Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures

  • The neuroanatomical connectivity of cortical circuits is believed to follow certain rules, the exact origins of which are still poorly understood. In particular, numerous nonrandom features, such as common neighbor clustering, overrepresentation of reciprocal connectivity, and overrepresentation of certain triadic graph motifs have been experimentally observed in cortical slice data. Some of these data, particularly regarding bidirectional connectivity are seemingly contradictory, and the reasons for this are unclear. Here we present a simple static geometric network model with distance-dependent connectivity on a realistic scale that naturally gives rise to certain elements of these observed behaviors, and may provide plausible explanations for some of the conflicting findings. Specifically, investigation of the model shows that experimentally measured nonrandom effects, especially bidirectional connectivity, may depend sensitively on experimental parameters such as slice thickness and sampling area, suggesting potential explanations for the seemingly conflicting experimental results.

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Author:Daniel Miner, Jochen TrieschORCiD
Pubmed Id:
Parent Title (English):Frontiers in neuroanatomy
Publisher:Frontiers Research Foundation
Place of publication:Lausanne
Document Type:Article
Date of Publication (online):2014/11/05
Date of first Publication:2014/12/03
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2014/12/03
Tag:common neighbor; cortical networks; cortical slices; graph theory; motifs; network topology; nonrandom connectivity
Issue:Article 125
Page Number:9
Copyright © 2014 Miner and Triesch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Institutes:Medizin / Medizin
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0