title: p-Value combiners for graphical modelling of EEG data in the frequency domain.
abstract: In the graphical modelling of brain data, we are interested in estimating connectivity between various regions of interest, and evaluating statistical significance in order to derive a network model. This process involves aggregating results across frequency ranges and several patients, in order to obtain an overall result that can serve to construct a graph.
  
  
  
  In this paper, we propose a method based on p-value combiners, which have never been used in applications to EEG data analysis. This new method is split into two aspects: frequency-wide tests and group-wide tests. The first step can be effectively adjusted to control for false detection rate.
  
  
  
  This two-step protocol is applied to EEG data collected from distinct groups of mental health patients, in order to draw graphical models for each group and highlight structural connectivity differences. Using the method proposed, we show that it is possible to reliably achieve this while effectively controlling for false connections detection.
  
  
  
  Conventionally, the Holm's Stepdown procedure is used for this type of problem, as it is robust to type I errors. However, it is known to be conservative and prone to false negatives. Furthermore, unlike the proposed methods, it does not directly output a decision rule on whether to accept or reject a statement.
  
  
  
  The proposed methodology offers significant improvements over the stepdown procedure in terms of error rate and false negative rate across the network models, as well as in term of applicability.
authors.affiliation: Department of Mathematics, 180 Queen's Gate, Imperial College London, SW7 2BB London, UK. Electronic address: ds711@ic.ac.uk.
authors.forename: Deborah
authors.initials: D
authors.lastname: Schneider-Luftman
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000503:physiopathology; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged; D012559:Schizophrenia / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: Electroencephalogram (EEG) data; Multiple hypothesis testing; Multivariate time series; Partial coherence; p-Value combiners