Abstract
With an ever growing number of published scientific studies, there is a need for automated search methods, able to collect and extract as much information as possible from those articles. We propose a framework for the extraction and characterization of brain activity areas published in neuroscientific reports, as well as a suitable clustering strategy of said areas. We further show that it is possible to obtain three-dimensional summarizing brain maps, accounting for a particular topic within those studies. After, using the text information from the articles, we characterize such maps. As an illustrative experiment, we demonstrate the proposed mining approach in fMRI reports of default mode networks. The proposed method hints at the possibility of searching for both visual and textual keywords in neuro atlases.
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Banerjee, S., Pedersen, T.: The design, implementation, and use of the Ngram Statistic Package. In: Proc. of the 4th International Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, pp. 370–381 (2003)
Brett, M., Johnsrude, I.S., Owen, A.M.: The problem of functional localization in the human brain. Nature Reviews Neuroscience 3(3), 243–249 (2002)
Deco, G., Jirsa, V.K., McIntosh, A.R.: Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews Neuroscience 12(1), 43–56 (2011)
Gonçalves, N., Vranou, G., Vigário, R.: Towards automated image mining from reported medical images. In: Proc. of VipIMAGE 2013 - 4th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, pp. 255–261. CRC Press (2013)
Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press (2001)
Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. J. Doc. 28(1), 11–21 (1972)
Laird, A.R., Lancaster, J.L., Fox, P.T.: Lost in localization? the focus is meta-analysis. Neuroimage 48(1), 18–20 (2009)
Levy, D.J., Glimcher, P.W.: The root of all value: a neural common currency for choice. Current Opinion Neurobiology 22(6), 1027–1038 (2012)
Müller, H., Clough, P., Deselaers, T., Caputo, B.: Experimental evaluation in visual information retrieval. The Information Retrieval Series, vol. 32 (2010)
Porter, M.F.: An algorithm for suffix stripping. Program: Electronic Library and Information Systems 14(3), 130–137 (1980)
Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L.: A default mode of brain function. Proc. National Academy Science U.S.A. 98(2), 676–682 (2001)
Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc. (2009)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)
Snyder, A.Z., Raichle, M.E.: A brief history of the resting state: The Washington University perspective. NeuroImage 62(2), 902–910 (2012)
Turney, P.D., Pantel, P.: From frequency to meaning: Vector space models of semantics. J. Artificial Intelligent Research 37(1), 141–188 (2010)
Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C., Wager, T.D.: Large-scale automated synthesis of human functional neuroimaging data. Nature Methods 8(8), 665–670 (2011)
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Gonçalves, N., Oja, E., Vigário, R. (2014). Medical Document Mining Combining Image Exploration and Text Characterization. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds) Discovery Science. DS 2014. Lecture Notes in Computer Science(), vol 8777. Springer, Cham. https://doi.org/10.1007/978-3-319-11812-3_9
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DOI: https://doi.org/10.1007/978-3-319-11812-3_9
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