Organizing conceptual knowledge in humans with a gridlike code

Coding abstract concepts in the brain Grid cells are thought to provide the neuronal code that underlies spatial knowledge in the brain. Grid cells have mostly been studied in the context of path integration. However, recent theoretical studies have suggested that they may have a broader role in the organization of general knowledge. Constantinescu et al. investigated whether the neural representation of concepts follows a structure similar to the representation of space in the entorhinal cortex. Several brain regions, including the entorhinal cortex and the ventromedial prefrontal cortex, showed gridlike neural representation of conceptual space. Science, this issue p. 1464 Grid cells in the brain can also represent nonspatial knowledge. It has been hypothesized that the brain organizes concepts into a mental map, allowing conceptual relationships to be navigated in a manner similar to that of space. Grid cells use a hexagonally symmetric code to organize spatial representations and are the likely source of a precise hexagonal symmetry in the functional magnetic resonance imaging signal. Humans navigating conceptual two-dimensional knowledge showed the same hexagonal signal in a set of brain regions markedly similar to those activated during spatial navigation. This gridlike signal is consistent across sessions acquired within an hour and more than a week apart. Our findings suggest that global relational codes may be used to organize nonspatial conceptual representations and that these codes may have a hexagonal gridlike pattern when conceptual knowledge is laid out in two continuous dimensions.

[1]  D. Hassabis,et al.  Deconstructing episodic memory with construction , 2007, Trends in Cognitive Sciences.

[2]  J. O’Neill,et al.  The reorganization and reactivation of hippocampal maps predict spatial memory performance , 2010, Nature Neuroscience.

[3]  H. Eichenbaum,et al.  Can We Reconcile the Declarative Memory and Spatial Navigation Views on Hippocampal Function? , 2014, Neuron.

[4]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[5]  Neil Burgess,et al.  Grid-like Processing of Imagined Navigation , 2016, Current Biology.

[6]  Ron Meir,et al.  Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis , 2016, eLife.

[7]  May-Britt Moser,et al.  The entorhinal grid map is discretized , 2012, Nature.

[8]  Christian F. Doeller,et al.  Evidence for grid cells in a human memory network , 2010, Nature.

[9]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[10]  Lukas Kunz,et al.  Reduced grid-cell–like representations in adults at genetic risk for Alzheimer’s disease , 2015, Science.

[11]  D. Hassabis,et al.  Tracking the Emergence of Conceptual Knowledge during Human Decision Making , 2009, Neuron.

[12]  Timothy E. J. Behrens,et al.  Online evaluation of novel choices by simultaneous representation of multiple memories , 2013, Nature Neuroscience.

[13]  G. Buzsáki,et al.  Memory, navigation and theta rhythm in the hippocampal-entorhinal system , 2013, Nature Neuroscience.

[14]  C. Koch,et al.  Invariant visual representation by single neurons in the human brain , 2005, Nature.

[15]  Edvard I. Moser,et al.  Shearing-induced asymmetry in entorhinal grid cells , 2015, Nature.

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

[17]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[18]  Daniel L Schacter,et al.  Ventromedial prefrontal cortex supports affective future simulation by integrating distributed knowledge , 2014, Proceedings of the National Academy of Sciences.

[19]  P. Dudchenko.  The hippocampus as a cognitive map , 2010 .

[20]  Samuel Gershman,et al.  Design Principles of the Hippocampal Cognitive Map , 2014, NIPS.

[21]  Nikolaus Weiskopf,et al.  Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: A whole-brain analysis at 3 T and 1.5 T , 2006, NeuroImage.

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

[23]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[24]  R. Poldrack,et al.  Quantifying the internal structure of categories using a neural typicality measure. , 2014, Cerebral cortex.

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

[26]  S. Carey,et al.  Understanding other minds: linking developmental psychology and functional neuroimaging. , 2004, Annual review of psychology.

[27]  I. Fried,et al.  Direct recordings of grid-like neuronal activity in human spatial navigation , 2013, Nature Neuroscience.

[28]  T. Hafting,et al.  Microstructure of a spatial map in the entorhinal cortex , 2005, Nature.

[29]  E. Tolman.  Cognitive maps in rats and men. , 1948, Psychological review.

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

[31]  K. Jeffery,et al.  Experience-dependent rescaling of entorhinal grids , 2007, Nature Neuroscience.

[32]  A. Rangel,et al.  Informatic parcellation of the network involved in the computation of subjective value. , 2014, Social cognitive and affective neuroscience.

[33]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[34]  Mark P. Brandon,et al.  During Running in Place, Grid Cells Integrate Elapsed Time and Distance Run , 2015, Neuron.