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Application of connected iterative scan in biological neural network

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Abstract

The current study applied connected iterative scan (CIS), a graph-theoretical-based approach that was previously developed to detect clusters among social networks, in detecting the functional clusters among the biological neural network of human brain extracted from resting-state functional MRI data. Unlike traditional clustering or modularity methods, CIS allows for overlapping among the detected clusters. CIS was tested on a simulation dataset as well as a biological dataset. CIS was able to detect the overlaps designed in the simulation dataset; CIS was also able to detect the designed overlap in the simulation dataset, and it also detected the overlap between the two networks in the biological dataset, which provided new knowledge about the architecture of the biological neural network. In conclusion, CIS is shown to be useful for revealing the delicate structures among the functional clusters in the biological neural network.

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Acknowledgments

This material is based upon work partially supported by the U.S. National Science Foundation (NSF) under Grant Nos. IIS-0324947 and by the U.S. Department of Homeland Security (DHS) through the Center for Dynamic Data Analysis for Homeland Security administered through ONR grant number N00014-07-1-0150 to Rutgers University. The content of this paper does not necessarily reflect the position or policy of the U.S. Government, no official endorsement should be inferred or implied.

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Authors and Affiliations

  1. Cognitive Science Department, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Xiao-Dan Yan

  2. Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Stephen Kelley & Mark Goldberg

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  1. Xiao-Dan Yan
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  2. Stephen Kelley
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  3. Mark Goldberg
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Correspondence to Xiao-Dan Yan.

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Yan, XD., Kelley, S. & Goldberg, M. Application of connected iterative scan in biological neural network. Neural Comput & Applic 21, 2097–2103 (2012). https://doi.org/10.1007/s00521-011-0633-3

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  • DOI: https://doi.org/10.1007/s00521-011-0633-3

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