future of Memory: Remembering, Imagining, and the Brain , 2012, Neuron.

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

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

[36]  Daniel S. Margulies,et al.  The default modes of reading: modulation of posterior cingulate and medial prefrontal cortex connectivity associated with comprehension and task focus while reading , 2013, Front. Hum. Neurosci..

[37]  Henry Kennedy,et al.  Cortical High-Density Counterstream Architectures , 2013, Science.

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

[39]  Yoed N. Kenett,et al.  Robust prediction of individual creative ability from brain functional connectivity , 2018, Proceedings of the National Academy of Sciences.

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

[41]  R. N. Spreng,et al.  The default network and self‐generated thought: component processes, dynamic control, and clinical relevance , 2014, Annals of the New York Academy of Sciences.

[42]  H T Siegelmann,et al.  The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions , 2015, Scientific Reports.