p-Value combiners for graphical modelling of EEG data in the frequency domain

•We adapt p-value combiners to work for frequency domain data.•A two-step procedure for graphical modelling on EEG data is proposed.•We control for false detections across networks with various level adjustments.•Methods are proposed to combine results across subjects and create group results.•Coloured graphs are made for each group, showing the prevalence of connections.

BackgroundIn 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.New methodIn 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.ResultsThis 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.Comparison with existing method(s)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.ConclusionsThe 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.