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Daniel P. Bliss
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Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2018) 30 (3): 353–364.
Published: 01 March 2018
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The intrinsic white matter connections of the frontal cortex are highly complex, and the organization of these connections is not fully understood. Quantitative graph-theoretical methods, which are not solely reliant on human observation and interpretation, can be powerful tools for describing the organizing network principles of frontal cortex. Here, we examined the network structure of frontal cortical subregions by applying graph-theoretical community detection analyses to a graph of frontal cortex compiled from over 400+ macaque white-matter tracing studies. We find evidence that the lateral frontal cortex can be partitioned into distinct modules roughly organized along the dorsoventral and rostrocaudal axis.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2014) 26 (4): 722–745.
Published: 01 April 2014
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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.