pFDR and pFNR estimation for brain networks construction

Authors

  • Sara Sala,

    Corresponding author
    1. Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy
    2. Neuroimaging Research Unit and Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
    • Correspondence to: Sara Sala, Department of Economics, Management and Statistics, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126, Milan, Italy.

      E-mail: sara.sala@unimib.it

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  • Piero Quatto,

    1. Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy
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  • Paola Valsasina,

    1. Neuroimaging Research Unit and Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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  • Federica Agosta,

    1. Neuroimaging Research Unit and Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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  • Massimo Filippi

    1. Neuroimaging Research Unit and Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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Abstract

Recent developments in the study of brain functional connectivity are widely based on graph theory. In the current analysis of brain networks, there is no unique way to derive the adjacency matrix, which is a useful representation for a graph. Its entries, containing information about the existence of links, are identified by thresholding the correlation between the time series that characterized the dynamic behavior of the nodes. In this work, we put forward a strategy to choose a suitable threshold on the correlation matrix considering the problem of multiple comparisons in order to control the error rates. In this context we propose to control the positive false discovery rate (pFDR) and a similar measure involving false negatives, called the positive false nondiscovery rate (pFNR). In particular, we provide point and interval estimators for pFNR and a method for balancing the two types of error, demonstrating it by using functional magnetic resonance imaging data and Monte Carlo simulations. Copyright © 2013 John Wiley & Sons, Ltd.

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