Literature DB >> 25979667

Between-network connectivity occurs in brain regions lacking layer IV input.

Korey P Wylie1, Eugene Kronberg1, Keeran Maharajh1, Jason Smucny1, Marc-Andre Cornier2, Jason R Tregellas3.   

Abstract

To better understand the cortical circuitry underlying connectivity between large-scale neural networks, we develop a novel, data-driven approach to identify potential integration subregions. Between-network connectivity (BNC) associated with any anatomical region is the amount of connectivity between that point and all large-scale networks, as measured using simple and multiple correlations. It is straightforward to calculate and applicable to functional networks identified using independent components analysis. We calculated BNC for all fMRI voxels within the brain and compared the results to known regional cytoarchitectural patterns. Based on previous observations of the relationship between macroscopic connectivity and microscopic cytoarchitecture, we predicted that areas with high BNC will be located in paralimbic subregions with an undifferentiated laminar structure. Results suggest that the anterior insula and dorsal posterior cingulate cortices play prominent roles in information integration. Cytoarchitecturely, these areas show agranular or dysgranular cytologies with absent or disrupted cortical layer IV. Since layer IV is the primary recipient of feed-forward thalamocortical connections, and due to the exclusive nature of driving connections to this layer, we suggest that the absence of cortical layer IV might allow for information to be exchanged across networks, and is an organizational characteristic of brain-subregions serving as inter-network communication hubs.
Copyright © 2015. Published by Elsevier Inc.

Entities:  

Keywords:  Cytology; Independent components analysis; Paralimbic; Resting state; fMRI

Mesh:

Year:  2015        PMID: 25979667      PMCID: PMC4468026          DOI: 10.1016/j.neuroimage.2015.05.010

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


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