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An empirical Bayes normalization method for connectivity metrics in resting state fMRI.

Chen S, Kang J, Wang G - Front Neurosci (2015)

Bottom Line: Moreover, the normalization function maps the original connectivity metrics to values between zero and one, which is well-suited for the graph theory based network analysis and avoids the information loss due to the (negative value) hard thresholding step.We apply the normalization method to a simulation study and the simulation results show that our normalization method effectively improves the robustness and reliability of the quantification of brain functional connectivity and provides more powerful group difference (biomarkers) detection.We illustrate our method on an analysis of a rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, University of Maryland College Park, MD, USA.

ABSTRACT
Functional connectivity analysis using resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful technique for investigating functional brain networks. The functional connectivity is often quantified by statistical metrics (e.g., Pearson correlation coefficient), which may be affected by many image acquisition and preprocessing steps such as the head motion correction and the global signal regression. The appropriate quantification of the connectivity metrics is essential for meaningful and reproducible scientific findings. We propose a novel empirical Bayes method to normalize the functional brain connectivity metrics on a posterior probability scale. Moreover, the normalization function maps the original connectivity metrics to values between zero and one, which is well-suited for the graph theory based network analysis and avoids the information loss due to the (negative value) hard thresholding step. We apply the normalization method to a simulation study and the simulation results show that our normalization method effectively improves the robustness and reliability of the quantification of brain functional connectivity and provides more powerful group difference (biomarkers) detection. We illustrate our method on an analysis of a rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

No MeSH data available.


Related in: MedlinePlus

(A) Mixture model estimation procedure using “locfdr” in R; (B) Normalization function vs. Original correlations: the blue line is the normalization function that maps the raw connectivity metrics to normalized metrics; the red line is used a reference representing no normalization is applied.
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Figure 3: (A) Mixture model estimation procedure using “locfdr” in R; (B) Normalization function vs. Original correlations: the blue line is the normalization function that maps the raw connectivity metrics to normalized metrics; the red line is used a reference representing no normalization is applied.

Mentions: We apply our normalization method to the simulated correlations with the main goal of differentially expressed connectivity discovery. The R (http://CRAN.R-project.org/) package “locfdr” is used to estimate the mixture model; and the normalization function gs(z) is calculated for each individual (see Example in Supplementary Material). Figure 3 shows that the mixture model is well estimated as well as the shape of the normalization function. Comparing to the original correlation or the variance stablizing transformation methods (e.g., Fisher's Z, probit, or logit transformed correlations), the the posterior probability based normalization function incorporates the “false positive” belief with observed connectivity expressions by empirical Bayes framework. The normalized correlations ares not related to the original correlations linearly, but monotonely increasing. g(z) increases steeply between around 0.4 and 0.6 because the posterior belief of “true positive” rises drastically. If there are both “true positive” correlation and anticorrelation components, then three components will be detected and estimated and two normalization functions are provided separately for positive and negative correlations (see details in Section 5).


An empirical Bayes normalization method for connectivity metrics in resting state fMRI.

Chen S, Kang J, Wang G - Front Neurosci (2015)

(A) Mixture model estimation procedure using “locfdr” in R; (B) Normalization function vs. Original correlations: the blue line is the normalization function that maps the raw connectivity metrics to normalized metrics; the red line is used a reference representing no normalization is applied.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4584951&req=5

Figure 3: (A) Mixture model estimation procedure using “locfdr” in R; (B) Normalization function vs. Original correlations: the blue line is the normalization function that maps the raw connectivity metrics to normalized metrics; the red line is used a reference representing no normalization is applied.
Mentions: We apply our normalization method to the simulated correlations with the main goal of differentially expressed connectivity discovery. The R (http://CRAN.R-project.org/) package “locfdr” is used to estimate the mixture model; and the normalization function gs(z) is calculated for each individual (see Example in Supplementary Material). Figure 3 shows that the mixture model is well estimated as well as the shape of the normalization function. Comparing to the original correlation or the variance stablizing transformation methods (e.g., Fisher's Z, probit, or logit transformed correlations), the the posterior probability based normalization function incorporates the “false positive” belief with observed connectivity expressions by empirical Bayes framework. The normalized correlations ares not related to the original correlations linearly, but monotonely increasing. g(z) increases steeply between around 0.4 and 0.6 because the posterior belief of “true positive” rises drastically. If there are both “true positive” correlation and anticorrelation components, then three components will be detected and estimated and two normalization functions are provided separately for positive and negative correlations (see details in Section 5).

Bottom Line: Moreover, the normalization function maps the original connectivity metrics to values between zero and one, which is well-suited for the graph theory based network analysis and avoids the information loss due to the (negative value) hard thresholding step.We apply the normalization method to a simulation study and the simulation results show that our normalization method effectively improves the robustness and reliability of the quantification of brain functional connectivity and provides more powerful group difference (biomarkers) detection.We illustrate our method on an analysis of a rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, University of Maryland College Park, MD, USA.

ABSTRACT
Functional connectivity analysis using resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful technique for investigating functional brain networks. The functional connectivity is often quantified by statistical metrics (e.g., Pearson correlation coefficient), which may be affected by many image acquisition and preprocessing steps such as the head motion correction and the global signal regression. The appropriate quantification of the connectivity metrics is essential for meaningful and reproducible scientific findings. We propose a novel empirical Bayes method to normalize the functional brain connectivity metrics on a posterior probability scale. Moreover, the normalization function maps the original connectivity metrics to values between zero and one, which is well-suited for the graph theory based network analysis and avoids the information loss due to the (negative value) hard thresholding step. We apply the normalization method to a simulation study and the simulation results show that our normalization method effectively improves the robustness and reliability of the quantification of brain functional connectivity and provides more powerful group difference (biomarkers) detection. We illustrate our method on an analysis of a rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

No MeSH data available.


Related in: MedlinePlus