Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matrix. The latter converts a measure of the connectivity between two time series, typically a correlation coefficient, into a binary choice on whether the two brain locations are functionally connected or not. As a result, the choice of a threshold τ over the correlation coefficient is key. In the present work, we propose a multiple testing approach to the choice of τ that uses the Bayes false discovery rate and a new estimator of the statistical power called average power function to balance the two types of statistical error. We show that the proposed average power function estimator behaves well both in case of independence and weak dependence of the tests and it is reliable under several simulated dependence conditions. Moreover, we propose a robust method for the choice of τ using the 5% and 95% percentiles of the average power function and False Discovery Rate bootstrap distributions, respectively, to improve stability. We applied our approach to functional magnetic resonance imaging and high density electroencephalogram data.

Quatto, P., Margaritella, N., Costantini, I., Baglio, F., Garegnani, M., Nemni, R., et al. (2019). Brain networks construction using Bayes FDR and average power function. STATISTICAL METHODS IN MEDICAL RESEARCH, 29(3), 866-878 [10.1177/0962280219844288].

Brain networks construction using Bayes FDR and average power function

Quatto, P;
2019

Abstract

Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matrix. The latter converts a measure of the connectivity between two time series, typically a correlation coefficient, into a binary choice on whether the two brain locations are functionally connected or not. As a result, the choice of a threshold τ over the correlation coefficient is key. In the present work, we propose a multiple testing approach to the choice of τ that uses the Bayes false discovery rate and a new estimator of the statistical power called average power function to balance the two types of statistical error. We show that the proposed average power function estimator behaves well both in case of independence and weak dependence of the tests and it is reliable under several simulated dependence conditions. Moreover, we propose a robust method for the choice of τ using the 5% and 95% percentiles of the average power function and False Discovery Rate bootstrap distributions, respectively, to improve stability. We applied our approach to functional magnetic resonance imaging and high density electroencephalogram data.
Articolo in rivista - Articolo scientifico
Scientifica
Average Power Function; Bayes FDR; Functional MRI; High Density EEG; Multiple hypothesis testing
English
Anno_pub:2020- Article first published online: May 14, 2019; Issue published: March 1, 2020
Quatto, P., Margaritella, N., Costantini, I., Baglio, F., Garegnani, M., Nemni, R., et al. (2019). Brain networks construction using Bayes FDR and average power function. STATISTICAL METHODS IN MEDICAL RESEARCH, 29(3), 866-878 [10.1177/0962280219844288].
Quatto, P; Margaritella, N; Costantini, I; Baglio, F; Garegnani, M; Nemni, R; Pugnetti, L
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/232377
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