pFDR and pFNR estimation for brain networks construction

Sara Sala, Piero Quatto, Paola Valsasina, Federica Agosta, Massimo Filippi

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)158-169
Number of pages12
JournalStatistics in Medicine
Issue number1
Publication statusPublished - Jan 15 2014


  • Brain functional networks
  • False discovery rate
  • False nondiscovery rate
  • Monte Carlo simulations
  • Resting state functional magnetic resonance imaging

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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