Literature DB >> 24116839

CoCoTools: open-source software for building connectomes using the CoCoMac anatomical database.

Robert S Blumenfeld1, Daniel P Bliss, Fernando Perez, Mark D'Esposito.   

Abstract

Neuroanatomical tracer studies in the nonhuman primate macaque monkey are a valuable resource for cognitive neuroscience research. These data ground theories of cognitive function in anatomy, and with the emergence of graph theoretical analyses in neuroscience, there is high demand for these data to be consolidated into large-scale connection matrices ("macroconnectomes"). Because manual review of the anatomical literature is time consuming and error prone, computational solutions are needed to accomplish this task. Here we describe the "CoCoTools" open-source Python library, which automates collection and integration of macaque connectivity data for visualization and graph theory analysis. CoCoTools both interfaces with the CoCoMac database, which houses a vast amount of annotated tracer results from 100 years (1905-2005) of neuroanatomical research, and implements coordinate-free registration algorithms, which allow studies that use different parcellations of the brain to be translated into a single graph. We show that using CoCoTools to translate all of the data stored in CoCoMac produces graphs with properties consistent with what is known about global brain organization. Moreover, in addition to describing CoCoTools' processing pipeline, we provide worked examples, tutorials, links to on-line documentation, and detailed appendices to aid scientists interested in using CoCoTools to gather and analyze CoCoMac data.

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Year:  2013        PMID: 24116839     DOI: 10.1162/jocn_a_00498

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  1 in total

1.  A macaque connectome for large-scale network simulations in TheVirtualBrain.

Authors:  Kelly Shen; Gleb Bezgin; Michael Schirner; Petra Ritter; Stefan Everling; Anthony R McIntosh
Journal:  Sci Data       Date:  2019-07-17       Impact factor: 6.444

  1 in total

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