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.
| Original language | English |
|---|---|
| Journal | Statistical Methods in Medical Research |
| DOIs | |
| Publication status | Published - Jan 1 2019 |
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Keywords
- Average Power Function
- Bayes FDR
- Functional MRI
- High Density EEG
- Multiple hypothesis testing
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
- Health Information Management
Cite this
Brain networks construction using Bayes FDR and average power function. / Quatto, Piero; Margaritella, Nicolò; Costantini, Isa; Baglio, Francesca; Garegnani, Massimo; Nemni, Raffaello; Pugnetti, Luigi.
In: Statistical Methods in Medical Research, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Brain networks construction using Bayes FDR and average power function
AU - Quatto, Piero
AU - Margaritella, Nicolò
AU - Costantini, Isa
AU - Baglio, Francesca
AU - Garegnani, Massimo
AU - Nemni, Raffaello
AU - Pugnetti, Luigi
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Average Power Function
KW - Bayes FDR
KW - Functional MRI
KW - High Density EEG
KW - Multiple hypothesis testing
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UR - http://www.scopus.com/inward/citedby.url?scp=85067055068&partnerID=8YFLogxK
U2 - 10.1177/0962280219844288
DO - 10.1177/0962280219844288
M3 - Article
C2 - 31088219
AN - SCOPUS:85067055068
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
ER -