Literature DB >> 9373019

Cortical structure predicts the pattern of corticocortical connections.

H Barbas1, N Rempel-Clower.   

Abstract

Cortical areas are linked through pathways which originate and terminate in specific layers. The factors underlying which layers are involved in specific connections are not well understood. Here we tested whether cortical structure can predict the pattern as well as the relative distribution of projection neurons and axonal terminals in cortical layers, studied with retrograde and anterograde tracers. We used the prefrontal cortices in the rhesus monkey as a model system because their laminar organization varies systematically, ranging from areas that have only three identifiable layers, to those that have six layers. We rated each prefrontal area based on the number and definition of its cortical layers (level 1, lowest; level 5, highest). The structural model accurately predicted the laminar pattern of connections in approximately 80% of the cases. Thus, projection neurons from a higher-level cortex originated mostly in the upper layers and their axons terminated predominantly in the deep layers (4-6) of a lower-level cortex. Conversely, most projection neurons from a lower-level area originated in the deep layers and their axons terminated predominantly in the upper layers (1-3) of a higher-level area. In addition, the structural model accurately predicted that the proportion of projection neurons or axonal terminals in the upper to the deep layers would vary as a function of the number of levels between the connected cortices. The power of this structural model lies in its potential to predict patterns of connections in the human cortex, where invasive procedures are precluded.

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Year:  1997        PMID: 9373019     DOI: 10.1093/cercor/7.7.635

Source DB:  PubMed          Journal:  Cereb Cortex        ISSN: 1047-3211            Impact factor:   5.357


  129 in total

1.  Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor.

Authors:  C C Hilgetag; M A O'Neill; M P Young
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2000-01-29       Impact factor: 6.237

2.  Laminar distribution of neurons in extrastriate areas projecting to visual areas V1 and V4 correlates with the hierarchical rank and indicates the operation of a distance rule.

Authors:  P Barone; A Batardiere; K Knoblauch; H Kennedy
Journal:  J Neurosci       Date:  2000-05-01       Impact factor: 6.167

Review 3.  Frontal-lobe involvement in spatial memory: evidence from PET, fMRI, and lesion studies.

Authors:  R P Kessels; A Postma; E M Wijnalda; E H de Haan
Journal:  Neuropsychol Rev       Date:  2000-06       Impact factor: 7.444

4.  Computational constraints that may have favoured the lamination of sensory cortex.

Authors:  Alessandro Treves
Journal:  J Comput Neurosci       Date:  2003 May-Jun       Impact factor: 1.621

Review 5.  Neurophysiological and computational principles of cortical rhythms in cognition.

Authors:  Xiao-Jing Wang
Journal:  Physiol Rev       Date:  2010-07       Impact factor: 37.312

6.  Dynamics of coupled thalamocortical modules.

Authors:  Jonathan D Drover; Nicholas D Schiff; Jonathan D Victor
Journal:  J Comput Neurosci       Date:  2010-05-20       Impact factor: 1.621

Review 7.  The importance of being agranular: a comparative account of visual and motor cortex.

Authors:  Stewart Shipp
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2005-04-29       Impact factor: 6.237

8.  Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala.

Authors:  H T Ghashghaei; C C Hilgetag; H Barbas
Journal:  Neuroimage       Date:  2006-11-27       Impact factor: 6.556

9.  Graded classes of cortical connections: quantitative analyses of laminar projections to motion areas of cat extrastriate cortex.

Authors:  Simon Grant; Claus C Hilgetag
Journal:  Eur J Neurosci       Date:  2005-08       Impact factor: 3.386

10.  A predictive network model of cerebral cortical connectivity based on a distance rule.

Authors:  Mária Ercsey-Ravasz; Nikola T Markov; Camille Lamy; David C Van Essen; Kenneth Knoblauch; Zoltán Toroczkai; Henry Kennedy
Journal:  Neuron       Date:  2013-10-02       Impact factor: 17.173

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